THE CONSEQUENCES OF AMBIVALENT POLITICAL ATTITUDES
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of
Philosophy in the Graduate School of The Ohio State University
By
Gregory W. Gwiasda, B.A., M.A.
****
The Ohio State University
2005
Dissertation Committee:
Professor Thomas E. Nelson, Adviser
Professor Kathleen M. McGraw
Approved by
Professor Herbert F. Weisberg
Adviser
Graduate Program in Political Science
ABSTRACT
Public opinion scholars agree that individuals can hold ambivalent political
attitudes; that is, they can simultaneously see reasons to support and oppose an issue
or candidate. However, two questions on the consequences of ambivalent attitudes
have been little studied. Does ambivalence about candidates induce people to seek
more information; and second, do ambivalent attitudes make individuals more likely
to abstain in elections. To answer these questions, I draw upon existing National
Election Studies survey data, original experimental analysis and a panel survey of
undergraduate students conducted in the spring of 2004. My findings indicate that
ambivalent individuals do not seek out additional information about candidates, but
rather process and store the information they encounter differently than do those who
are not ambivalent. I also find that ambivalence increases the likelihood of abstention
in two-candidate races, but increases the likelihood of voting for independent
candidates when that option is available. The latter finding that ambivalent
individuals often opt to abstain in elections is particularly noteworthy, as it raises
questions about the extent to which our political system is truly representative of the
public will.
ii
TO SARA
iii
ACKNOWLEDGMENTS
There are a number of people that I would like to thank for providing
invaluable support, directly or indirectly, for me as I have worked on completing this
project.
First, I am thankful for the support I received while getting my M.A. at the
University of Wisconsin-Milwaukee. I am especially appreciative of the time and
energy that Ronald Weber and Tom Holbrook took in preparing me for my career
beyond UWM. I cannot imagine a better way to start a graduate career than the years
I spent in Milwaukee.
I am likewise grateful for the help, guidance, and knowledge I have received
from all of my professors at Ohio State. As a department, it has provided an excellent
environment in which to develop as a scholar. Of course, I especially benefited from
the help I received from my dissertation committee. As a group, I could not have
asked for a better set of people to guide me through the transition from student to
scholar. As I embark upon my own career, it is my sincere hope that I can embody
the strengths of each one of these people as I make my own contributions to the field
of political science – as both scholar and teacher.
Kathleen McGraw has provided numerous helpful insights and suggestions
throughout the development of this project. She has been extremely generous with
iv
her time and the quality of my work was improved by each of our encounters. Herb
Weisberg has done a wonderful job in guiding me through the maze that is deemed
the “professionalization” process of the graduate student. No doubt, much to his
chagrin, his excellent skills in this regard has caused him to deal with more questions
from me on more topics than one man should endure. This said, he never once
hesitated to offer his help to me and for that I will always be thankful. He is truly one
of the finest professors and nicest people I have had the good fortune to cross paths
with.
Lastly, I thank Tom Nelson for all of his help throughout the past six years.
While he may not know this, my encounters with Tom when I was visiting Ohio State
when deciding which school to attend nearly single handedly determined that Ohio
State was the right school for me. As I got to know Tom that weekend, I knew that
he was the type of person that I wanted to work with as I developed professionally.
He not only took an interest in my professional goals, but about my general wellbeing. Over the course of my graduate career I have come to think of Tom as both a
mentor and a friend – two roles that he plays equally well. Truly, one of the best
decisions I have made was coming to Ohio State and choosing Tom as my advisor.
I also greatly appreciative the numerous friendships that I have made while at
Ohio State. While I should probably thank them for the help and insights they have
provided me on this project, truth be told, I am more grateful for their dexterity in
v
getting me to avoid this project – even if those skills sometimes resulted in a lighter
wallet after an evening of poker and/or a lighter bottle of aspirin some following
morning. For this, and other, help, I must point a finger at Eileen Braman, Bridget
Coggins, Charles Ellis, Andy Farrell, Paul Fritz, Andrew Holbrook, Jeff Martinson,
Mary Outwater, Charlie Smith, Brent Strathman, Justin Taylor, Margaret Williams,
and Sean Williams.
I would be remiss if I did not thank Steve Edelson for what is now nearly two
decades of friendship and, in all sincerity, for his complete lack of interest in the
consequences of ambivalent attitudes. I am also thankful for Jeremy Scott’s
friendship – another person who diligently reminds me of a world outside political
science.
I would also like to thank my parents for their support throughout my life (as
this has also resulted in a lighter wallet on their part). I am especially thankful for the
help that my father has given me over the years. He has read a number of things that
I have written and his comments and suggestions have been extremely helpful. The
quality of both my writing and thinking has been greatly enhanced as a result of his
insights. While my appreciation may not always be clear, I have always been grateful
to have him as a resource (not just financially – though that didn’t hurt).
Finally, and most importantly, I thank Sara Dunlap Gwiasda for everything.
Despite what I think should be her better judgment, she has never been ambivalent in
vi
her support for me in all aspects of my life. For this, and much more, I will always be
thankful that she has chosen to include me as a part of her life.
vii
VITA
March 19, 1975 . . . . . . . . . . . . . . . . . . . .
Born – Ames, IA
1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.A., Government, Lawrence University
1999. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
M.A., Political Science, University of
Wisconsin – Milwaukee
2002 – 2003. . . . . . . . . . . . . . . . . . . . . . .
Graduate Teaching Associate, The Ohio
State University
2003 – 2004. . . . . . . . . . . . . . . . . . . . . . .
Presidential Fellow, The Ohio State
University
2003 – Present. . . . . . . . . . . . . . . . . . . . . . Graduate Teaching Associate, The Ohio
State University
PUBLICATIONS
1.
Gwiasda, Gregory W. 2001. “Network News Coverage of Campaign
Advertisements: Media’s Ability to Reinforce Campaign Messages.” American
Politics Research 29:461-482.
FIELDS OF STUDY
Major Field: Political Science
viii
TABLE OF CONTENTS
Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dedication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vita. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
List of Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Page
ii
iii
iv
viii
xi
xvi
Chapters:
1.
Introduction – Ambivalent Attitudes. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Attitudes v. Behaviors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ambivalent Attitudes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
What We Do and Do Not Know About Ambivalent Attitudes. . . . . . . . .
Information Seeking Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstention. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
11
16
28
32
40
49
2.
The Causal Implications of Ambivalent Attitudes: Experimental
Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Experimental Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ambivalence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Information-Seeking Behavior: Measurement and Expectations. . . . . . .
Measurement and Expectations: Memory-Based v. On-line Processing.
Results: Searching Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results: On-line v. Memory Based Processing. . . . . . . . . . . . . . . . . . . . .
Abstention: Measurement and Expectations. . . . . . . . . . . . . . . . . . . . . . .
Results: Abstention. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
55
59
65
69
71
82
86
89
91
3.
Campaign Behavior: An Examination of the 1980-2000 National
Election. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Information-Seeking Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ambivalence and Control Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Findings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstention. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
116
119
120
122
134
137
ix
1980. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1984. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1988. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1992. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1996. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alternative Explanations for Abstention. . . . . . . . . . . . . . . . . . . . . . . . . .
Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
137
141
141
142
143
144
144
146
4.
The 2004 Presidential Campaign – Panel Data. . . . . . . . . . . . . . . . . . . . .
Panel Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ambivalence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Information-Seeking Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Panel Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Information-Seeking Behavior: Statistical Model. . . . . . . . . . . . . . . . . .
Information-Seeking Behavior: Results. . . . . . . . . . . . . . . . . . . . . . . . . .
Caveats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results: Abstention. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
181
184
188
192
195
198
201
207
209
215
5.
Conclusion: Ambivalence – The Good and Bad . . . . . . . . . . . . . . . . . . .
Ambivalence and Abstention. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ambivalence and Information-Seeking Behavior. . . . . . . . . . . . . . . . . . .
What Have We Learned? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
239
241
244
248
Appendix A – Distribution of Search Variables in Experimental
Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
Appendix B – Candidate Information and Policy Statement Appendix
for Experimental Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Appendix C – Questions Used in Experimental Analysis. . . . . . . . . . . . 262
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
x
LIST OF TABLES
Table
Page
2.1
Predicting Powell Ambivalence: OLS Regression. . . . . . . . . . . . . . . . 99
2.2
Predicting Sullivan Ambivalence: OLS Regression. . . . . . . . . . . . . .
100
2.3
Predicting Decision Ambivalence: OLS Regression. . . . . . . . . . . . . .
101
2.4
Effect of Ambivalence on the Number of Any Candidate Statements
Searches: Negative Binomial Results. . . . . . . . . . . . . . . . . . . . . . . . .
102
2.5
Effect of Ambivalence on the Number of Candidate Specific
Statement Searches: Negative Binomial. . . . . . . . . . . . . . . . . . . . . . .
103
2.6
Effect: of Ambivalence on the Number of New Issue Statement
Searches: Ordered Probit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
104
2.7
Effect of Ambivalence on the Number of Candidate Agreement
Searches: Negative Binomial. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
105
2.8
Effect of Ambivalence on the Number of Candidate Disagreement
Searches: Negative Binomial. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
106
2.9
Effect of Ambivalence on the Total Number of Issues Correctly
Recalled: Ordered Probit Regression. . . . . . . . . . . . . . . . . . . . . . . . . .
107
2.10
Predicting Reaction Times to the Candidate Feeling Thermometer
Scores: OLS Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
2.11
Predicting Subjects Scores on the Motivation to Vote Index: OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
109
3.1
Impact of Ambivalence on Pre-Election Media Use Indices: OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
154
3.2
Impact of Ambivalence on Personal Political Discussions: OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
155
xi
3.3
Impact of Ambivalence on Changes in Accurately Placing the
Candidates on an Ideological Scale from the Pre-Election to PostElection Surveys in 1980: OLS Regression. . . . . . . . . . . . . . . . . . . . .
156
3.4
Impact of Ambivalence on Changes in Uncertainty when Placing the
Candidates on an Ideological Scale from the Pre-Election to PostElection Surveys in 1980: OLS Regression. . . . . . . . . . . . . . . . . . . . . 157
3.5
Impact of Ambivalence on Changes in Accurately Placing the
Candidates on an Ideology and Aid to Blacks Issue Position Scales
from the Pre-Election to Post-Election Surveys in 1996: OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
Impact of Ambivalence on Changes in Uncertainty in Placing the
Candidates on the Ideology and Aid to Blacks Issue Position Scales
from the Pre-Election to Post-Election Surveys in 1996: OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
Impact of Ambivalence on Changes in Accurately Placing Reagan
on Ideology and Issue Position Scales from the Pre-Election to PostElection Surveys in 1984: OLS Regression. . . . . . . . . . . . . . . . . . . . .
160
3.6
3.7
3.8
Impact of Ambivalence on Changes in Uncertainty When Placing
Reagan on Ideology and Issue Position Scales from the Pre-Election
to Post-Election Surveys in 1984: OLS Regression. . . . . . . . . . . . . . . 161
3.9
Impact of Ambivalence on Changes in Accurately Placing Mondale
on Ideology and Issue Position Scales from the Pre-Election to PostElection Surveys in 1984: OLS Regression. . . . . . . . . . . . . . . . . . . . .
162
Impact of Ambivalence on Changes in Uncertainty When Placing
Mondale on Ideology and Issue Position Scales from the PreElection to Post-Election Surveys in 1984: OLS Regression. . . . . . .
163
3.11
Impact of Ambivalence on Changes in Campaign Interest from the
Pre-Election to Post-Election Surveys in 1984: OLS Regression. . . .
164
3.12
Multinomial Logit Results for Candidate and Abstention Choices:
1980 Election. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
165
3.10
xii
3.13
First Differences – Differences in Predicted Outcomes for Each
Variable in 1980: Probability of Maximum Value – Probability of
Minimum Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
166
3.14
Multinomial Logit Results for Candidate and Abstention Choices:
1984 Election . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
3.15
First Differences – Differences in Predicted Outcomes for Each
Variable in 1984: Probability of Maximum Value – Probability of
Minimum Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
168
3.16
Multinomial Logit Results for Candidate and Abstention Choices:
1988 Election. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
169
3.17
First Differences – Differences in Predicted Outcomes for Each
Variable in 1988: Probability of Maximum Value – Probability of
Minimum Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
170
3.18
Multinomial Logit Results for Candidate and Abstention Choices:
1992 Election . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
3.19
First Differences – Differences in Predicted Outcomes for Each
Variable in 1992: Probability of Maximum Value – Probability of
Minimum Value, (All Other Values Placed at their Mean) . . . . . . . .
172
3.20
Multinomial Logit Results for Candidate and Abstention Choices:
1996 Election . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
3.21
First Differences – Differences in Predicted Outcomes for Each
Variable in 1996: Probability of Maximum Value – Probability of
Minimum Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
174
3.22
Multinomial Logit Results for Candidate and Abstention Choices:
2000 Election. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
175
3.23
First Differences – Differences in Predicted Outcomes for Each
Variable in 2000: Probability of Maximum Value – Probability of
Minimum Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
176
xiii
3.24
Predicting Self-Reported Turnout in Previous Presidential Election
Logit Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
177
3.25
T-Test for Difference of Means in Decision Ambivalence Scores:
Comparing Values from Two-Candidate Elections to Three
Candidate Elections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
178
4.1
Correlation Among Ambivalence Measures. . . . . . . . . . . . . . . . . . . .
221
4.2
Factor Analysis Results of Positive and Negative Measures:
Eigenvalues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
222
4.3
Difference of Means and Proportions Tests: Assessing Differences
in Subjects Who Completed Both Waves to Those Who Only
Completed the First Wave Survey. . . . . . . . . . . . . . . . . . . . . . . . . . . .
223
4.4
Wave 1 and Wave 2 Ambivalence and Information-Seeking
Correlations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
224
4.5
Effects of Objective Ambivalence on the General Attention to the
2004 Presidential Campaign Index, OLS Regression. . . . . . . . . . . . .
225
4.6
Effects of Subjective Ambivalence on the General Attention to the
2004 Presidential Campaign Index, OLS Regression. . . . . . . . . . . . .
226
4.7
Effects of Objective Ambivalence on the Media Use Index in the
2004 Presidential Campaign, OLS Regression. . . . . . . . . . . . . . . . . .
227
4.8
Effects of Subjective Ambivalence on the Media Use Index in the
2004 Presidential Campaign, OLS Regression. . . . . . . . . . . . . . . . . .
228
4.9
Effects of Objective Ambivalence on the Attention to Kerry
Information Index in the 2004 Presidential Campaign, OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
229
Effects of Subjective Ambivalence on the Attention to Kerry
Information Index in the 2004 Presidential Campaign, OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
230
4.10
xiv
4.11
Effects of Objective Ambivalence on the Attention to Bush
Information Index in the 2004 Presidential Campaign, OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
231
Effects of Subjective Ambivalence on the Attention to Bush
Information Index in the 2004 Presidential Campaign, OLS
Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
232
4.13
Summary of Each Ambivalence Measures Affect on InformationSeeking Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
232
4.14
Effect of Objective Ambivalence on Self-Reported Likelihood of
Voting – Wave 1, Ordered Probit Regression. . . . . . . . . . . . . . . . . . .
233
4.15
Effect of Objective and Subjective Ambivalence on Self-Reported
Likelihood of Voting – Wave 1, Ordered Probit Regression. . . . . . . .
234
4.16
Effect of Objective and Subjective Ambivalence on the Wave 2
Motivation to Vote Index, OLS Regression. . . . . . . . . . . . . . . . . . . . . 235
4.12
xv
LIST OF FIGURES
Figure
2.1
2.2
2.3
2.4
Page
First Differences for Probability of Reading any Issue Statement:
Maximum Ambivalence Probability – Minimum Ambivalence
Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
110
First Differences for Probability of Reading a Candidate Specific
Statement: Maximum Ambivalence Probability – Minimum
Ambivalence Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
111
First Differences for Probability of Reading a New Candidate Issue
Statement: Maximum Ambivalence Probability – Minimum
Ambivalence Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
112
First Differences for Probability of Reading a Candidate Agreement
Statement: Maximum Ambivalence Probability – Minimum
Ambivalence Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
113
2.5
First Differences for Probability of Reading a Candidate
Disagreement Statement: Maximum Ambivalence Probability –
Minimum Ambivalence Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . 114
2.6
First Differences for Probability of Correctly Recalling Candidate
Issue Positions: Maximum Ambivalence Probability – Minimum
Ambivalence Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
115
3.1
Percent of Respondents with Negative Griffin Ambivalence Scores
(e.g. Low Ambivalence) Who Have More Unfavorable than
Favorable Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
3.2
Percent of Respondents with Positive Griffin Ambivalence Scores
(e.g. High Ambivalence) Who Have More Favorable than
Unfavorable Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
4.1
First Differences – Effect of Objective Ambivalence Self-Reported
Vote Likelihood: Maximum Ambivalence Probability – Minimum
Ambivalence Probability, Wave 1. . . . . . . . . . . . . . . . . . . . . . . . . . . .
xvi
236
4.2
4.3
First Differences – Effect of Objective Ambivalence Self-Reported
Vote Likelihood: Maximum Ambivalence Probability – Minimum
Ambivalence Probability, Wave 2. . . . . . . . . . . . . . . . . . . . . . . . . . . .
237
First Differences – Effect of Subjective Ambivalence Self-Reported
Vote Likelihood: Maximum Ambivalence Probability – Minimum
Ambivalence Probability, Wave 2. . . . . . . . . . . . . . . . . . . . . . . . . . . .
238
xvii
CHAPTER 1
INTRODUCTION – AMBIVALENT ATTITUDES
Political elites in America often portray political battles as zero-sum games.
Typically, they cast issues as stark choices: the inviolability of civil liberties versus
the needs of national security, an unborn child'
s right to life versus a woman'
s right to
choose, affirmative action versus egalitarianism. The public, however, often finds
manifold shades of gray instead of the elites'black and white. Countless studies show
that support for a policy varies depending on how the issue is framed (Kahneman and
Tversky 1979, 1984; Nelson, Clawson and Oxley 1997; Nelson and Oxley 1999;
Schuman and Presser 1981) and that the same people will often change positions on
an issue when surveyed at different times (Converse 1964; Hochschild 1981;
Feldman and Zaller 1992). Such studies are often seen as evidence that the public
holds unstable, malleable and ill-defined attitudes on various issues of the day. After
all, how can a person reasonably support tax cuts at one point, oppose them a month
later, only to support them yet again a few months later? There is no doubt that real
attitude conversions occur among the public from time to time, but there is also no
doubt that many people are not firmly wedded to one side of the issue at any given
time point.
1
Rather than treat these shifts as evidence of ill-defined attitudes, recent work
in social and political psychology indicates that many people may hold stable, but
ambivalent, attitudes (cf. Alvarez and Brehm 1995, 1997, 2002; Craig, Kane and
Martinez 2002; Lavine 2001; McGraw, Hasecke and Conger 2003; Zaller 1992).
Ambivalence means that a person can simultaneously maintain a set of positive and
negative beliefs about an attitude object, such as a political issue or a candidate. That
is, while many people are pro-choice because they support a woman’s right to choose
or are pro-life because they support a child’s right to life, a number of people are
simultaneously pro-life and pro-choice because they believe both in a child'
s right to
life and in a woman'
s right to choose. Although such a mixed assessment would
seem inconsistent and contradictory to an elite, this hybrid attitude seems perfectly
reasonable to many people. Much of the public see no reason why their abortion
attitude cannot acknowledge the legitimacy of woman’s right to choose as well as that
of a child’s right to life.
The idea that an attitude itself can consist of both positive and negative
considerations somewhat contradicts the traditional view of attitudes. Typically,
attitudes have been defined as one’s general predisposition towards some matter, such
as a policy issue. The limitation of the traditional view is that it further assumes that
a person’s general predisposition would be positive, negative, or neutral. While
people may commonly make a positive or negative evaluation, there is no reason why
an individual’s general evaluation of an object cannot consist of both positive and
negative components. Therefore, rather than regard attitudes as one’s general
predisposition of an object, a better definition views them as the set of considerations,
2
both positive and negative, that one thinks about when assessing an object. From this
perspective, attitudes are still considered to be a person’s evaluation of an object, but
that evaluation is now allowed to be multidimensional. This perspective enables
people to have a clearly defined set of conflicting beliefs that might lead them to
express support for or opposition to an issue at different times – all the while having a
stable attitude. What was once seen as evidence of weak attitudes may in fact simply
reveal ambivalent ones.
Although most studies focus on ambivalent issue attitudes, the public can also
develop ambivalent attitudes about political candidates. The two concepts are similar
– both occur when an individual holds both positive and negative considerations
about the issue or candidate – but there are a few notable differences. First, while the
competing considerations that generate issue ambivalence tend to be well established
for the public on each issue, this is not the case for candidate ambivalence. For
example, if we know that a person has an ambivalent attitude towards abortion, then
we are virtually certain that this person is weighing a woman’s right to choose against
a child’s right to life. In contrast, the factors that generate candidate ambivalence are
not uniform across the population. Knowing that a person is ambivalent about a
presidential candidate does not tell us what specific considerations are driving the
conflicted feelings. Take, for example, John Kerry. A person may agree with
Kerry’s policy to protect American jobs, but disagree with his opposition to a
constitutional ban on gay marriage. But it is equally plausible that this person agrees
with Kerry’s position on tax cuts, but disagrees with his pro-choice voting record.
Moreover, candidate ambivalence may not be driven by issue considerations at all; it
3
could be the product of any number of considerations, such as personality traits or
one’s affective reaction to the candidate.
Another distinguishing factor about candidate ambivalence is that an
individual must ultimately confront and reconcile these conflicted considerations in a
campaign. Simply stated, on Election Day an individual must either vote in favor of
one candidate or not cast a vote at all. It is impossible for someone to stake out a
middle ground on Election Day, as one’s decision is all or nothing. This is a sharp
contrast to the environment in which issue ambivalence occurs, where individuals are
never compelled to reconcile their opposing considerations. An ambivalent person
can easily state support for abortion in a survey or conversation at one moment with
the knowledge that she can modify her statement at a later date. The voter in an
election has no such luxury; one'
s decision on Election Day is ineradicable. In sum,
while broadly similar to issue ambivalence, candidate ambivalence has clear
differences. Thus it becomes important to devote attention to the specific effects of
candidate ambivalent attitudes.
It is also important to focus on candidate ambivalence, as there are reasons to
believe that such attitudes will become more prevalent among the electorate. In
today’s elections nearly every facet of a candidate’s life is carefully scrutinized by the
media, the opposing candidate, and various interest and issue advocacy groups. The
public learns not only about candidates’ positions and platforms, but also about nearly
every facet of their personal life. Additionally, the number of players who seek to
“inform” the public is growing. Campaigns are no longer candidate-centered affairs
in which the individuals running for office can largely control what messages get out
4
to the public. The result of all this “education” is a public that is inundated with
countless messages that push and pull them in multiple directions when assessing the
candidates. While many people may be able to fend off or ignore much of this
information, others will integrate some of these assertions into their own set of
considerations, producing more people with ambivalent candidate attitudes.
So what are the effects of candidate ambivalence? Surprisingly, while the
general concept of ambivalence is not new to political science, the bulk of attention
has been devoted to issue ambivalence. Scant work exists that focuses solely on
candidate ambivalence, though a few new studies suggest a possible shift in this trend
(cf. Lavine 2001; McGraw, Hasecke and Conger 2003; Meffert, Guge and Lodge
2004). These recent studies aside, a number of questions remain about the influence
of candidate ambivalence on electoral politics. I address this deficiency by
examining two little-studied consequences of candidate ambivalence. First, I
determine whether candidate ambivalence induces people to seek out more
information about the candidates. There is evidence that suggests the existence of
such behavior. Political scientists have shown that ambivalent voters wait longer to
decide which candidate to support (Lavine 2001) and hold more accurate information
about the candidates (Meffert, Guge and Lodge 2004), while social psychologists
have found a link between ambivalence and systematic processing (Cunningham et al.
2003; Jonas, Diehl and Brömer 1997; Maio, Bells and Esses 1996). Thus, for
example, if we know that ambivalence is positively related to issue accuracy, then it
stands to reason that this relationship exists because ambivalent people acquire more
information about the candidates. Additionally, if the ambivalent are waiting until
5
later in the campaign to decide which candidate to support, then it is also likely that
they are acquiring more information during this time in order to make their decision.
Despite the evidence that suggests such a process occurs, there is yet no evidence
documenting its existence.
Findings like these tend to imbue the ambivalent voter with a set of traits that
closely resembles the common view of the ideal democratic citizen. They suggest
that ambivalence causes the public to actively engage their political environment, to
seek out information, and to deliberate upon (i.e. systematically process) this
information in a thorough manner when assessing the candidates. The problem,
however, is that the above findings can also be explained if the ambivalent are more
likely to engage in memory-based processing. People who engage in memory-based
processing evaluate a candidate based upon specific pieces of information they
retrieve from memory at the time they make the judgment. Conversely, people who
engage in on-line processing simply store summary evaluations of the candidates in
their memory and do not retain the specific pieces of information used to form their
summary evaluation. Thus, if ambivalence promotes memory-based processing, then
it is not surprising that ambivalence is related to accuracy, as these people are merely
more likely to recall the specific pieces of information they encounter over the course
of the campaign. From this perspective, ambivalent voters do not seek or acquire
more information; they simply are better at recalling this information at a later point.
Thus, the information-seeking hypothesis needs to be pitted against the alternative
memory-based processing explanation.
6
The second question I explore is whether ambivalent attitudes make
individuals more likely to abstain in elections. There are again a number of reasons
to believe that ambivalence would cause one to withdraw from the political process.
First, from a psychological perspective, it can be emotionally and cognitively difficult
to resolve one’s internally conflicted feelings. Quite simply, people do not like to
engage in trade-off reasoning and will often seek to sidestep the problem altogether
(Tetlock 2000). Thus, voters who must assess candidates who embody both positive
and negative traits will find it more difficult to arrive at an overall evaluation.
Instead, these voters may simply avoid the problem by abstaining in the election.
Rational choice theory provides a somewhat different reason for why
ambivalence might lead to abstention. It asserts that a person will vote for the
candidate who provides the voter with the greatest expected utility (e.g. Downs
1957). As ambivalence about a candidate increases, then this potential voter has a set
of positive considerations that increase her expected utility and a set of negative
considerations that diminish it. Ultimately, the ambivalent voter may conclude that
she is rationally indifferent towards this candidate and decide that it is simply not
worth her energy to vote in the election.
It is important to note that from either the psychological or rational choice
perspective, abstention does not occur as a result of uncertainty, lack of information,
or indifference to the issues involved in the campaign. Instead, abstention occurs
because the voters have too much information that they cannot reconcile on Election
Day to determine which candidate to support. Although the ambivalent individual
may see abstention as a better choice in relation to the choices on the ballot, they may
7
not see it as their ideal option. The ambivalent may want to vote in an election, but
fail to do so because there is no candidate on the ballot who represents their own
interests. Therefore, an interesting caveat to the ambivalence-abstention linkage
emerges when independent challengers are added to the electoral choice set. While
ambivalent voters may be less likely to support either the Democratic or Republican
candidate in an election, they may be more likely to support independent candidates.
This allows them to stay involved in the electoral process without having to reconcile
their conflicted attitudes about the two major party candidates.
In sum, there are a number of reasons to study precisely what role ambivalence
plays in voting behavior decisions. In this dissertation I investigate the foregoing
questions with data from experiments, national surveys, and a panel survey of
undergraduate students. First, in chapter two I draw upon original experimental analysis
that explicitly manipulates the extent to which subjects feel ambivalent towards
candidates running for the U.S. Senate and assess their information-seeking behavior
and motivation to vote in an election. Specifically, subjects are presented the issue
positions of the two candidates and are then given the opportunity to read more detailed
issue statements by the candidates at a later point in the study. This chapter adds to our
understanding of ambivalence in two ways. First, it is the first study to use experimental
analysis to systematically test the relationship between ambivalence and informationseeking behavior. Second, it is also, so far as I know, the first study in political science
that explicitly manipulates ambivalence, enabling me to make stronger causal claims
than previous studies have been able to make.
8
While experimental analysis is an extremely powerful tool for revealing causal
processes, it is often limited in its level of external validity. Therefore, in order to
increase confidence that my experimental findings can be generalized to a larger
population. I use National Election Study data to examine attitudes towards presidential
candidates. The results appear in chapter three. I determine if ambivalent members of
the public are more likely to report engaging in information-seeking behaviors, such as
devoting more time to media use or acquiring greater information about the candidates.
Second, I examine if ambivalence is positively related to abstaining in an election.
Finally, when available, I determine if ambivalence is positively related to independent
candidate support.
Next, in chapter four I use data from an original panel study of undergraduates,
examining their attitudes of the candidates in the 2004 Presidential election. The use of
panel data allows me to see how attitudes and behaviors at time one influence the same
attitudes and behaviors at time two. In this analysis, I let the passage of time during an
actual presidential campaign serve as my stimulus. While this means that I lose some
control over the design, it enables me to see how the attitudes and behaviors related to
actual political candidates change over time.
Before turning to the results, I discuss in this chapter why it is important to study
ambivalence. Although ambivalence has received increasing attention in studies of
political attitudes, it is a concept that is still only vaguely defined. I argue that before
ambivalence can be fully integrated into theories of political behavior it is important to
revisit the concept of attitudes themselves. Over the course of decades of research on
political opinions, attitudes, and behaviors, the definition of attitudes has become
9
muddled. Most notably, studies frequently conflate the concepts of attitudes and
behaviors. Although blurring these lines usually does not lead to problematic
conclusions for the questions involved in those studies, the concepts must be
disentangled before the role of ambivalent attitudes can be fully understood. After
discussing the concept of ambivalence itself, I examine the research that has been done
on ambivalence. As I will show, while there have been notable gains in our
understanding of how ambivalence affects behavior, a number of questions remain.
Specifically, I demonstrate why it is important to study the relationship between
ambivalence and both information-seeking behavior and abstention.
Overall, this research is significant for two main reasons. First, there is
growing awareness that ambivalence is an important component of political attitudes,
but little work has been done to explicitly test the consequences of these attitudes.
My research fills this gap and further elucidates the role of ambivalence on political
behavior. Second, if my hypotheses are confirmed, then the results raise important
normative questions about the extent to which our political system is genuinely
representative. Specifically, given the importance that elections play in our
democratic system for connecting public desires with governmental outputs, it is
always worrisome when we can identify any groups that are underrepresented by
these linking mechanisms. While we generally worry about the representation of
specific demographic groups (e.g., women, minorities, etc.), my research reveals that
ambivalence potentially mutes the voice of an important segment of the public – a
matter of particular significance precisely because ambivalent people may have well
thought out opinions based on a large set of information, yet decide to opt out of the
10
process. Taken as a whole, my research not only further explains political behaviors,
it also raises important questions about the level of representation in our political
system.
Attitudes v. Behaviors
Scholars have long been interested in understanding and studying attitudes
because of their belief that attitudes strongly affect the way that people interact with
their environment and surroundings, most notably that attitudes influence behaviors.
For instance, people who consider themselves environmentalists are more likely to
participate in recycling programs. Consumers buy the latest Nike shoes because they
want to be like LeBron James. In the political domain, when people believe that
Kerry agrees with them on more issues than Bush, they tend to support Kerry with
donations of money, volunteered time, and votes on Election Day. Likewise, during
surveys people express support for the war in Iraq based on their attitudes associated
with the war. As political scientists we are primarily concerned with attitudes
because we believe they influence the behaviors we are most interested in – support
for a policy, campaign contributions, activism, abstention, etc.1
In the above paragraph I consciously distinguished between the attitudes we
have towards political objects, such as policies and candidates, and our behaviors that
are related to these objects, such as expressing support for a policy in a survey or
casting a vote for a candidate. This is a subtle, but important, distinction. Acts, such
1
Behaviors can also influence our attitudes. As Bem (1967) demonstrates, people often infer their
feelings towards an object by looking back on their past behaviors. For instance, if one is asked how
much they like a given restaurant over another one, they may recall that they have dined at one many
more times than the other. Thus, given this discrepancy in patronage, this person determines that he
likes that restaurant more.
11
as statements made during a survey or voting in a ballot booth, should be viewed as a
unique type of behavior that is affected by our attitudes. These responses may reveal
part of our attitude, but they do not necessarily reveal the full structure of our attitude.
For instance, if we learn that a person expresses moderate support for the war in Iraq,
then we know this person’s attitude is constructed of at least some positive
considerations on the war. However, we do not know if this person’s attitude is also
comprised of a set of negative considerations. As a result, we should not equate
expressions of support for a policy (e.g., the war in Iraq) with one’s attitude towards
the policy. While the two concepts are strongly related, they are nonetheless distinct.
Unfortunately, attitude scholars often equate attitudes about an object with responses
to questions about the object. For instance, Converse’s (1964) finding that there is a
weak correlation between responses to opinion questions at two time points is often
cited as evidence of attitude instability. But Converse never measures individual
attitudes; rather he measures individual expressions of support for various issues, a
specific form of political behavior. Thus, it is inappropriate to make assertions about
attitude stability from this data, though it is fully valid to assert that there is response
instability over the time period.
Another way to think of this is to treat behaviors, such as responses to survey
questions, as observable indicators of latent concepts – attitudes. The extent to which
a person’s latent attitude is positive, negative or mixed will affect the likelihood of a
given behavior, such as stating support for a policy or candidate. Just as we would
expect a person with pro-environmental attitudes to engage in recycling behaviors, we
would expect a person with fiscally conservative attitudes to express support for tax
12
cuts on a survey. Moreover, a person with a large number of positive environmental
considerations is likely to recycle regularly, while one with a mix of positive and
negative environmental considerations is likely to engage sporadically in recycling
behaviors. Therefore, learning that a person recycles only some of the time is not
definitive evidence of attitude instability, but may instead indicate that the person
holds a set of mixed considerations. Likewise, learning that a fiscal conservative did
not express support every tax cut does not negate the stability of her fiscally
conservative attitude. Our attitudes do not dictate our behaviors, but instead affect
the likelihood of our behaviors. Observing inconsistency in behaviors should not be
seen as conclusive evidence of an ill-defined or unstable attitude.
The problem of conflating attitudes and behaviors has a long history in both
psychology and political science. The early work on attitudes started with the
premise that attitudes should be measured and conceptualized as one’s placement
along a bipolar scale, ranging from highly positive to highly negative with a neutral
point in the middle (Thurstone 1928). From this vantage point, attitudes are seen as a
zero-sum game; in order for someone to view an object more positively, then this
person must, by definition, view the object less negatively. Intuitively, this makes
sense and viewing attitudes as a zero-sum assessment accords nicely with our
conception of politics. One simply needs to recall Laswell’s popular definition of
politics as the conflict over “who gets what, when, and how” (1936) to see how nicely
this view of attitudes complements our notion of politics. The day after an election
there is a set of winning candidates and a set of losing candidates; after each session
of Congress there are laws that legalize some actions and prohibit others; and each
13
year the Supreme Court clarifies any ambiguities in who gets what, when, and how.
In each case, the set of outcomes is mutually exclusive. A candidate cannot both win
and lose the office, and we cannot have a law that both allows and bans partial birth
abortions. Politics is a game of winners and losers and understanding what leads
people to support or oppose one or another contending option is a matter of central
importance. Thus, studies such as Converse’s (1964) are valuable because they
demonstrate that individuals often are not firmly for or against many political issues.
This type of knowledge, in turn, helps explain many facets of the political process.
For instance, it is not surprising to find politicians taking vague issue positions when
public opinion surveys often show that support for issues vacillates over time or
varies depending on question wording. While useful in many regards, studies that
conflate behaviors with attitudes do not reveal what changes, if any, might be
occurring to one’s attitude.
If politics is ultimately about who wins and loses, then one might ask, why
bother to make any distinction between attitudes and behaviors at all? That is, what
leverage is gained by examining attitude structure instead of focusing solely on
survey responses that assess support or opposition for an issue or candidate? The
answer is simply that without doing so we will never fully explain or understand
political behaviors. More importantly, if we do not make such a distinction, we not
only risk providing incomplete explanations, we risk providing false ones. Consider
the person who expresses support, opposition, and then support for a tax cut policy at
three time points. If we define this person’s attitude on this policy as her expressed
level of support or opposition to this policy, then this definition constrains the
14
conclusions we can draw about her attitude. Either this person undergoes actual
attitude conversions and is an example of attitude instability over this time period, or
this person has a non-attitude on the issue and essentially states support or opposition
as random responses to the question.2 In the traditional view of attitudes, no
allowance is given to the possibility that that the person has a stable and clearly
defined, but conflicted attitude that leads to response instability. In fact, allowing that
possibility contradicts the common definition of an attitude, which constrains a
person to an attitude of support, opposition, indifference, or ignorance ("don'
t know").
By definition, the traditional view never allows one to have an attitude that is
simultaneously positive and negative.
What happens if we view an attitude as one’s general evaluation of an object
and define the response to a survey question as a behavioral manifestation of that
attitude? First, the same two options listed above can still explain the outcome.
Changes in responses over time may reveal attitude conversion (attitude instability) or
non-attitudes (random guessing). A third explanation, however, becomes available,
namely that a stable but conflicted attitude underlies these responses. It is possible
that a person held reasons both to support and oppose tax cuts and maintained a stable
attitude over the time period. Given the consistent and stable conflicted feelings this
person experiences, it is not surprising that her behaviors (e.g., expressions of
support) related to the issue appear unstable when she was asked to select among a
zero-sum set of choice options. Our ability, then, to explain this person’s behavior is
2
Technically, one could have a mix of the two outcomes. For example, one could have experienced a
non-attitude at time 1, then came to oppose the issue at time 2 only to be converted to support at time
3.
15
conditional upon our definition of an attitude. If we conflate attitudes and behaviors,
then we limit the number of explanations we can draw upon to explain behaviors. In
many cases, we can still gain useful and beneficial insights when these lines are
blurred. If we want to know if some factor will make a person more likely to vote for
a candidate, then we simply need to know how candidate support differs in the
presence and absence of that factor. When we want to know why people express
support or opposition for a candidate or issue, then we need to look more closely at
the components of a person’s attitude.
Ambivalent Attitudes
As noted, going back to Thurstone’s (1928) seminal work, attitudes are
traditionally viewed as one’s overall evaluation of an object on a bipolar scale. For
the most part scholars largely ignored the question about whether this is an
appropriate definition of an attitude, and focused on other issues, such as whether
people even have attitudes on issues (e.g., Converse 1964) or how question-wording
might bias the measurement of attitudes (e.g., Schuman and Presser 1981). Thus,
while scholars sought better ways to understand and measure attitudes, there was little
examination of the definition of an attitude itself.
This is not to say that the traditional view of attitudes was never questioned.
At around the same time as Thurstone provided his definition of attitudes, Allport
(1935) began to reconsider the utility of conceptualizing attitudes on a single bipolar
scale, stating, “each person possesses many contradictory attitudes, and for this
reason his mental set at the moment of submitting to a scale may tell only a part of the
story” (832). Allport believed that we needed to take seriously the idea that people
16
must often grapple with a set of competing considerations when evaluating an object.
Just because we can get a person to summarize her various considerations on a single
scale when studying attitudes does not mean that we always should do this. Allport’s
observation that people often have conflicted feelings about attitude objects did not,
however, initially do much to change how scholars treated attitudes.
Some twenty years later, Heider (1958) proposes a “balance theory” that helps
describe the way that individuals view the world around them. Heider models a
person'
s social world as reducible to three elements or components, one of them being
the person himself or herself and the other two being some pair of external "objects"
– such as government spending and state of the economy, or a political issue and a
candidate. The relationship between each pair of the three components is either
positive or negative and the mathematic product of the three evaluations must be
positive in order to avoid dissonance. Consider the following example. The Bushperson link is negative, the Iraq war-person link is positive, so the Bush-Iraq war link
would have to be negative to make the product of the three links a positive (a negative
x a positive x a negative = a positive). Given, however, that the Bush-war link is not
in reality "negative" (Bush supports the war, as does the person), the person would
have to do one of two things to achieve internal consistency – change the assessment
of Bush or cease supporting the war.
Heider’s theory gained favor with many social psychologists. Unfortunately,
rather than compel scholars to accept the idea that it is reasonable and natural for
people to maintain an internal set of competing considerations about their world (i.e.
have a conflicted attitude), the theory became a way for scholars to explain how the
17
public avoids internalizing conflicted feelings. Thus, instead of serving to challenge
the traditional two-pole definition of an attitude, Heider’s work was used to
demonstrate when people might have problems forming such an evaluation.
Nonetheless, at a minimum, it did make scholars aware of a person'
s possessing
conflicted feelings, even if that occurrence stood only as an example of an unnatural
and uncomfortable state of being.
Although balance theory did not itself propose the idea of a stable, but
conflicted, attitude, there was a growing appreciation for this proposition at the same
time he proposed his theory. Thompson, Zanna and Griffin (1995) argue that Brown
and Farber provide the first working definition of an ambivalent attitude in their 1951
paper on conflict theory. They asserts that ambivalence occurs when the alternatives
facing an individual satisfy three conditions: (1) the options must have contradictory
implications, (2) the person must view the outcomes as nearly equal in importance,
and (3) a compromise among the options is not available (Thompson, Zanna, Griffin
1995, 363). From this perspective, it is easy to see why issues like abortion generate
feelings of ambivalence among the public. Ultimately, the options are legal or illegal
abortions, where freedom of choice is satisfied only by the former and preserving life
by the latter. A person might easily see both outcomes as equally important and also
understand that one cannot compromise between the two outcomes – it is not possible
to guarantee both the right to choose and the right to life.
Furthering the concept, Scott (1966) proposed that ambivalence be viewed as
“the degree to which objects are defined in terms of both desirable and undesirable
attributes” (391). Scott added that ambivalence should be classified as a structural
18
property of cognition, where a structural property “refers to the manner in which
elements of cognition (ideas) are interrelated” (391). By calling ambivalence a
structural property of an attitude, he elevates the status of conflicting feelings from
something that should be avoided (e.g., as in dissonance theory) to a natural facet of
one’s attitudes that needs to be accounted for by scholars. Scott makes an equally
important contribution for such analysis by providing a formula for measuring
ambivalence.
The importance of separately accounting for one’s positive and negative
feelings is further illustrated in Kaplan’s (1972) critique of the bipolar unidimensional
scales traditionally used to assess attitudes. The classic measure might use a standard
7-point scale that ranges from minus-3 to plus-3, where negative scores indicate
negative evaluations and positive scores indicate positive evaluations. For instance,
Kaplan argues that a score of minus-2 on this scale can reveal different things about
different people, depending on the number of positive and negative considerations
each person has. For some respondents it might indicate that they have many
negative thoughts on the issue and only a few positive thoughts, but for others it could
instead indicate that they have some negative and no positive considerations on the
issue. As Kaplan points out, we cannot distinguish between these two types of people
by relying solely on the standard one-dimensional scale. He adds that the question
becomes even more complex when one turns to the mid-point on the scale, which
indicates that the respondent has an equal amount of positive and negative
considerations. The problem is that this can mean one has no positive or negative
thoughts (indifference) or 75 positive and 75 negative thoughts each (extreme
19
ambivalence). Thus, when a person reports a negative or positive position on the
scale, we at least know whether they lean towards positively or negatively assessing
the object. But when people place themselves at the mid-point, it is impossible to
distinguish between two fundamentally different concepts – indifference and
ambivalence. Kaplan therefore asserts that the positive and negative components of
an attitude must be measured separately in order to distinguish among the different
types of attitudes people maintain.
As social psychology integrated the concept of ambivalence into discussions
of attitudes, opinion scholars in political science likewise began to modify their
treatment of attitudes. Perhaps the most useful innovation from political science is to
treat attitudes as a distribution of considerations and responses as one outcome this
distribution. This idea is best put forth in Bartels’ (1988) work on uncertainty. He
argues that we should not treat responses, such as rating a candidate a 63 on a
standard 100-point feeling thermometer, as an indication of the respondent’s true
assessment of the candidate. Instead, Bartels believes each response should be
viewed as one of many legitimate answers a person might give from a range of
responses. The possible responses associated with our attitudes have two main
characteristics: a central location (mean) and a distribution associated with that
central location (variance). In line with spatial theories of voting (e.g., Enelow and
Hinich 1984), Bartels defines the distribution as a measure of uncertainty, where a
wider variance indicates more uncertainty (see also Alvarez 1997; Alvarez and
Franklin 1994; Franklin 1991 for other examples that treat response variance as
uncertainty). Thus, a person who is very certain that he extremely likes a candidate
20
might have a distribution with a mean of 90 and a standard deviation of 3. This
person may not always place the candidate at 90, but the placement will always fall
very close to 90. A person who somewhat favors a candidate, but is uncertain about
this assessment, might have a mean of 65 and a standard deviation of 25. More often
than not this person will give the candidate a favorable score (i.e. a score higher than
50), but the range of responses will sharply vary each time – and sometimes will be
unfavorable (i.e. fall below 50). From this perspective, responses at a given time do
not reveal one’s true fixed attitude, but merely represents one “draw” from the set of
responses one might give at any time.
Other scholars have embraced the idea that survey responses might be a
function of a distribution of considerations, though not all of them believe that
variance should be equated with uncertainty. For instance, Feldman and Zaller
(Feldman 1995; Zaller and Feldman 1992; Zaller 1992) also argue that people sample
across a set of considerations when responding to survey questions.3 Unlike Bartels,
they do not view the range of considerations as an indicator of uncertainty in one’s
attitude; instead, they view the range as encompassing all possible opinions an
individual personally thinks are valid responses to the question. For instance,
reconsider the voter who has a response distribution with a mean of 65 and a standard
deviation of 25. Rather than treat this person as uncertain, Zaller and Feldman’s
approach allows this person to be certain, but conflicted, about this candidate. In
3
Although not using the term “distribution of considerations,” Hochschild (1981) makes a similar
argument. After conducting a series of in-depth interviews with the same people at different timepoints, she concludes that people often see reasons to support and oppose an issue. Moreover, while
one’s level of expressed support may change from time to time, the concerns one must weigh against
each other often remained constant. Hence, the attitude was often stable, merely the responses change.
21
other words, there may be things about this candidate that, when highlighted, lead this
person to sample from the lower scores in her distribution and things that may lead
her to sample from the higher end. This person tends to like the candidate, but also
holds some negative beliefs about the candidate that can pull her in the negative
direction on the scale. Any variance in this person’s responses should therefore not
be seen as indicative of her uncertainty about the candidate, but as a function of the
fact that she has competing considerations about the candidate. There is no reason
why she cannot be very certain that she has competing considerations.
This perspective allows one to hold both positive and negative considerations
about an attitude object, to be ambivalent about the object, while still providing
different responses to surveys over time. In fact, a “fundamental claim” of Zaller’s
theory is that, on most issues, most people have a “tendency toward some degree of
ambivalence” (59). He adds that “in an environment that carries roughly evenly
balanced communications on both sides of issues, people are likely to internalize
many contradictory arguments, which is to say, they are likely to form considerations
that induce them to both favor and to oppose the same issue” (59). By bringing in the
idea of ambivalence, Zaller shows that a stable, albeit internally conflicted attitude,
can lead to instable behaviors, namely variation in survey responses.
In an attempt to reconcile these two approaches, Alvarez and Brehm (1995,
1997, 1998, 2002) have used a series of inferential models to determine when
response variance indicates uncertainty and when it indicates ambivalence. They
argue that if response variance decreases when information increases, then this
indicates uncertainty about the issue. However, if response variance increases with
22
added information, then this indicates ambivalence on the issue. In the former case,
more information leads to more stable responses (e.g., smaller distribution), while
more information decreases stability in the latter (e.g., larger distribution). To test
this theory, they use a series of heteroskedastic models that estimate both the central
tendency of responses and the variance surrounding those responses (which is akin to
using classic regression models to predict the residuals in a regression model). They
find that some issues (e.g., affirmative action) are associated with uncertainty, while
others (e.g., abortion) exhibit ambivalence. While these models are useful for
describing whether the public is uncertain or ambivalent about an attitude, they are
unable to make inferences about individual level processes. Nonetheless, they further
demonstrate the utility of thinking of attitudes as a possible composite of positive and
negative considerations.
As scholars become more aware of the importance of ambivalence for
understanding behavior, they are also, unsurprisingly, devoting more attention to
providing better definitions of the concept of ambivalence itself. The typical
definition states that ambivalence occurs when people simultaneously hold a set of
“competing considerations relevant to evaluating a political object” (Lavine, 915,
2001). In the past few years, however, scholars have sought to refine the concept of
ambivalence. The emerging evidence indicates that ambivalence should not be seen a
uniform concept, but rather one that takes many different forms.
Although this literature is still in its nascent stages, social psychologists
generally agree that ambivalence can be loosely divided into two types: objective and
subjective (cf. Newby-Clark, McGregor, and Zanna 2002; Priester and Petty 1996;
23
Thompson, Zanna, and Griffin 1995). Objective ambivalence occurs when a person
has internalized a number of positive and negative considerations about an attitude
object, but may not consciously be aware of feeling conflicted about the matter at any
given time. For instance, when asked about affirmative action, a person might state
that she thinks it is a good policy because it helps remedy a number of systematic
barriers that minorities face in society. When asked the same question at another time
she might state that she thinks it is a bad policy as it practices a form of reverse
discrimination. At either time, this person is not consciously aware of or considering
the opposing side of the argument when responding to the question. When
objectively ambivalent people respond to questions, they are likely to answer on the
basis of the considerations that are salient and accessible to them at the time of
judgment. Thus, at one point, they may firmly state their opposition to affirmative
action programs when their opposing considerations are salient, unaware of the
relevant supporting considerations they hold on the issue as well. And the converse is
true when they assert their support of the programs. In short, people who have
objectively ambivalent attitudes have positive and negative considerations that can
pull them in opposing directions, but are not consciously dealing with this mental tug
of war at any given point.
Objective ambivalence is typically measured by separately assessing a
person’s positive and negative feelings. For instance, one might ask a person to place
him- or herself on a scale that ranges from no negative feelings to a lot of negative
feelings, and then do the same for positive considerations. Another common
approach is to ask people to list separately the number of positive and negative
24
aspects they associate with the object. A researcher who is interested in ambivalent
attitudes towards abortion might ask respondents to ignore whatever reasons they can
think of to oppose abortion and simply list the reasons why they personally might
support this issue (and vice-versa with the reasons they might oppose the issue).
These responses are then inputted into some formula in order to get a summary
measure of ambivalence (see Priester and Petty 1996; Thompson, Zanna, and Griffin
1995 for more discussion on the various available formulas).
Subjective ambivalence occurs when a person is consciously aware of being
conflicted about a given matter. People who are subjectively ambivalent might
preface a statement by saying something like “I can see both sides to the issue,” or
“While X is important, we cannot ignore Y.” Subjective ambivalence is measured by
asking people the extent to which they feel conflicted or pulled in different directions
when making an assessment. The first step in feeling subjectively ambivalent is
developing objectively ambivalent attitudes. After all, one cannot feel subjectively
torn on an issue until one believes there are positive and negative aspects to the issue.
In fact, studies show that subjective and objective ambivalence are generally weakly
correlated at about r = .40 (Priester and Petty 1996; Thompson, Zanna, and Griffin
1995), suggesting that these are two distinct factors. In other words, people who state
they have both a number of positive and negative feelings about a candidate or issue
will not necessarily also state that they feel subjectively torn when evaluating the
issue or candidate. Using a survey design, Newby-Clark, McGregor, and Zanna
(2002) collect measures of both objective ambivalence as well as reaction times to
determine how quickly one can access both positive and negative considerations. Not
25
surprisingly, they find that higher levels of objective ambivalence are positively
related to subjective ambivalence. More interesting, they find that the interaction of
objective ambivalence with accessibility is also positively and significantly related to
subjective ambivalence. Individuals who have both high levels of competing
considerations and who can access these considerations quickly are associated with
the highest levels of subjective ambivalence. Using an experimental design,
McGraw, Hasecke, and Conger (2003) manipulate the amount of objective
information respondents were exposed to and also found a positive, albeit weak,
relationship between objective ambivalence and subjective ambivalence for
individuals with low political knowledge. Although more work that compares
objective and subjective ambivalence is needed, particularly studies that determine
how people move from objective to subjective ambivalence, the findings so far
demonstrate that the two types are distinct.
As mentioned earlier, ambivalence can also differ depending on the object of
consideration. To date, nearly all studies of political ambivalence have focused on
either issue or candidate ambivalence. While it is useful to examine the effects of
ambivalence towards each candidate in the context of a campaign, the question that is
perhaps most interesting is what happens when people are conflicted about their vote
choice? What happens when we move the attitude object from the individual
candidates to the office of the presidency? In other words, what are the electoral
consequences when people are conflicted about which candidate to vote for? A
person who views both candidates positively (or both negatively) might be
ambivalent about which candidate to support at the ballot, yet is not ambivalent about
26
either candidate individually. I will refer to the conflicted feelings one has in
choosing between the two candidates as decision ambivalence and the conflicted
feelings one has about a specific candidate as candidate ambivalence. To my
awareness, Lavine (2001) is the only person to examine decision ambivalence, and he
gives it only passing attention. He finds that people who experience high levels of
decision ambivalence are more likely to report supporting different candidates on the
pre- and post-election interviews.
It is unfortunate that decision ambivalence has not received more scholarly
attention. While many people know whom they will support in a campaign months
before Election Day, a sizable segment of the population remains conflicted or
undecided for much of the campaign. In fact, in the months leading up to the election
we often see dramatic shifts in the poll numbers for the two candidates, a candidate
might lead by nine points on one day, trail by two a week later, and so forth. One
explanation for these shifts in the polls is that a number of people have changed their
allegiance from firmly supporting one candidate to firmly supporting the other
candidate. Additionally, over the course of a campaign a number of people will move
out of the undecided camp and come to put their support behind one of the candidates.
This surely tells the story for some of the public, but the story should be completed by
accounting for people who are conflicted about whom to support in November. In the
weeks preceding the election, these people will lean towards one candidate or the other
depending on various factors at the time of the survey, such as recent news stories about
the candidates, debate performance, or a discussion with a friend at work. In other
words, these shifts are likely driven in part by decision ambivalence that pushes and
27
pulls voters towards each candidate throughout the campaign. Just as we need to focus
on the different effects of subjective and objective ambivalence, we should also pay
attention to the effects of candidate and decision ambivalence.
Because of the attention that scholars are giving to ambivalence, not only
must its influences be documented but the concept itself needs to be better
understood. It is no longer sufficient simply to assert that ambivalence is the state of
holding simultaneously conflicting considerations. A major weakness with this
definition is that it does not allow the researcher to distinguish between the effects of
objective and subjective ambivalence on political attitudes. Ultimately, research is
needed on how these various components work together to affect political attitudes,
including an understanding of what factors lead to the development of both subjective
and objective ambivalence.
What We Do and Do Not Know About Ambivalent Attitudes
Having established that ambivalent attitudes exist (e.g. Feldman and Zaller
1992; Hochschild 1981), scholars are now exploring its effects on public opinion.
For instance, Nelson and colleagues demonstrate that political elites can move mass
opinion by drawing upon the public’s competing considerations on an issue (Nelson,
Clawson and Oxley 1997; Nelson and Oxley 1999). These studies show that elites
use language that helps the public prioritize the importance of its competing
considerations. For instance, elites seeking to win over a public conflicted about
affirmative action will use language that “educates” the public that the most important
aspect of the issue is the need to remedy past injustices. In their 1997 study, Nelson,
Clawson, and Oxley examine how the competing claims of free speech vs. public
28
order influence the public'
s readiness to allow the KKK to hold a political rally.
Using response times to words associated with each argument, they find that framing
rhetoric does not increase the likelihood of certain considerations being more
accessible than others. This indicates that the public is at least equally aware of the
supportive and opposing arguments when assessing the issue. Of course, this equality
could result from anything ranging to equal unawareness of each side (indifference)
or high levels of awareness of both sides (ambivalence). Similarly, Bassili (1996)
demonstrates that individuals with higher levels of competing considerations are more
vulnerable to persuasive messages than are individuals with a more monolithic set of
considerations. Such findings suggest that the extent to which public opinion
fluctuates is at least partially a function of how much ambivalence exists among the
public in general.
Various scholars argue that ambivalence is positively related to response
instability. First, Alvarez and Brehm (1995, 2002) examine attitudes towards
abortion and find that individuals who have reasons to both support and oppose an
issue (i.e., are ambivalent) are associated with higher levels of error variance in their
responses. Steenbergen and Ellis (2003) replicate this finding by assessing the effects
of affective ambivalence on candidate evaluations.4 These studies demonstrate that it
is harder to predict the opinions of individuals who experience issue ambivalence,
suggesting that ambivalence produces greater response instability. This finding is
consistent with Zaller and Feldman’s (1992) study of welfare attitudes where they
4
According to Steenbergen and Ellis, affective ambivalence exists when a person experiences both
positive and negative emotional reactions to a candidate. In contrast, most studies examine what
Steenbergen and Ellis refer to as “cognitive” ambivalence, which is based on any set of competing
beliefs about a candidate.
29
draw upon a two-wave panel design and show that ambivalent respondents are more
likely to change their response over time. Finally, Hochschild (1981) uses in-depth
interviews to study public opinion and also concludes that conflicted individuals are
more likely to show greater inconsistency in their responses.
There is also evidence that ambivalence related to political candidates affects
evaluations. First, a number of studies show that ambivalence is negatively related to
candidate assessments (Holbrook et al. 2001; Lavine 2001; McGraw, Hasecke and
Conger 2003). For instance, Lavine demonstrates that the interaction of pre-election
NES feeling thermometer scores with ambivalence measures is negatively related to
post-election scores. This indicates that individuals with ambivalent attitudes are
more likely to lower their assessments of the candidates over the course of the
campaign. This finding accords nicely with Cacioppo, Gardner and Bernston’s
(1997) theory of asymmetric attitude formation, which states that people generally
give more weight to negative considerations than to positive considerations. Thus,
when one is ambivalent and has roughly the same number of positive and negative
considerations about an attitude object, this person is likely evaluate the object
negatively as a result of this unequal weighting process.
Ambivalence has also been found to affect the extremity and certainty of
opinions about candidates. Meffert, Guge and Lodge (2004) show that ambivalence
is associated with more moderate evaluations of presidential candidates on feeling
thermometer scales. Using a folded thermometer score as their dependent variable,
Meffert, et al., find that ambivalent individuals are more likely to place candidates
near the mid-point. Interestingly, McGraw and Bartels (2005) argue that this may not
30
be the result of more moderate evaluations by ambivalent individuals; rather it may
be the result of the negativity effect of ambivalence. They point out that the mean
feeling thermometer score for candidates tends to hover around 60 and thus the
negative impact of ambivalence would tend to push one towards the mid-point of the
scale. Turning to uncertainty, studies also show that ambivalence is positively
associated with feelings of subjective uncertainty about one’s response in a survey, a
finding borne out via experimental (McGraw, Hasecke and Conger 2003) and survey
data (Meffert, Guge and Lodge 2004).
While not an exhaustive review of the literature on ambivalence, the above
discussion clearly illustrates that ambivalence has a substantive influence on political
behavior. In the following chapters I will examine two additional potential
consequences of ambivalence that have received little to no systematic study in the
political science literature. First, I examine whether ambivalence increases one’s
attention to and engagement in political campaigns. In general, people do not like to
internalize conflicted feelings, preferring instead to fashion a set of consistent
considerations. One way to resolve feelings of ambivalence is to seek out additional
information on the attitude object in order to determine if one is ultimately closer to
liking or disliking the object. Thus, voters with conflicted attitudes about political
candidates will presumably pay more attention to and seek more information about
the candidates running for office. Of course, as noted, this hypothesis needs to be
pitted against competing hypotheses, most notably that ambivalence promotes
memory-based processing. Second, I investigate whether ambivalence increases the
likelihood of abstaining in elections. Although many people may seek information to
31
reduce their conflict, this does not guarantee that the new information will help them
resolve their ambivalence. There are surely a number of people who remain
conflicted about the candidates on Election Day. In general, most people prefer to
avoid conflict when possible; thus, ambivalent individuals may opt to “resolve” their
conflicting feelings about the candidate by simply avoiding the decision altogether.
They may decide to stay at home on the day of the election.
• Information Seeking Behavior
Typically, when we are faced with conflicting information about competing
options, we seek more information before settling upon our choice. If we are
shopping for a car and find that each model has some good features and some
questionable ones, our next step is often to ask our friends for their opinions or go
online to learn more about the cars. Ultimately, we want information that allows us
to determine whether a given car fits our particular needs and wants – do the
advantages outweigh the disadvantages or vice-versa? There is no reason to believe
that our procedures for evaluating candidates or political issues are drastically
different from this basic process. There is an intuitive appeal to the idea that
ambivalence makes one more likely to seek out information about and pay attention
to a candidate. Intuition aside, numerous studies provide further plausibility to the
supposition that political ambivalence causes people to engage in information-seeking
behavior
For example, Meffert, Guge and Lodge (2004) find a positive relationship
between ambivalence and a person’s ability to accurately place a candidate’s issue
position on an ideological scale. Ambivalence is measured via responses to open32
ended questions and accuracy as the deviation between an individual’s placement of a
candidate on the scale from the sample mean of all respondents (see Alvarez 1997 for
more details on this approach). This finding, however, does not tell us how or why
these individuals have more accurate information about the candidates. One
explanation for the higher level of accuracy among ambivalent individuals is that they
have sought more information in an attempt to resolve their competing
considerations.
Another possibility, however, is that ambivalent people are more likely to
engage in memory-based processing. If true, then ambivalent individuals would be
more successful in recalling the information they encounter about a candidate over
the course of a campaign. Memory-based models (e.g. Zaller 1992) argue that people
do not have fixed or ready-made responses that are available whenever they are asked
to make an evaluation. Rather, people construct their opinions based on information
retrieved from memory at the time of judgment. If ambivalence promotes memorybased processing, then we would expect ambivalent voters to be more likely to
accurately recall the information they encountered in a campaign. From this
perspective it is differences in cognitive processing, not differences in information
acquisition, that drive the relationship between ambivalence and accuracy.
There is limited evidence indicating a positive relationship between
ambivalence and interest in politics or campaigns. First, McGraw and Bartels
(forthcoming) recently analyzed the relationship between institutional ambivalence
and attention to politics. They examine ambivalent attitudes towards the three
branches of government and find that ambivalence towards Congress is positively
33
associated with attention to politics in 1996 and 1997 NES surveys. McGraw and
Bartels report a weak link between attention and ambivalence towards Clinton, and
no link at all between attention and ambivalence towards the Supreme Court.5
Unfortunately, political sophistication is the only variable controlled for in the study,
thereby limiting the extent to which we can rule out other influences, such as
partisanship or efficacy.
The Columbia and Michigan studies of voting behavior examine the
relationship between conflicted feelings and interest in campaigns. Lazarsfeld,
Berelson, and Gaudet (1944/1968) find that as the number of cross-pressures on a
person increases, his or her interest in the campaign decreases, noting that these
cross-pressured voters “escaped from any real conflict by losing interest in the
campaign” (62). Although cross-pressured people are not the same as ambivalent
ones, it is reasonable to think that these people, as a group, are more likely to develop
ambivalent attitudes. Campbell, Converse, Miller and Stokes (1960/1980) examine
attitude conflict by using responses to questions “that the individual himself identifies
as being positive or negative, pro-Republican, pro-Democratic, and so forth” (81).
They find that people who provide a set of competing partisan considerations are less
likely to care who wins the election.
While these findings suggest that ambivalence impedes one’s motivation to
engage the political environment, questions abound about the causal processes at
work. If ambivalence leads people to seek information in an attempt to resolve their
5
McGraw and Bartels use Clinton ambivalence as a proxy measure of general presidential
ambivalence, as the NES surveys did not ask any questions that could be used to assess institutional
ambivalence about the Presidency.
34
conflicted feelings, then, in turn, this ought to increase their chances of making a
favorable or unfavorable judgment. Engaging in such information-seeking behavior
does not, however, guarantee that ambivalent feelings will subside. When
ambivalence persists many people may then seek to resolve their conflict by deciding
not to make a choice altogether. The relationships presented by the Columbia and
Michigan studies may only show the latter half of the process – namely that people
who remain conflicted do in fact withdraw from the process. It is possible that there
was a set of ambivalent voters who sought more information and resolved their
conflicted feelings. Thus, these studies may only be examining the attitudes of voters
who were never able to shed their ambivalent feelings.
Another set of studies show that ambivalent individuals tend to be more
thoughtful when evaluating attitude objects. Lavine (2001) finds that ambivalence is
positively related to the amount of time one takes to decide which candidate to vote
for in presidential elections. For example, holding all other values at their mean, he
shows that an individual with the lowest level of ambivalence reports deciding who to
vote for in July, while individuals with the highest levels of ambivalence state that
they did not decide until mid to late September. According to Lavine, this indicates
that ambivalent voters take more time to decide whom to vote for. Before this
explanation is accepted, however, one must consider two competing hypotheses.
First, it may not be true that ambivalent people are giving more thought to the
candidates; instead, they may delay their choice because they put off thinking about
the candidates altogether. That is, ambivalent individuals are not investing more
thought in the candidates, but are simply thinking about the candidates at a different
35
point in the campaign. Second, there is also a question of causality. Ambivalence
may not cause people to delay their choice of candidate; instead, people who take
longer to decide may be more likely to become ambivalent.
Experimental work in social psychology on ambivalence and value conflict
shows that ambivalence leads to more cognitive effort when making assessments.
Tetlock (1986) argues that value conflicted individuals draw upon more information
and are more likely to integrate their available information when they assess an
issue.6 Maio, Bell and Esses (1996) examine how people react when presented with a
number of different persuasive messages and find that ambivalence is positively
related to systematic information processing. In their 1997 study, Jonas, Diehl and
Brömer replicate this finding. More important, they also provide evidence for the
mediating link between these two concepts. In their examination of attitudes towards
fictional shampoos, they demonstrate that ambivalence leads to decreased confidence
in one’s assessment, which in turn leads to systematic processing of the relevant
information. In a study using magnetic resonance imaging (MRI) data, Cunningham
et al. (2003) show that a person confronted with an ambivalent stimulus is more likely
to show activity in the ventrolateral prefrontal cortex of the brain, which has been
linked in other studies to controlled systematic processing. These findings suggest
that ambivalence, to an extent, should lead people to be more diligent voters because
it encourages them to ponder systematically consider information they encounter.
6
While many scholars argue that it is necessary to distinguish between value-conflict and ambivalence
(e.g. Meffert, Guge, and Lodge in press), most also acknowledge that the two concepts are very
similar. Moreover, scholars of each concept tend to draw upon literatures in the other fields when
establishing theoretical predictions.
36
From this premise one can argue that ambivalence leads to normatively desirable
citizen traits.
Before praising the ambivalent voter, however, it is important to consider an
alternative explanation. It is possible that the increased amount of time and effort
ambivalent people take when evaluating attitude objects may again result from an
increased use of memory-based processing. People who engage in memory-based
processing are more likely to rely on specific information, such as policy positions,
than on general information, such as party identification. Using survey data, this is
what both Lavine (2001) and Steenbergen and Ellis (2003) find. Drawing upon
experimental results, McGraw, Hasecke and Conger (2003) also find that ambivalent
individuals are more likely to rely on specific information retrieved at the time of
judgment when evaluating candidates. In a different experiment, Steenbergen and
Ellis (2003) find that ambivalence is positively related to higher reaction times of
candidate evaluations, where higher times are argued to be an indirect indicator of
memory based processing. Of course, there are again causal issues to consider, as
these studies cannot rule out the possibility that people who engage in memory-based
processing are more likely to develop ambivalent attitudes. Nonetheless, people who
engage in memory-based processing are more likely to take longer to make an
assessment, as they have to retrieve and process their information from memory at the
time of judgment. Conversely, the on-line voter will not engage in systematic
processing because this person needs only to retrieve the summary evaluation at the
time of judgment. More importantly, it is not the case that the on-line voter is making
a hasty judgment of the candidate, but rather finds it easier to make an evaluation.
37
Given this possible explanation, it is important to pit the information-seeking
hypothesis against the memory-based processing one.
In summary, evidence exists which supports the claim that ambivalence leads
to a greater desire to seek and acquire information about political candidates. Linked
to ambivalent attitudes are a number of consequences, such as greater information
accuracy and increased attention to politics that indicate that ambivalence leads to
information seeking behavior. There are, however, other competing hypotheses that
need to be considered. The positive relationship between accuracy and ambivalence
may be the result of increased propensity for memory-based processing among the
ambivalent. Such people do not acquire more information; they simply are more
likely to retain the specifics about the information they do acquire. The problem of
causality is another concern. Studies that rely on cross-sectional survey data face the
classic problem of causal inference – is ambivalence causing or is it caused by its
covariates, or are both driven by some other third factor? Thus, the positive
relationship Lavine finds between ambivalence and the time taken to select a
candidate could arise because ambivalence causes people to spend more time thinking
about the candidates or because people who delay selecting a candidate are more
likely to develop ambivalent attitudes. It is also possible that both factors are driven
by some third factor, such as individual personality traits. For example, people who
have a high need for cognition may be both more likely to think longer about which
candidate to support as well as to assimilate a set of competing considerations about
the candidates.
38
The experimental work on ambivalence has provided some insights into the
causal processes at work, but it also has its limitations. First, few experiments have
expressly examined ambivalence towards political objects. While it is useful to know
that ambivalence towards a fictional shampoo leads to systematic processing (Jonas,
Diehl and Brömer 1997), this finding does not tell us how people act in the domain of
politics. Thus, while we have some understanding of the causal implications of
ambivalence, we do not know if these same relationships hold in a political context.
A second, and much more critical, problem with experimental findings on
ambivalence is that they do not manipulate ambivalence itself. Ambivalence studies
tend to manipulate the stimuli presented to the subjects with the expectation that the
different stimuli will generate different levels of ambivalence (cf. Cunningham et al.
2003; Maio, Bell and Esses 1996; McGraw, Hasecke and Conger 2003). The primary
advantage of experiments is that they enable us to determine precisely the causal
mechanisms at work. This is true, however, only so long as we are sure that
differences in our conditions are the product of changes in our concept of interest.
That is, the causal impact of ambivalence can only be determined when ambivalence
itself is manipulated in the study. The problem with manipulating the object and
assuming that ambivalence, in turn, also varies is that we risk conflating the effects of
ambivalence with some other factor. For instance, it may be the case that only certain
types of people, such as those with a high need for cognition, are more likely to
develop ambivalent attitudes about these objects. Thus, any results that show an
impact of ambivalence may be picking up the impact of the need for cognition.
Clearly, we need better experimental methods to study these relationships.
39
None of these concerns imply that we know nothing about the relationship
between ambivalence and information use; clearly we know much more now than we
did just ten years ago. Instead, they indicate that we must now be more exacting in
our research in order to establish precisely what impact, if any, ambivalence has on
information-seeking behavior. To date, studies of ambivalence have isolated a
number of processes that may be driving the above results – on-line v. memory-based
processing, information seeking v. information retention, etc. – and it is now time to
pit these hypotheses against each other empirically in a single study. As a
contribution to this task, in the following chapters I draw upon both existing survey
data and original research and explicitly test the relationship between ambivalence
and information-seeking behavior.
• Abstention
The discussion so far suggests that ambivalence towards a candidate might
lead a person to seek out more information about the candidate. Ideally, this new
information enables a person to resolve his or her ambivalence, but this need not be
the case. Given candidates’ penchant for ambiguity (e.g. Shepsle 1972; Page 1978),
it is not always easy to acquire concrete information on the candidates even when one
actively seeks it out. Moreover, the acquisition of new information may only further
demonstrate that the person has an ambivalent candidate attitude. A number of
people, for a variety of reasons, are certain to wake up on Election Day holding
ambivalent attitudes towards at least one of the candidates. Although these people
have invested time into the campaign and may be interested in the outcome, there is
nothing that obligates these people to vote in the election. Going back to the car40
buying analogy, a person who wants to buy a new car may remain conflicted about
the car even after getting more information about it. As much as this person may
want to buy a car (e.g., due to his needs for a new car, the time he invested in the
process, etc.), he may ultimately decide to walk away from the decision and not buy a
car at this point. In other words, ambivalence towards one car (candidate) does not
require that a person opt to buy another car (candidate); he may merely opt to make
no purchase (abstain).
It is far too common in political science to treat elections as a choice between
two (or occasionally three) main candidates, when in fact abstention is always a
viable choice for the electorate. When abstention is brought back into the calculus of
voting, it is typically treated as the consequence of some obstacle or barrier, such as
the requirement to register or the difficulty of getting to the polls, rather than as
something deriving from the candidates themselves. From this perspective, scholars
have examined the effect of SES factors, registration requirements and mobilization
efforts on turnout (cf. Knack 1995; Timpone 1998; Rosenstone and Hansen 1996;
Wolfinger and Rosenstone 1980). In some instances, evaluations of candidates can
also influence abstention behavior. An extreme ideologue may abstain in an election
because he cannot see a “dime’s worth of difference” between the two perceived
centrist options. From this vantage point, attitudes towards the choice-set of
candidates also influence a person’s decision to participate in an election. Lacy and
Burden (1999) study the impact of Perot’s candidacy in the 1992 election and
demonstrate such an effect. They show that although Perot supporters did tend to
favor Bush over Clinton, this preference was not strong enough to motivate all of
41
them to vote. Instead, Lacy and Burden show that a large proportion of Perot voters
would simply have abstained had Perot not been on the ballot.
There are a number of reasons to expect that ambivalence, either candidate or
decision, causes people to withdraw from electoral politics. The survey work of the
Columbia and Michigan studies provides some early evidence that the decisionally
ambivalent will also opt to avoid, rather than resolve, their conflict by abstaining.
Lazarsfeld, Berelson and Gaudet (1944/1968) assert that half of the non-voters in their
sample were people who either equally liked or disliked the two candidates (46).
Likewise, Campbell et al. (1960) state that people with inconsistent partisan attitudes
are, among other things, “somewhat less likely to vote at all than is the person whose
partisan feelings are entirely consistent” (83). While each study devotes no more than a
few sentences to this relationship, the idea that decision conflict leads to withdrawal
from the electoral process has an early history in political science.
From a psychological perspective, it is emotionally and cognitively difficult to
resolve one’s conflicted considerations. Quite simply, people do not like to make
tradeoffs. For instance, Tetlock (2000) shows that there are certain value tradeoffs
that people deem as “taboo,” forbidden under any circumstances. In his study, he
examines how much support there is for programs that allow people to pay for
adopted children or to buy organs for transplant operations. Not surprisingly, he
shows that a vast majority of people are opposed to such policies. However, after
adding compensatory features to ensure the fairness of the system (e.g., giving organ
credits to the poor so they also have a chance to “bid” for organs), finds that a third to
a half of the respondents come to view the policies favorably. The converse of this
42
finding, however, is that half or more of the respondents view even the compensating
trade-offs as off-limits or "taboo".
Voters will rarely, if ever, find candidates who are in total agreement with their
own views on all “burning-issue” matters. Instead, candidates will deviate from a
voter'
s values in varying ways. The greater the degree and extent of such deviations, the
more difficulty the voter will have in deciding whether to support or oppose the
candidate. As a simplified but stark illustration, consider a choice between a candidate
whom a voter perceives to be pro-choice and anti-environment and another whom the
voter views as pro-life and anti-environment. Which candidate would be preferred by a
voter who was both pro-choice and pro-environment? The initial reaction is that this
voter will surely vote for the first candidate on the commonplace principle that a partial
victory is better than a total loss. Many voters will in fact make just this decision, but
for two reasons, many might instead opt not to vote at all.
First Tetlock (2000) argues that once people engage in trade-off reasoning they
become vulnerable to criticism of their choice. Thus, the fictional voter who supports
the pro-choice/anti-environment candidate risks having to defend this choice to her
liberal friends. One can easily imagine ambivalent voters buying a bumper sticker that
changes the classic slogan from "Don'
t Blame Me, I Voted for the Other Guy" to "Don'
t
Blame Me, I Didn'
t Vote.” Second, ambivalent individuals may be more likely to
discount the importance of the decision, making it unnecessary to vote. Lazarsfeld,
Berelson and Gaudet (1944/68) state:
When people desire and shun a course of action in about equal degree, they often
do not decide for or against it but rather change the subject or avoid the matter
altogether. For many clashes of interest, the easy way to get out of the
43
uncomfortable situation is simply to discount its importance and to give up the
conflict as not worth the bother. (62)
The authors show that as the number of cross-pressures on a person increase, his or her
interest in the campaign decreases, a finding again confirmed by the analysis of
Campbell et al. (85, 1960). Thus, the ambivalent voter described above may come to
believe that there is no need to vote, as she convinces herself that the election is simply
not that important in regard to her issues. This does not mean that she does not care
about the issues, just that she no longer sees her issues to be at stake in the campaign.
For instance, although one of the candidates is pro-choice, she may argue that abortion
policies will not change during the next four years regardless of who is in office. Now
that abortion rights are no longer at risk, her motivation and need to vote for a prochoice/anti-environmental candidate subsides.
Rational choice theory provides another explanation for why ambivalence may
lead to abstention. Simply stated, rational choice theory asserts that a person will vote
for the candidate who provides the voter with the greatest expected utility (e.g. Downs
1957). Consider an individual who has both three reasons to support and also three
reasons to oppose each candidate (the reasons for and against are different for the two
candidates) and further assume that this person gives equal weight to each consideration.
While recognizing that a different set of positive and negative consequences will result
depending on which candidate is elected, a “rational” person also realizes that he or she
is ultimately indifferent to which set of outcomes occurs (see Downs 1957, chapter 14
for a further discussion on why rational indifference should lead to abstention). The
expected utility from each candidate for the voter is zero and thus so is the difference in
44
the two expected utilities, making the person indifferent to the two choices. Thus, one
can liken decision ambivalence to the differential in expected utilities; the smaller the
differential, the greater the decision ambivalence one will feel. As decision ambivalence
increases, individuals may be more likely to decide not to vote for either candidate as the
comparative advantage one candidate has over the other in terms of expected utility
decreases.
The limitation with the rational choice approach is that it boils the entire decision
down to a person’s expected utility and does not account for (nor is it concerned) with
how those utilities are derived. For a rational choice theorist, so long as two candidates
have the same expected utility, then the voter should have the same motivations to vote
(or not vote) for either candidate. But this need not be the case. Consider, for instance,
a person who views two candidates via the following expected utility formulas:
(1) U1 = 3X1 + 3X2 + 3X3 – 3X4 – 3X5 – 3X6 = 0
(2) U2 = 1X1 + 1X2 + 1X3 – 1X4 – 1X5 – 1X6 = 0
In each equation, the X values indicate the six issues or dimensions this person uses to
assess the two candidates in the election; the values attached to each X indicates the
expected utility of that issue for this voter. Equation one indicates that the voter gains
nine “units” from candidate one (three each on the first three issues) and loses nine
“units” over the next three issues. This voter would gain but also lose three units from
the second candidate. In other words, the voter derives identical expected utilities for
the two candidates, but the degrees of loss and gain differ. While this voter does not
expect any net gain or loss with either candidate, he sees that he clearly loses big and
wins big with candidate one on certain issues while he loses small and wins small on
45
these same issues with candidate two. This voter is more ambivalent towards the first
than the second candidate, as he experiences a greater pull in the positive and negative
directions from the first than from the second candidate. It is reasonable to believe that a
voter facing a number of large losses and gains will react differently than does a voter
who confronts a candidate who yields only small losses and gains, even if the expected
utilities are the same. Thus, it becomes problematic (or at least limiting) to treat all
expected utilities with the same value as likely to have the same impact on behavior.
What matters is not just the final value but also its derivation.
I do not cite this problem to illustrate a flaw in rational choice theory, but to
indicate how the social psychological concept of ambivalence can complement rational
choice theories of voting behavior. Before arranging this marriage, however, one must
recognize the difference in how the two approaches define the concept of indifference.
A rational choice perspective states that indifference occurs when the expected utilities
from the options available are equal (Downs 1957, 261). For Downs, it does not matter
if the expected utility from each option is extremely high, zero, or somewhere in
between; it only matters that the sum of the two candidate utilities are equal. For the
social psychologist, indifference occurs only when a person holds no positive or
negative beliefs whatsoever (see Kaplan 1972 for a discussion from psychology on
indifference versus ambivalence). The concept of ambivalence, then, can complement
rational choice theories by presenting a way to distinguish among types of “rational”
indifference. When someone is described as experiencing a high level of ambivalence
about a candidate, this is the same as saying one’s “rational” indifference is the product
of large positive expected utility on certain dimensions and large negative expected
46
utility on other dimensions. Psychological and rational indifference, therefore, are only
equivalent when an individual has no considerations about the candidate. By accounting
for these differences in why people are indifferent, we can better assess their political
behavior.
My investigation of ambivalence covers one last matter. So far in my
discussion, I have considered voters whose choice is between a Republican and a
Democratic candidate. What happens, however, when the choice expands to include a
third-party candidate? One must keep in mind that ambivalent people are not, by
definition, the same as apathetic or disinterested voters. To be ambivalent one must
have at least some reason to support and to oppose a candidate, thereby indicating some
level of engagement with or interest in the candidates. The more ambivalence one
experiences, the more competing considerations one must resolve. This suggests that if
ambivalence does in fact lead individuals to withdraw from elections, they do not do so
because they deem elections unimportant, but because they see no satisfactory outlet for
their own preferences in an election. That is, while conflicted voters may feel motivated
to stay at home when they must choose between the two party candidates, this need not
be the case if they are given another means by which to voice their concerns at the ballot
box.
Such a scenario should have the greatest impact on the behavior of the
decisionally ambivalent. These people have a large number of competing considerations
about both candidates; hence, they are unlikely to prefer staying at home on Election
Day. More plausibly, they do want to participate in electoral politics but are stymied by
their inability to choose between the Democratic and Republican candidate. The
47
presence of third-party candidates on the ballot would give these people an opening to
resolve their ambivalence by throwing their support behind one of these challengers.
There are a couple of reasons to think that this behavior does in fact occur. First, it is
possible that the voter conflicted over the party candidates finds an independent
candidate whose policies and positions better harmonize with the voter'
s own
preferences. As mentioned earlier, Lacy and Burden (1999) show that a number of
people turned out to vote for Perot because they saw him as a better choice than Bush or
Clinton. It is also possible, however, that the decisionally ambivalent voter would throw
his or her support behind an independent candidate merely as a protest vote against the
two party candidates. These voters may neither expect the independent candidate to win
nor fully agree with the candidate’s platform, but they want to stay vested in the process
and register their dissatisfaction. Whatever their rationale, there are number of reasons
why decision ambivalence could translate into support for an independent challenger in
an election.
By their very nature political campaigns are filled with strife and conflict. Each
candidate seeks to defeat his opponent and win office. Throughout the campaign
season, the public learns a number of things, both good and bad, about each candidate;
consequently a number of people develop conflicted attitudes about the candidates.
People who are pulled in multiple directions (ambivalence) are likely to respond
differently to their world than are those who are pulled in one direction (preference), or
those who are not pulled in any direction at all (indifference). While many people
ultimately resolve this conflict and cast their vote for one or the other candidate, others
instead opt, for any of several reasons, not to vote at all.
48
Conclusion
The study of ambivalence has an importance that goes beyond its appeal to
academic curiosity. The matters I have discussed suggest that ambivalence plays a
variety of normative and substantive roles in electoral politics. First, consider the
positive relationship found between ambivalence and accuracy. This might indicate a
greater level of involvement and diligence by the ambivalent voter, although it could
also simply be an artifact of processing information in particular ways, with none of the
processes being better than the others. If the former is true, and ambivalent individuals
are more likely to think harder and more critically, then this would suggest a
normatively better process of candidate evaluations. However, if the latter is true, and
ambivalence merely leads to a different type of processing, such as memory-based
processing, then the accuracy finding is merely the result of tests that are biased to favor
the ambivalent. From this perspective, the ambivalent and non-ambivalent are basing
their judgment on the same information, but differing on how they are storing and
retrieving this information later in the campaign. Tests that reveal such differences in
processing do not, however, tell us anything about the quality of candidate judgments.
Studies of on-line processing consistently inform us that recollection of candidate
specific behavior is not a requisite for one’s ability to support a candidate in accordance
with one’s own preferences. Conversely, the ability to recall specific information should
not be touted as an indicator of greater ability to select the correct candidate.
Along these lines, we should be equally cautious when lauding systematic
processing as a normatively positive virtue, when it may likewise be a by-product of
different, not better, cognitive behavior. Ambivalent people, by definition, must grapple
49
with their internally conflicted thoughts when evaluating an attitude object. We can
hardly be surprised that they spend more time thinking about the object, weigh the pros
and cons, and consider the tradeoffs involved across dimensions before rendering a
judgment. In contrast, a person who faces a set of univalent considerations about an
attitude object is unlikely to spend much time thinking about the various tradeoffs before
evaluating the object, as there simply is no need. This does not mean, however, that the
former person is making the better judgment or that the latter person is prone to
mistakes. The first person is simply making a different judgment because this person is
faced with different information. Clearly, these questions need to be resolved before we
can assert that ambivalence promotes the development of better democratic citizens.
There are equally important normative questions about the relationship between
ambivalence and electoral participation. To be ambivalent, one must have a set of
competing considerations about the candidates, which indicates a certain degree of
interest or involvement in the campaign. Moreover, if it is discovered that ambivalence
causes information-seeking behavior, then this would indicate an even deeper
connection with the campaign. Even if ambivalence does not cause information-seeking
behavior it is evident that the ambivalent are not apathetic or indifferent to the electoral
process. By definition, they have a host of thoughts on the candidates and it is likely
that they want to participate in our elections, but fail to do so out of dissatisfaction with
the options on the ballot.
It is always worrisome when we find a group among the electorate that
systematically refrains from participating in our elections. Scholars have rightfully
devoted much attention to how well certain demographic groups (e.g., women,
50
minorities, etc.) are represented in our elections, but there are also many reasons to
suspect that ambivalent individuals may opt to stay home on Election Day. Clearly, the
loss of the ambivalent voter is a troubling scenario; it suggests that there is a segment of
the population that is actively engaged in the electoral process in general, but that then
fails to participate in the most crucial aspect of the campaign.
The study of ambivalence is important for a variety of reasons, ranging from
satisfying scholarly curiosity about political behaviors to addressing a number of
substantively important electoral implications. At this point, it is safe to say that
ambivalence certainly does matter in explaining political behavior, but how it matters is
still unclear. Some evidence suggests ambivalence leads voters to behave as “better”
democratic citizens, actively seeking out information and critically assessing the
candidates. A set of competing arguments suggest, however, that ambivalence merely
causes voters to engage in different behaviors, ones that are no better or worse than
those of the non-ambivalent. Plainly, we need a more systematic investigation of the
effects of ambivalence. This not only will provide us with a better understanding of
what influence ambivalence has on political behaviors, but will enable us to better
understand the normative implications of these influences.
51
CHAPTER 2
THE CAUSAL IMPLICATIONS OF AMBIVALENT ATTITUDES:
EXPERIMENTAL ANALYSIS
In the previous chapter I argue that there are a number of reasons why
ambivalence might cause both information-seeking behavior and an increased
likelihood of abstaining in an election. It is important to document such relationships,
as the current evidence is at best suggestive that such behaviors occur. There are,
however, other competing explanations that must first be ruled out before making any
firm claims. It is therefore important to refine what we know about the causal
influences of ambivalence on electoral behavior. In this chapter I employ
experimental analysis that examines the causal effects of ambivalent attitudes about
political candidates. Overall, this chapter advances the study of ambivalence in four
important ways. First, it simultaneously pits information-seeking explanations
against other competing hypotheses, namely that ambivalence causes memory-based
processing. Second, it documents the relationship between ambivalence and
abstention decisions. Third, it examines the effects of decision ambivalence as well
as candidate ambivalence; decision ambivalence is a form of ambivalence that
heretofore has received scant attention.
52
The final, and perhaps most notable, benefit of this study is that it provides a
methodological innovation for studying ambivalence. Specifically, this is the first
study in political science to my awareness that explicitly manipulates the state of
ambivalence itself when examining its effects. This allows me to determine the
causal impact of ambivalence by eliminating potential confounds in a manner that has
not been possible in other studies of ambivalence. The primary benefit of
experimental analysis is that it allows one to control for a variety of factors while
simultaneously manipulating the value of another factor in order to assess causal
relationships. Existing experimental studies on ambivalence have not actively
manipulated ambivalence. Instead, these studies vary factors such as the amount or
type of information a person receives about an attitude object in hopes of
manipulating levels of ambivalence across subjects. Another approach that has been
used is to rely on attitude objects that the experimenter believes subjects will view
ambivalently. In either case, subjects are presented some information and are then
asked to report their feelings of ambivalence about the object. The limitation of this
approach is that it rests on the hope that the variance in recorded ambivalence across
subjects is a function of the manipulation. The experimenter, however, cannot firmly
predict which subjects will experience greater feelings of ambivalence in response to
the stimuli. This in turn makes it more difficult to draw firm causal inferences about
the role of ambivalence.
The main problem with such approaches is that they potentially conflate the
effects of ambivalence with some other factors, such as personality traits, that may
also covary with ambivalence. For example, it might be the case that people with
53
higher levels of political sophistication are more likely both to develop ambivalent
attitudes and to seek information about the object. If so, then any positive
relationship one finds between ambivalence and information seeking behavior may
simply be picking up the effects of sophistication. In other words, while people with
high levels of ambivalence in these studies may behave differently than do people
with low levels of ambivalence, one cannot definitely state that ambivalence is the
cause of this difference. When conducting experimental analysis, it is important not
only that there is variance in the concept of interest, but to understand why this
variance occurs. Therefore, scholars that cannot verify that their manipulation causes
increased feelings of ambivalence are unable to make causal claims about the role of
ambivalence for political behavior. It is impossible to differentiate whether
ambivalence is causing the observed differences in behavior or whether both
ambivalence and these differences are caused by some other factor. Of course, one
can attempt to control statistically for these potential confounds, but the resulting
analysis is more akin to a survey design than to true experimental analysis. Although
this does help rule out some possible spurious correlates, it does not enable us to
make causal claims. The fundamental advantage of experimental analysis exists only
so long as we can confidently assume that our manipulations cause changes in our
variable of interest (i.e. ambivalence). Without the ability to make this assumption,
we can only speculate on causation.
My experiment overcomes this limitation by conditioning the candidate
descriptions on each subject’s own issue preferences. Specifically, I manipulate the
extent to which the candidate’s position on the issues agrees or disagrees with each
54
subject’s own positions on the issues (details are provided below in my discussion of
the experimental design). This allows me to determine and control which subjects
will experience more or less ambivalence about the two candidates. So long as I am
confident that subjects in the ambivalent condition experience more ambivalence than
subjects in the non-ambivalent conditions, I can rule out competing hypotheses and
make causal claims about the relationship between ambivalence and my dependent
variables.
Experimental Design
Participants in this study were 162 undergraduate students in political science
courses who received extra credit for their time. Subjects completed the experiment
on computers and are informed that the purpose of the study is to examine how
people form political attitudes. They started the study by answering some
demographic questions and were then asked to provide their opinions on eight
political issues.7 Subjects are required to state whether they support or oppose each
issue; they are not given the option of responding don’t know or no opinion. After
reporting which side of the issue they fall on, they were then allowed to state whether
they somewhat, moderately, or strongly hold that position. For example, once a
subject has declared a pro-choice/life attitude, this person is then asked if she is
somewhat/moderately/strongly pro-choice/life.
Once issue preferences have been recorded, I ask respondents a series of
questions about their media use, past voting behavior, need for cognition and need for
7
Specifically, the eight issues are: school vouchers, environmental regulations, government funded
health insurance, abortion, expansion of the Patriot Act, affirmative action, tax cuts, and gay civil
unions.
55
evaluation. The primary purpose of including these questions at this stage is to focus
the subject’s cognitive energies on something other then the issues they were
previously asked about. In short, they serve as a distracter task. Next, subjects were
presented information about six issue positions of two candidates running for the
United States Senate in the state of Oregon. Although the candidates were fictional,
students were informed that these were real people running for office in 2004.8 The
information is presented in two columns, where biographical information is placed on
the top of each column and the issue positions are provided in the six rows below;
each row provides the information on the same issue for both candidates. For
example, the first row states whether each candidate is for or against school vouchers.
Each candidate is given a similar background; both are male, married with children,
hold advanced degrees and have similar work experience (see appendix for full
description of candidate information). Finally, a picture of each candidate is provided
along with the information so that subjects can put a face with each name. This
picture is also provided each time the subjects are asked a question about the
candidate in order to help them remember which candidate they are being asked
about.9
Using the subject’s own opinions on the issues, I manipulate the extent to
which each candidate takes positions that agree or disagree with the subject’s own
preferences on the issues. If the subject is assigned to a condition in which she is to
8
After the students complete the study they are informed that the candidates running for office are
fictional.
9
The pictures are of actual Florida state representatives and both men are in fact born within a few
years of each other. While every attempt was made to select two people who look similar, it is
possible that one of the candidates is more physically appealing to the subjects. Therefore, the pictures
are randomly assigned to the two columns.
56
view the candidate ambivalently, then the candidate agrees with the subject on three
issues and disagrees on three issues. If a subject is assigned to view the candidate
favorably, then she is given a candidate that agrees with her on five issues and
disagrees with her on one issue, and vice-versa for the unfavorable candidate
condition. Ultimately, subjects are assigned to one of four conditions, each
representing a different type of election: (1) ambivalent candidate v. ambivalent
candidate, (2) ambivalent v. good, (3) good v. good and (4) ambivalent v. bad.10
Combined, these elections are designed to create different levels of both candidate
and decision ambivalence for the subjects in this study. For instance, in conditions 1,
2, and 4 the subjects should experience significantly greater feelings of candidate
ambivalence towards one of the candidates. The extent to which the two candidates
are similar should generate feelings of conflict about one’s vote choice or, in other
words, it should create decision ambivalence. Thus, subjects should experience
higher feelings of decision ambivalence in the first and third conditions.
After familiarizing themselves with the information about the two candidates,
subjects are asked a variety of questions about their attitudes towards both candidates.
This information is used to generate various measures of ambivalence (a more
detailed discussion of these questions is provided below).11 They are then asked a
series of questions about trust in government, efficacy, and sophistication that serve
10
For conditions 2 and 4, random assignment determined which of the candidates is presented as the
ambivalent candidate. For example, in condition 2, the candidate in the left column is the ambivalent
candidate in half of the cases and is presented as the good candidate in the other half. This is done to
control for any possible bias subjects might have for focusing on information presented in the left of
right column. That is, it is possible that subjects will naturally read more items in left column.
Therefore, if the ambivalent candidate was always presented in the left column, then this might inject
bias into my results.
11
The order in which the questions about the candidates are presented is randomized so that half the
time they are asked about Powell first, and half the time about Sullivan first.
57
two purposes beyond merely collecting this information. First, they help simulate the
passage of time that occurs during a campaign from the time a voter encounters
information to when one votes in an election. Second, and related, it serves as a
distracter task that focuses the subject’s cognitive energies to other thoughts than
those directly related to the candidates. As in a real campaign, subjects get
information about the candidates, turn their attention to other affairs, and then call
upon this information again at a later time. After completing these questions, they are
asked to place each candidate on a feeling thermometer and to recall the issue
positions of each candidate.
In the final stage of the experiment subjects are again provided the issue
information about the two candidates in the same format as before, but with two
exceptions. First, subjects now have the opportunity to read brief statements on the
issues by the two candidates. For instance, if Sullivan favors school vouchers, then
the subject can click on the statement that says “James Sullivan supports school
vouchers” and a new window will pop-up on the screen that provides a brief
statement by Sullivan on the school voucher issue. If both candidates support
vouchers, then each candidate provides a different pro-voucher statement on the
issue, but with the same underlying theme. In this case, both candidates support
vouchers because they want to give parents greater choice in their children’s
education, but use different language to convey this message. The second difference
is that half of the subjects are provided information on two additional issues, tax-cuts
and gay civil unions, to see if ambivalent voters are more likely to seek new
information. Subjects are free to read as many, or as few, of these statements as they
58
like and can go back and reread an issue statement as often as they want. When
subjects are finished getting information about the candidates, they are then taken to
the “voting booth.” At this point they are asked both about the likelihood that they
would vote in an election between these two candidates. Finally, they are asked
which candidate they would support in an election if they did vote, including an
option that they would not vote in the election at all.
Ambivalence
Ambivalence occurs when an individual simultaneously maintains a set of
both positive and negative considerations about an attitude object, such as a candidate
for the Senate. There is a growing awareness that ambivalence itself is not a
monolithic concept and that it is useful to distinguish between two types of
ambivalence – subjective and objective ambivalence. Subjective ambivalence is
easily measured by simply asking people to state the extent that they have conflicted
feelings about the attitude object. Therefore, I ask subjects the extent to which they
agree or disagree with the statement, “I have mixed feelings about Powell/Sullivan.”
I measure subjective decision ambivalence with the following question:
Please take a moment to consider both candidates. Now think about which of
the two candidates you would be most likely to vote for in an election. Once you have
decided which candidate you are likely to support, please place yourself on the
following scale:
I had no trouble deciding which candidate to support in an election.
I was a little conflicted about deciding which candidate to support in an
election.
I was somewhat conflicted about deciding which candidate to support in an
election.
59
I was very conflicted about deciding which candidate to support in an
election.
Objective ambivalence is measured by asking respondents to report separately
their negative and positive feelings about each candidate. I use two different, but
similar, questions to tap each subject’s positive and negative feelings. First, I tell
respondents to:
Please think about Powell/Sullivan as a candidate. Consider only your
negative feelings towards him, ignoring any positive thoughts you may have about
him, and place yourself on the following scale:
I have no negative/positive feelings towards Powell/Sullivan.
I have a few negative/positive feelings towards Powell/Sullivan.
I have some negative/positive feelings towards Powell/Sullivan.
I have a lot of negative/positive feelings towards Powell/Sullivan.
As a second means of assessing positive and negative considerations, I also ask
subjects the extent to which they agree or disagree with the statement: “There are
many good reasons why I would support/oppose Powell/Sullivan for the U.S.
Senate.” The correlations between both the positive and support questions and the
negative and oppose questions all fall around .5 for each candidate. This indicates
that the different measures of positive and negative considerations are in fact
reasonably correlated together.
While positive and negative feelings are the two components of ambivalence,
these considerations must ultimately be assimilated into a single measure of
ambivalence. Any measure of ambivalence must account for both the intensity of
one’s feelings as well as the similarity of one’s positive and negative considerations.
That is, an individual who has a high degree of positive and negative feelings about a
60
candidate should be viewed as more ambivalent than a person who simply has a small
amount of positive and negative feelings about the candidate. As discussed in the
previous chapter, there is an emerging literature that discusses a number of ways in
which ambivalence can be measured (Newby-Clark, McGregor, and Zanna 2002;
Priester and Petty 1996; Thompson, Zanna, and Griffin 1995). After reviewing
several of these options Thompson, Zanna, and Griffin (1995) assert that the Griffin
measure is the best. Specifically, the Griffin measure is:
AmbivalenceCandidate =
P+N
− P−N ,
2
where P and N refer to the level of positive and negative feelings one has about the
candidate. The first term in the equation captures the total intensity of one’s
considerations and the second term controls for the similarity in the number of
negative and positive considerations. I use this formula to generate two separate
measures of objective ambivalence for each candidate – one for the positive/negative
questions and one for the support/oppose questions. The two Powell and Sullivan
measures of ambivalence correlate moderately well together with values of .47 and
.34 respectively. I therefore calculate the mean of the two ambivalence scores for
each candidate and use this as my measure of objective ambivalence in the analysis
below.
Just as individuals may be conflicted in their assessments of individual
candidates, they may also be conflicted about their vote decision. Lavine (2001)
modifies the Griffin formula to measure how conflicted one is in selecting between
the two major party candidates in an election, namely:
61
Ambivalence Decision =
P1 + P2 + N 1 + N 2
− [ P1 − P2 + N 1 − N 2
4
]
where P1, P2, N1 and N2 indicates the number of positive and negative feelings for the
two candidates. In this equation the first term again captures the intensity of
considerations felt towards the two candidates and the second term accounts for the
similarity in these considerations. Combined this measure indicates the extent to
which one is pulled in different directions when comparatively assessing the two
candidates, where higher scores indicate higher levels of decision ambivalence.
Of course, in order to be confident in any causal claims I make about
ambivalence it is imperative that my manipulations do in fact generate higher levels
of ambivalence. For a simple manipulation check, I run a series of OLS regression
models with each ambivalence measure as a dependent variable. Each model
includes three dummy variables that indicate the conditions in which each type of
ambivalence should increase. For instance, the variables “Powell condition” and
“Sullivan condition” equal one when the subject is in a condition in which Powell and
Sullivan are presented as the ambivalent candidate, respectively. The “decision
condition” variable equals one for those conditions in which I expect higher levels of
decision ambivalence, namely when the two candidates are either both in the
ambivalent or favorable condition.
For each dependent variable I examine two models, one that includes only the
three dummy variables, and another that controls for other individual traits that might
also influence the development of ambivalent attitudes. In regards to the latter, I use
a dummy variable to control for females and non-whites. I also include the
62
respondent’s age, class standing (e.g. freshman, sophomore, etc.) and a four-point
folded party identification measure to assess strength of partisanship. Finally, I
include indices each for trust in government (.66), efficacy (.52), media use (.74),
sophistication (.64), need for cognition (.79), and need for evaluation (.51), where the
numbers in parentheses indicate the alpha score for each index.12 The results are
presented in Tables 2.1 to 2.3.
I find that the experimental manipulations are positively and significantly
related to ambivalence in every model. My manipulations, on average, increased
feelings of subjective ambivalence by about a half point in each model. Similarly,
objective ambivalence also increased as a result of my manipulations. On average,
the manipulations increased feelings of objective candidate ambivalence by about .4
points and objective decision ambivalence by about .75 points.
These results are virtually unchanged when the individual traits variables are
added to the models. The addition of the individual trait variables, however, does
notably increase the explanatory power of each model. For instance, the adjusted R2
increases from roughly .05 in the simple model to .18 in the full model when
predicting subjective Powell ambivalence, as the education, minority, sophistication,
need for evaluation and partisan strength variables are all significant influences.
Overall, there is largely no general pattern of results for the control variables, except
that partisan strength consistently reduces ambivalent scores. While one might expect
strong partisans to be less ambivalent in an actual election, as they are firmly attached
12
See data appendix for information on the specific questions used in these indices.
63
to one of the candidates, it is somewhat surprising to see this effect in this study.13
The party identification of the candidates was not provided, thereby precluding one
from using the party heuristic as a means of alleviating any possible conflicted
feelings about the candidates. For whatever reason, strong partisans apparently find it
easier to assess a candidate either positively or negatively.14 So while individual
traits do affect whether one will develop ambivalent attitudes, my manipulations
generate increased feelings of ambivalence that are independent of the impact of
personal characteristics.
In sum, these findings indicate that ambivalence about a candidate is not
solely driven by characteristics of a candidate alone. The extent to which a person
feels ambivalent about a candidate also varies based upon that individual’s own
characteristics, most notably strength of partisanship. Thus, while my manipulations
do significantly explain some of the variance in ambivalent attitudes for the subjects
in my study, other factors are also influential. Therefore, rather than use the
condition variables in the analysis below, I use the direct measures of objective and
subjective ambivalence. There are two main reasons for making this choice.
13
Party identification is not included in the model as there is no reason to believe that partisans should
be more or less likely to view the candidates ambivalently. While not shown here, I did estimate a
model that includes dummy variables for both Democratic and Republican identifiers and none of
these variables are significant in any of the models. Simply stated, party identification did not affect
feelings of ambivalence.
14
One possible explanation is that strong partisans are more likely to hold strong positions on the
issues and/or are more likely to see certain issues as party issues. Using this information, they may
feel more confident in inferring the party identification of the candidates. For instance, if one learns
that a candidate favors vouchers, then a subject might logically conclude that this candidate is probably
a Republican. Drawing upon this information, this subject has a much easier time forming a positive
or negative evaluation of the candidate, depending on the party identification of the subject him or
herself.
64
First, ambivalence is a complex concept that cannot simply be measured via a
trait present-trait absent dichotomous variable. While I can manipulate the extent to
which a person agrees or disagrees with the candidate’s issue positions, I cannot
ensure that this generates precisely the same level of favorable, unfavorable, or
ambivalence for each person in the same experimental condition. As a result, the use
of the ambivalence condition dummy variables is unlikely to adequately measure the
subtle differences in feelings of ambivalence across individuals. Simply stated, a
complex concept necessitates the use of measures that can pick up subtle differences.
Second, I draw upon the direct measures in order to compare the effects of objective
and subjective ambivalence. As noted, there is a growing appreciation that these are
two separate concepts, each with the potential to exert its own unique influence on
behavior. It is therefore important to distinguish between these two types of
ambivalence to see whether they affect behavior in different ways. Ultimately, the
use of these direct measures of ambivalence means that not everyone who
experiences high levels of ambivalence in my study does so as a result of my
manipulations. Nonetheless, my manipulations clearly do exert a significant and
sizable increase in feelings of ambivalence, thereby indicating that any relationships I
find between ambivalence and my dependent variables is the result of a causal effect
of ambivalence.
Information-Seeking Behavior: Measurement and Expectations
By recording the searching behavior of my subjects I am able to generate a
number of dependent variables to determine the relationship between ambivalence
and information-seeking behavior. The first question I explore is simply whether
65
ambivalence fosters a general motivation for more information. The dependent
variable in this analysis is a count of the total number of times a person clicked on
any issue statement, regardless of candidate. For instance, if a person read four
statements about Sullivan and five about Powell, then this person’s score on this
measure would be nine. The second variable I examine is the total number of times a
person seeks additional information about a specific candidate. In the above example,
this person would have a score of four and five for Sullivan and Powel respectively.
The first four hypotheses I test are:
H1: Candidate ambivalence is positively related to the total number of searches.
H2: Decision ambivalence is positively related to total number of searches.
H3: Ambivalence about a candidate is positively related to the total number of
searches about that candidate.
H4: Decision ambivalence is positively related to the total number of searches about a
specific candidate.
While these are the most basic types of information-seeking behavior one can
engage in, it is possible that ambivalent voters may seek specific types of information
or search for information in a different manner than do those who are not ambivalent.
It is also important to test for the presence of these other forms of behavior. First, I
explore whether ambivalent individuals are more likely to seek new information
about the candidates. After developing an ambivalent attitude about a candidate, a
voter may desire new information about the candidate, perhaps as means of “breaking
the cognitive tie” this person faces. For this analysis I draw on the experimental
manipulation in which half of the subjects are given information about each
candidate’s position on two new issues. I again count the number of times a subject
reads one of these statements and predict the following:
66
H5: Candidate ambivalence is positively related to reading the 2 new issue statements
by that candidate.
H6: Decision ambivalence is positively related to reading the 2 new issue statements
by a candidate.
Another way that ambivalent voters may differ from the non-ambivalent
voters is by focusing on those issues on which they either agree or disagree with the
candidate. From a variety of perspectives, there are reasons to believe that
ambivalent voters may be more concerned about the negative aspects of one’s
candidacy rather than the positive ones. First, Cacioppo and Bernston (1994) argue
that, all else equal, individuals give their negative considerations more weight than
they give their positive considerations when making an evaluation. From a different
perspective, Shepsle (1972) uses a formal model and asserts that voters are more
likely to be risk averse than risk acceptant during an election. Both approaches
suggest that voters may focus on those aspects of a candidate that will put them in the
domain of losses, namely those policies in which the voter and candidate disagree.
Thus, when voters are ambivalent about a candidate and must deal with both positive
and negative beliefs, they will focus on the areas of issue disagreement. In doing so,
they can better determine the magnitude of their losses if the candidate is victorious.
This motivation towards negative information, however, is likely to be
attenuated by the characteristics of one’s opponent. Voters, in general, may want to
acquire information that reinforces their initial preference; they want information that
will assure them that they have made the correct choice. If true, then voters will seek
out positive information on a candidate when that candidate is viewed more favorably
than the opponent. For example, when a voter confronts an election in which an
67
ambivalent candidate competes with an unfavorable candidate, she is motivated to
seek positive information about the ambivalent candidate. Conversely, if the election
matches an ambivalent candidate against a favorable candidate, this same voter is
now motivated to seek negative information about the ambivalent candidate. Thus,
while viewing a candidate ambivalently may affect one’s general motivation to seek
out positive or negative information about a candidate, this process might be
attenuated by how the voter views the opposing candidate. I generate two dependent
variables to examine this relationship. The first variable records the number of times
a subject reads a statement on an issue that he or she agrees with the candidate on and
another that records the number of times a subject reads a statement that he or she
disagrees with the candidate on. Specifically, I expect:
H7: Candidate and decision ambivalence are positively related to seeking negative
information about a candidate
H8: Subjects are more likely to seek positive information about an ambivalent
candidate when the opposing candidate is viewed unfavorably.
H9: Subjects are more likely to seek negative information about an ambivalent
candidate when the opposing candidate is viewed favorably.
Altogether, the above analysis will test whether ambivalence generally leads
to greater information-seeking behavior. Moreover, it will further examine whether
ambivalent individuals are more likely to engage in certain types of informationseeking behavior. Such analysis is important, as the relationship between
ambivalence and a motivation to seek out information has yet to be explicitly tested,
despite the findings from several studies that suggest the plausibility of such a link.
Nonetheless, the acquisition of new information is not the only way to explain these
68
findings and the information-seeking hypothesis must be pitted against other potential
explanations.
Measurement and Expectations: Memory-Based v. On-line Processing
Although there is suggestive evidence of a causal link between ambivalence
and information-seeking behavior, the same evidence is equally suggestive that
ambivalence causes different information-processing behavior. Specifically, it is
possible that ambivalent voters are more likely to evaluate candidates via memorybased instead of on-line processing. If true, then ambivalence could be positively
related to candidate accuracy or systematic processing even if ambivalent voters are
no more likely to seek out additional information. From this perspective, these
relationships are not driven by ambivalent voters acquiring more information, but
rather that they are more likely to retain the specific bits of information they
encounter during a campaign. Consequently, the ambivalent are more likely to recall
candidate specifics, such as issue positions, when asked to provide this information at
a later time. The non-ambivalent, conversely, have a harder time answering such
questions because this information has been discarded from working memory once
they have updated their on-line tally. It is not the case that the non-ambivalent
encounter or seek less information than the ambivalent; instead they simply do not
retain specific information for later use. Given this possible linkage, it is also
important to test whether ambivalence leads people to process and store information
differently than do those who are non-ambivalent.
I draw upon two different tests to examine the relationship between
ambivalence and memory-based processing. First, I calculate a simple index of the
69
number of issues a subject correctly recalls about a candidate. The values of this
index range from zero (correctly identifying none of the candidate’s positions) to six
(correctly identifying all of the candidate’s positions). Individuals who engage in
memory-based processing should be more likely to correctly recall a candidate’s issue
positions, as they are more likely to retain specific pieces of information about the
candidate. Conversely, individuals who use on-line processing will use the issue
information to form an overall evaluation of the candidate, but then have a harder
time recalling the specific information. Therefore, if ambivalence is positively
related to recalling the correct issue positions of the candidates, then this suggests that
ambivalent individuals are more likely to engage in memory-based processing.
Formally, I expect:
H10: If ambivalence promotes better recall of information encountered, then
ambivalence will be positively related to the number of issues a subject
correctly recalls.
The dependent variable for my second test is the amount of time it takes a
subject to respond to the feeling thermometer questions. One of the products of
memory-based processing is an increased reaction time to evaluation questions
(Steenbergen and Ellis 2003). People who draw upon specific bits information stored
in memory at the time of judgment should, on average, take longer to respond to
questions than do people who simply need to retrieve a summary evaluation of the
candidate. Therefore, response times can be used to test whether ambivalence causes
memory-based processing, namely:
H11: If ambivalence promotes memory-based processing, then ambivalence will be
positively related to response times.
70
Combined, these tests enable me to determine whether ambivalent individuals
process information differently than do non-ambivalent voters. More importantly, this
analysis, coupled with that from the information-seeking behavior analysis, will provide
a set of tests that pit the information-seeking and information-processing hypotheses
against each other.
Results: Searching Behavior
Before turning to the analysis, a methodological issue about my data must first
be addressed. First, while all subjects had the opportunity to read candidate
statements, many subjects simply opted not to read any statements at all, specifically
80 of the 162 subjects in my study (49.4%) did not read a single statement.
Consequently, the distribution of the candidate search variables is highly skewed by
this large number of zero values (see data appendix for more details on the
distribution of the search variables). The use of OLS regression or likelihood
techniques such as ordered logit or probit on such data are inappropriate, as they
might provide both incorrect coefficients and standard errors. In other words, the use
of such techniques can yield inaccurate results about the direction and significance of
the independent variables. To account for this problem, I treat the decision to read an
issue as an event and use a negative binomial estimation procedure to model my
results.
I analyze each of my independent variables with three different models,
starting with the simplest model and then increase its complexity. The first model
includes only the two candidate ambivalence measures as well as the measure of
decision ambivalence. In the second model, I control for context effects and dummy
71
variables for the different election types. Finally, to account for the effects of
individual traits, I include a set of variables that measure the individual characteristics
of my subjects. Specifically, I include the same variables used in the manipulation
check models: sex, age, education, race, partisan strength and indices for trust in
government, efficacy, sophistication, need for cognition and need for evaluation.
While these models serve as the baseline for my analysis, it will be necessary to
deviate from this template in some instances. Any changes will be noted where
appropriate.
The first question I examine is whether ambivalence promotes general
information-seeking behavior; the dependent variable is the total number of times a
person reads any statement by a candidate. Along with the baseline measure
mentioned above, I include a dummy variable in each model for subjects who are
presented two additional issues when given the opportunity to read the candidate
statements. This is used to control for the fact that subjects who are presented with
eight issues may be more likely to search for more information than are subjects who
are presented with six issues. The results for total searches are presented in Table 2.4.
The first column examines the effect of subjective candidate and decision
ambivalence on total searches. These results indicate that subjective ambivalence
about Powell decreases the likelihood of seeking any information, while subjective
decision ambivalence increases the likelihood of reading candidate statements.
Finally, the next two columns add the election dummy and personal trait variables to
the model. Among the control variables, only sophistication attains significance and,
as expected, shows that sophistication is positively related to the number of
72
statements a subject reads about the two candidates. Overall, the relationship
between the ambivalence measures and total searches remains largely the same in
these larger models, though the decision ambivalence measures ceases to be
significant in the full model. The last three columns examine the relationship
between objective ambivalence and total searches. As with subjective ambivalence,
these models also reveal a negative relationship between Powell ambivalence and
total searches and a positive effect of sophistication. In contrast to subjective
decision ambivalence, objective decision ambivalence does not significantly
influence one’s propensity to read candidate statements.
While the above coefficients indicate whether ambivalence is positively or
negatively related to candidate searches, it is impossible to directly interpret the
substantive impact of negative binomial coefficients. Therefore, I examine the effects
of ambivalence by calculating a series of predicted probabilities. Specifically, I set
the value of one of the ambivalence measures to either its minimum or maximum
value and then set the values of the other two measures at their mean. I subtract the
predicted probability from the minimum ambivalence model from the results from
maximum ambivalence model and graph the results (see Figure 2.1). Positive values
indicate that ambivalence makes the outcome more likely and negative values mean
ambivalence makes the outcome less likely.15 The top graph shows the effects of
changes in Sullivan ambivalence, the middle graph Powell ambivalence, and the
bottom graph shows the effects of decision ambivalence.
15
Although some subjects searched for more than 10 statements, the difference in probabilities for the
two types of subjects is generally less than .005 for predicting greater than 10 searches. Therefore, I
opt to display only the probabilities for reading the first 10 statements. All the predicted probabilities
in this chapter are generated in Stata via J. Scot Long and Jeremy Freese’s (2003) SPOST program.
73
Overall, these graphs reveal two general trends. First, candidate ambivalence,
whether subjective and objective, largely decreases the likelihood that one searches
for additional information. Second, for all three types of ambivalence, the biggest
change in probability is concentrated on the likelihood that one searches for any
information at all – the probability that one reads zero statements. For instance, a
person who experiences maximum subjective ambivalence about Powell has roughly
a .13 greater likelihood of not reading any candidate statement than is one who
experiences the minimum level of ambivalence. Likewise, the probability that a
subject who reports having the highest level of either type of decision ambivalence
does not read a single statement is about .08 less than one who reports having no
difficulty in choosing between the candidates. If we focus on the probability of
predicting one or more statements, then we see that the values hover around zero and
show a largely uniform distribution. So while the direction of candidate and decision
ambivalence differs, both types of ambivalence exert their largest impact on the
probability that one simply reads a statement about the candidate. These results
suggest that ambivalence may not affect the amount of information that one seeks out
about a candidate, but rather influences whether or not one seeks out any information
at all.
The above analysis demonstrated that candidate ambivalence does not lead
one to generally seek out more information in a campaign. A more pointed
hypothesis, however, is whether ambivalent feelings about a candidate increase one’s
motivation to seek out information about that specific candidate. Before turning to
this question, it is necessary to discuss two changes in how I analyze my models. For
74
each subject I have two measures of candidate searches, namely the number times a
person seeks information about either Powell or Sullivan. Rather than analyze this
information with two separate models, I treat the two variables as a repeated measure
of the same general dependent variable – candidate searches. Combined, this allows
me to increase my sample size from 162 to 324. By pooling the data in this manner, I
can no longer assume that each observation is independent of each of the other
observations. When this assumption is violated, one should still get unbiased
coefficient estimates, but potentially with incorrect standard errors. Thus, I run my
models using robust standard errors clustered on each respondent to control for this
problem, thereby indicating that the two observations for each respondent should not
be treated as independent.
Second, now that each subject counts as two observations it is necessary to
modify the ambivalence and election condition variables. First, the model no longer
includes measures of Powell and Sullivan ambivalence, but instead includes more
general measures of candidate and opponent ambivalence. For instance, when a
subject is searching for information on Powell, Powell ambivalence now becomes
candidate ambivalence and Sullivan ambivalence becomes opponent ambivalence.
Conversely, when this person searches for information on Sullivan, Sullivan
ambivalence now equals candidate ambivalence and Powell ambivalence is renamed
opponent ambivalence. Second, the four election conditions are expanded to six
conditions. Specifically, the election condition variables now specify how the subject
views the candidate who is the object of the searches as well as how the opposing
candidate is viewed. For instance, consider the Powell ambivalent versus the Sullivan
75
favorable election. When the subject is searching for information on Powell then the
election condition is “Ambivalent v. Good,” indicting the candidate of interest is
ambivalent and the opponent is favorable. Likewise, when this subject searches for
Sullivan the election condition is “Good v. Ambivalent,” meaning that candidate of
interest is favorable and the opponent is ambivalent.
The results for candidate specific searches are presented in Table 2.5 and
show a pattern of results similar to that for total searches. Turning to subjective
ambivalence, I find a negative relationship between opponent ambivalence and
candidate specific searches in the first two models, though the relationship is no
longer significant in the full model. This shows that one is less likely to seek
information about a candidate when one is conflicted about the opposing candidate.
Decision ambivalence, however, significantly increases the likelihood of searching
for information about a candidate in the first two models, though it loses significance
in the full model. Turning to objective ambivalence, I find a negative relationship
between opponent ambivalence and candidate searches. Candidate ambivalence is
also negatively related to getting information about the candidate in the first model,
but ceases to be significant in the final two models. Finally, the objective decision
ambivalence measure is not insignificantly related to candidate searches.
The results are again displayed graphically in Figure 2.2 using the same
predicted probability procedure outlined above. The results are highly consistent with
those from the previous models. Most notably, the most dramatic effect of
ambivalence is again concentrated on the likelihood of whether one simply searches
for information or not. A full shift in the candidate and opponent ambivalence
76
measures yields roughly a .04-.10 increase in the likelihood that one does not read
any statement (i.e. a predicted value of zero). The same shift in decision ambivalence
decreases the likelihood that one does not read any statement by about .08. As
before, the bulk of ambivalence’s effect is on the probability of reading zero
statements.
While the above suggests that candidate ambivalence does not cause
information-searching behavior, it may simply be the case that it leads people to seek
out specific types of information. I examine this question by exploring the different
types of information that one might try to obtain in an election by taking advantage of
the second manipulation in this study. Along with manipulating candidate positions, I
also varied the amount of issue information subjects were presented when given the
opportunity to read candidate statements. Specifically, half of the subjects were only
allowed the chance to read statements on the original six issues, while the other half
were also presented two additional issues.16 Drawing upon this manipulation, I
examine the number of times a subject opts to read a statement on one of the two new
issues. For this analysis, I investigate only those subjects who were presented with all
eight issues, as it is impossible for those who are not in this condition to seek such
information.17 Among these subjects, roughly 65% do not seek any information on
the two new issues, about 17% sought information on one statement, and another
17% sought information on two statements. Finally, one person read the candidate
statements on these two issues three times and another person read these statements
16
All subjects who are presented with 2 new issues will agree with the candidate on one fo the issues
and disagree with the candidate on the other.
17
Specifically, 87 of the 162 subjects were placed in this condition, yielding a repeated measures
sample of 174 cases.
77
four times. Therefore, I recoded the dependent variable into three categories: 0
searches, 1 search, and 2-4 searches. I use an ordered probit model (see Table 2.6 for
results) to analyze these results.18
Overall, the results do not notably differ from the previous analysis. First, the
objective candidate ambivalence coefficient is significant and negative in the first two
models, losing significance in the final model. Second, both subjective and objective
decision ambivalence exert a positive significant effect on seeking information in
each model. The predicted probabilities for these models are calculated as before and
presented in Figure 2.3. We again see that ambivalence felt towards a candidate
increases the likelihood that one bypasses accessing any information at all, while
decision ambivalence increases the chances that a subject will seek some new
information. The general trend mirrors the earlier findings, namely that decision
ambivalence increases the likelihood of searching, while candidate ambivalence,
when significant, decreases this likelihood.
As noted above, ambivalent voters might be inclined to focus on issues in
which their own preferences agree or disagree with the candidate’s positions. They
might focus on areas of disagreement in order to determine how much will be lost if
the candidate wins. Or they might focus on issues of agreement when the ambivalent
candidate is viewed more favorably than the opponent. To test for these
relationships, I use negative binomial models to examine the effects of ambivalence
on agreement and disagreement searches. First, I examine how many times a subject
reads a candidate statement in which the subject’s own preferences accord with the
18
I also ran the model using the negative binomial procedure and the substantive findings do not
notably differ.
78
candidate’s position on the issue. Second, I examine the number of times a subject
reads a candidate statement in which the subject’s own preferences disagree with the
candidate’s stand on the issue. The opportunity, however, to seek such information is
not constant across conditions. If a subject is in a condition where he views the
candidate either favorably or unfavorably, then there are a greater number of chances
for this subject to seek out information on areas of agreement and disagreement
respectively. For example, if a person agrees with Sullivan’s positions on five of the
six issues, then this means that roughly 87% of the statements one can read are
agreement statements. So if this person randomly selected an issue to read, then there
is a .87 probability of reading an agreement statement. Simply stated, depending
upon the condition a subject is in, the deck is potentially stacked in favor of
agreement or disagreement searches. I account for this bias in the agreement models
by adding a variable that records the total number of issues a subject’s preferences
accord with the candidate’s positions. Likewise, in the disagreement analysis, I add a
variable for the number of issues that a subject’s issue positions differ from those of
the candidate. So in the previous example, the subject who agrees with the candidate
on five of the six issues would be given a score of five on the agreement variable and
a score of one on the disagreement variable. The results for agreement and
disagreement searches are presented in Tables 2.7 and 2.8 respectively.
The results from both models, not surprisingly, are similar to the previous
analysis. Turning to the agreement searches, my results show a negative relationship
between both subjective and objective opponent ambivalence and agreement
searches. This relationship is significant in the first two models for both types of
79
ambivalence and loses significance in the full model. Subjective candidate
ambivalence does not exert a significant effect in any model, but objective candidate
ambivalence is negatively related to agreement searches in the first two models. By
and large, decision ambivalence is unrelated to agreement searches, though subjective
decision ambivalence is positive and significant in one of the models. Moving to the
disagreement models, I find that subjective decision ambivalence is the only
significant ambivalence variable in any of the models. As before, it is positively
related to searching behavior. Finally, the differences in predicted probabilities are
again graphically displayed in Figures 2.4 and 2.5 and, not surprisingly, are consistent
with the previous models. Candidate and opponent ambivalence make one less likely
to read any information on the candidates, while decision ambivalence exerts the
exact opposite effect.
The question remains, however, as to whether voters engage in motivated
information searches. I draw upon the election condition dummy variables to
determine if decisions to seek positive or negative information are conditioned on
how one views the opposing candidate. I test this relationship via a series of postregression tests in which I determine whether specific pairs of coefficients are
statistically different from each other. Specifically, I compare three pairs of
coefficients: (1) Ambivalent-Good to Ambivalent-Bad, (2) Ambivalent-Good to
Good-Ambivalent, and (3) Bad-Ambivalent to Ambivalent-Bad. When testing each
pair of coefficients, I expect to find significantly more positive and significantly less
negative searches in the second election in each pair. For instance, in the first test
(Ambivalent-Good v. Ambivalent-Bad) I expect that a voter will want to reinforce the
80
decision to support the ambivalent candidate when running against a bad candidate
and, in turn, seek positive information. The results from the post-regression tests are
presented in the bottom rows of Tables 2.7 and 2.8 and provide tentative evidence in
favor of motivated searches.
First, looking at the agreement search models, I find that the ambivalent-good
coefficient is never statistically different from the ambivalent-bad coefficient.
However, the ambivalent-good coefficient is statistically different than the goodambivalent coefficient in one of the subjective ambivalence models. Moreover, these
coefficients are also in the correct direction, namely there are more positive searches
for the candidate when the candidate is viewed more favorably than the opponent.
Finally, the bad-ambivalent coefficient is statistically different from the ambivalentbad coefficient in every case, and the coefficients are again in the correct direction.
Turning to the disagreement searches, my results reveal that none of the pairs of
coefficients are statistically different from each other.
While the evidence from the agreement models is suggestive of motivated
searching behavior, it is important to remain cautious about drawing any firm
conclusions from these findings. First, although in each model I control for the total
number of issues a subject agrees or disagrees with the candidate on, these values are
determined by the election condition variables. By definition, a person in the
ambivalent condition will agree and disagree with the candidate on an equal number
of issues. Thus, the election condition dummy variables may simply provide a better
measure of an opportunity to seek such information than does simply providing a
single variable for the total number of agree/disagree issues as a control. However, if
81
the election condition variables were simply picking up an opportunity to seek
agreement or disagreement searches, then I should find same pattern of results (in the
opposite direction) for the disagreement models. That is, a subject who is in the badambivalent election also has more of an opportunity to seek out disagreement
information than does a subject in the ambivalent-bad condition. Yet, none of the
pairs of election coefficients are significant in the disagreement models, suggesting
that the differences in the agreement models are not solely a function of opportunity.
In short, the evidence does not definitely demonstrate motivated searching behavior,
nor does it fully rule out such behavior. The existence of such a process merits more
attention in future studies of ambivalence.
Results: On-line v. Memory Based Processing
The above evidence indicates that ambivalence about an individual candidate
does not cause information-seeking behavior. In fact, to the contrary, the evidence
indicates that candidate ambivalence tends to reduce one’s propensity to gain
additional information. Given these findings, the question remains – why have
scholars found a positive relationship between ambivalence about a candidate and
accurately holding issue information about a candidate? An alternative explanation is
that ambivalent individuals do not seek out more information, but rather they are
simply more likely to retain and recall specific information about candidates. In
short, they are perhaps more likely to engage in memory-based instead of on-line
processing.
82
If ambivalence fosters better retention of candidate information, then I should
find that ambivalence is positively related to recall of the candidate issue positions in
my experiment.
I test this hypothesis by examining how well subjects are able to recall the issue
positions of the two candidates. Recall that subjects are first presented the issue
information on the two candidates and are then asked a series of “distraction
questions,” such as efficacy and sophistication. Once these questions have been
completed, each subject is asked to recall both Sullivan’s and Powell’s positions on
the six issues.19 It is important to keep in mind that at this stage in the study each
subject has been presented with the same amount of information; no subject has yet
had the opportunity to acquire more information. Therefore, any effect of
ambivalence on recall cannot be attributed to the acquisition of the new information;
it must be the product of better information retention. Using the responses to the
recall questions, I create an index of the number of issues a person correctly recalls
for each candidate. The values of this index can theoretically range from zero (no
issues correctly recalled) to six (all issues are correctly recalled). In actuality, the
values tend to fall between three and six issues correctly recalled.
I use this index as my dependent variable and test for the effects of
ambivalence via ordered probit analysis (see Table 2.9 for results). Turning to the
subjective ambivalence, I find that both subjective candidate and opponent
ambivalence are positively related to recall of candidate positions, while decision
19
Each subject is asked about all six of one of the candidates issue positions before being asked about
the positions of the other candidate. Which candidate is asked about first is determined by random
assignment.
83
ambivalence has no influence. The objective ambivalence models, however, largely
reveal no relationship between any type of ambivalence and issue recall. Candidate
ambivalence is negatively related to recall at the .10 level in the simplest model, but
no other ambivalence coefficient is significant in any model. The first differences for
the effects of ambivalence are again graphed and displayed in Figure 2.6. I set each
variable at its means and calculate the probability that of recalling one thru six issues
for a person who experiences both the maximum and minimum level of ambivalence.
As before, positive results demonstrate that ambivalence makes the outcome more
likely. Interestingly, these graphs indicate that that main effect of ambivalence is
concentrated on the likelihood that a person correctly recalls all six issue positions.
The effect of each type of ambivalence on the change in probability of recalling 1 to 5
issues hovers around .03-.05. The likelihood that one recalls all six issues is changes
by about .10 to .20 when one moves from the minimum to maximum level of
ambivalence. For instance, a person who reports the minimum amount of subjective
ambivalence about the candidate is roughly .20 less likely to recall correctly all six
issues than is a subject who reports the highest level of subjective ambivalence.
Overall, these results indicate that candidate ambivalence increases the likelihood that
one will correctly recall candidate specific information.
Another test of memory-based processing is to examine reaction times to
general candidate evaluation questions. By definition, people who utilize on-line
processing do not draw upon specific information at the time of judgment and instead
rely on their summary tallies of the attitude object (i.e. the candidate). Conversely,
people who rely upon memory-based processing must retrieve some set of relevant
84
information about the object and then assimilate this information into a summary
judgment. This, in turn, means that on average people who have higher reaction
times are more likely to be engaging in memory-based processing. So if ambivalence
is positively related to reaction times, then one can reasonably assume that the
ambivalent are engaging in memory-based processing. To test this hypothesis, I use
each subject’s reaction time on the feeling thermometer questions as my dependent
variable. Along with the standard independent variables used in my analysis this far,
it is necessary to control for the fact that some people simply respond faster to
questions in general. Thus, I also include each subject’s mean response time to the
six ambivalence questions asked earlier as an independent variable20. This control
serves two purposes. First, it provides a baseline measure of one’s general reaction
time. Second, by using the mean response to other candidate evaluation questions I
am using an independent variable that is closely related to my dependent variable.
This variable, in turn, should account for a sizable portion of the variance in response
time to the general feeling thermometer scores, making it more difficult for the other
variables to explain any of the variance in response times.21
These results are presented in Table 2.10 and the findings are nearly identical
for subjective and objective ambivalence. In every model, candidate ambivalence is
positively and significantly related to higher reaction times. Neither opponent nor
decision ambivalence is a significant influence in any model. These findings provide
20
Specifically, the question that ask if the person has positive, negative, reasons to support, reasons to
oppose, and mixed feelings about the candidate as well as conflicted feelings about one’s vote
decision.
21
I also ran the model using the mean reaction time to 6 demographic questions, as well as the
combined mean time to the 6 ambivalence and 6 demographic questions. The results do not
substantively differ when different baseline reaction measures are used.
85
unambiguous support for the ambivalence causing memory-based processing
hypothesis.
My findings from both the recall and reaction time models tell a very
consistent story. In each case, the evidence strongly supports the claim that
ambivalent voters are more likely to engage in memory-based in lieu of on-line
processing. With respect to candidate ambivalence, these results are also largely
consistent with the evidence from information-seeking analysis. Simply stated,
ambivalence about a candidate does not cause one to be more likely to pursue and
acquire more information about a candidate, but rather makes them more likely to
retain the specific information they encounter. This is an important finding, as it
shifts the way in which we should view the potential benefits of ambivalence. It
suggests that ambivalent individuals do not process information better, but simply
process information differently. I will revisit this issue in more detail in the
concluding section of this chapter.
Abstention: Measurement and Expectations
At this point, I will turn my attention on whether ambivalence leads voters to
abstain in elections. Ambivalent individuals face a difficult decision on Election Day.
They are not disengaged voters who are uninformed or indifferent to the candidates.
In fact, by definition, an ambivalent individual has multiple thoughts about a
candidate, but these thoughts pull the voter in opposing directions. The problem for
these voters is a difficulty in ascertaining how to use the information they have to
determine whether they should support the candidate. Rather than solve this
86
problem, many of the ambivalent may simply opt to avoid the problem and simply do
not vote in an election.
The subjects in my study are, in fact, given the option of abstaining in my
hypothetical election. Given, however, that a major hurdle for voting is often the
time and effort needed to get to the polling stations, I anticipated that few people
would select this option. Once a “voter” is at the polling station, he is almost
certainly going to cast his ballot. Additionally, the fact that this is not a real election
may also make people less inclined to abstain, as there is no real consequence to
voting incorrectly. As it turns out, only 10 of the 162 (6.2%) subjects in my sample
stated that they would abstain in this election.
Fortunately, in anticipation of this problem, I also asked each subject a series
of questions designed to measure their general predisposition for voting in an election
between these two candidates. I described a variety of situations one might face on
Election Day that would make it more difficult to get to the polling station. After
describing each situation, I then asked them how likely they thought it was that they
would vote under that circumstance. Specifically, I asked them about their
motivation to vote under the following conditions:
(1) Imagine that you were sick with the flu on Election Day, how likely do
you think it is that you would take the effort to go out and vote?
(2) Suppose that when you get to the polling place you see a line of people
waiting out the door to vote. A precinct worker tells you that it will be
at least 45 minutes until you are able to vote. How likely is it that you
will stay and wait in line?
(3) Suppose that you work on Election Day and the only time you can vote is
over your lunch break. Given this situation, how likely do you think it
is that you would vote in this election?
(4) Imagine that your car breaks down on Election Day and that in order to
vote you will have to take a 30 minute bus ride (each way) to the
87
polling station. Given this scenario, how likely do you think you are
to take the bus ride and vote?
(5) Imagine that you have classes and work during the day and the only way
that you can vote is if you get up at 6:30 and vote in the morning.
How likely do you think you are to get up and vote?
After each question, subjects could state whether they thought it was “not at all”,
“somewhat”, “fairly”, “very”, or “extremely” likely that they would vote in the
election under those circumstances.
Ultimately, this index seeks to measure a person’s underlying motivation to
vote in an election between Sullivan and Powell. The primary advantage of using this
index is that subjects are provided with plausible reasons for abstaining on Election
Day. While social pressures may exist that compel us to state we are planning to
vote, these pressures should be alleviated once we are given a legitimate reason for
why we might be unable to participate. By providing subjects with valid excuses for
abstaining in this hypothetical election, these questions should be less prone to
systematic bias emanating from social norms for voter turnout. I measure one’s
propensity to vote in this election by calculating the mean of these five questions
(alpha = .859). Scores on this index range from 0 to 4 and are used as the dependent
variable, where higher values indicate a greater propensity to vote. Using this
measure, I predict:
H14: Candidate ambivalence is negatively related to voting propensity
H15: Decision ambivalence is negatively related to voting propensity
Admittedly, this index is not identical to one’s actual decision to vote in an
election, but it should serve as a good proxy measure of my concept. First, it is
88
reasonable to assume that those people who respond with a greater likelihood of
voting in this fictional election should, at a minimum, have a greater motivation to
actually vote in a similar real election. Second, there is likely a positive social
desirability bias to this measure, namely most people are probably inclined to report a
high likelihood of voting in all of these conditions. This bias, in turn, should make it
more difficult to find any significant differences in responses to this question as a
result of my manipulations. Combined, it is reasonable to expect that any relationship
I find between ambivalence and this voting propensity measure is also likely to exist
in a non-experimental setting.
Results: Abstention
I first examine the relationship between ambivalence and one’s motivation to
vote by running a simple model that includes only the subjective and objective
ambivalence measures. For the second model, I add a dummy variable for whether
one voted in a 2002 gubernatorial or senate election, the four-point strength of
partisanship measure and indices each for efficacy, trust in government, general
attention to the media, and sophistication. Numerous studies have demonstrated a
positive relationship between these variables and turnout in actual election; therefore,
I expect these measures to likewise be positively related to motivation to vote.
Overall, the first model provides the most liberal test for my theory, as it does not
account for any other potential influence on vote decisions, while the second model
provides a more stringent test by controlling for these factors. The results for both
models presented in Table 2.11.
89
First, it is important to note that the control variables behave largely as
expected in these models. Specifically, trust in government, efficacy, and past
turnout all exert a positive influence on the dependent variable.22 These relationships
provide further validation that individual scores on my motivation to vote index are,
in fact, tapping the same concept that motivates one to vote in a non-experimental
setting. Turning to the effects of ambivalence, I find that neither Sullivan nor Powell
ambivalence is significantly related to one’s motivation to vote. Thus, in contrast to
my initial expectations, candidate ambivalence does not affect abstention decisions.
The results for decision ambivalence, however, do support my original hypotheses.
As expected, both subjective and objective decision ambivalence exert a significant
negative impact on a subject’s propensity to vote. Subjective decision ambivalence is
significant at the .10 level in the simple model, though it loses significance in the full
model. Specifically, a one unit increase on the subjective decision ambivalence scale,
on average, produces a .12 decrease in one’s predicted voting propensity score.
Therefore, a full shift in the subjective ambivalence scale (e.g. from 0 to 3), decreases
the motivation to vote index value by about a third of a point. As a means of
comparison, such a shift is roughly two-thirds the impact that past voting behavior
exerts on turnout behavior.
Objective decision ambivalence likewise is associated with a significant
negative influence on the motivation to vote in both models, namely at the .05 level in
the simple model and at the .10 level in the fuller model. For instance, a one point
increase in feelings of objective decision ambivalence decreases one’s motivation to
22
Partisan strength is significant at the .13 and .11 levels for the subjective and objective models,
respectively.
90
vote score by about .08 points in each of the full models. The lowest score for
objective decision ambivalence in my dataset is -5.25 and the highest is 3, a range of
8.25 points. Thus, a full shift on this scale drops one’s motivation to vote score, on
average, by about two-thirds of a point. A smaller shift, of say ± 1 standard deviation
(± 1.4 points), yields about a .34 point drop, which is about the same effect as a full
shift in subjective ambivalence.
Combined, these results clearly demonstrate that both subjective and objective
ambivalence cause one to become less motivated to vote. Moreover, in each case, the
magnitude of this effect is not minimal, demonstrating the potential for decision
ambivalence to exert a substantively, as well as significantly, significant impact on
turnout decisions. The story from these results is clear. Voters who are conflicted
about which candidate to support on Election Day are more likely to stay at home.
Discussion
Taken together, the results presented in this chapter provide mixed support for
my hypotheses. My expectations about the relationship between ambivalence and
abstention are generally confirmed, while the evidence in support of the informationseeking hypothesis is, at best, mixed. I will start by discussing my findings on the
relationship of ambivalence and abstention, as these results are more straightforward.
As just discussed, I find that as people experience greater levels of decision
ambivalence they become more likely to abstain in an election. Candidate
ambivalence, however, does not influence one’s motivation to vote. Although I
predicted both types of ambivalence will increase abstention, this pattern of results is
consistent with the general argument I laid out in the previous chapter. In generic
91
terms, my hypothesis stated that the more ambivalent one is about an attitude object,
the more likely this person will try to avoid making a definitive favorable or
unfavorable assessment of the object. My results suggest that the key object in an
election is not an individual candidate, but rather is the vote decision itself. When
one is conflicted about which candidate to support – is decisionally ambivalent – this
person becomes less motivated to vote for either candidate. In other words, people
who are conflicted about their vote choice are less likely to make a choice altogether;
they are more likely to abstain.
The fact that candidate ambivalence is not related to abstention does not
counter my basic argument, as these people are not necessarily conflicted about the
attitude object of interest – vote choice. People who are conflicted about a candidate
appear to be just as motivated to vote and express support for one candidate over the
other as do those who are not ambivalent about the candidate. In other words, these
people have no apprehensions about expressing a vote choice preference. Clearly
ambivalent attitudes matter for people when deciding whether to vote, but it is
ambivalence about one’s vote choice, and not a candidate, that affect this decision.
The positive relationship between decision ambivalence and abstention has
important substantive and normative implications. First, it demonstrates that many
individuals may not be abstaining in elections out of apathy, uncertainty or
indifference to the candidates. Instead, many individuals may abstain in election due
to an abundance of information, but information that pulls the potential voter in
opposite directions. Scholars now realize the need to distinguish between the effects
of ambivalence and uncertainty (cf. Alvarez and Brehm 2002, McGraw et al. 2003),
92
as many see the two concepts as closely related. My study is able to differentiate the
effects of ambivalence from the effects of uncertainty by holding the amount of
information constant across conditions. This, in turn, means that the relationship I
find between decision ambivalence and motivation to vote cannot be attributed to
uncertainty. As a result, I find that many people might opt to stay at home on
Election Day out of dissatisfaction with the candidates, and not out of apathy or
uncertainty. From this perspective, abstention decisions are driven by institutional
factors, such as the choice-set of candidates, and not by individual traits. This means
that part of our concerns about dwindling voting rates in recent elections should not
be solely directed at limitations of the electorate, but with potential flaws in how we
select our candidates.
Turning to the information-seeking results, I find mixed evidence in support
of my theory. First, contrary to my expectations, I find that candidate ambivalence
does not cause one to seek out additional information on a candidate. In fact, those
times that candidate-ambivalence is significant, it decreases the likelihood of
information-seeking behavior. Decision ambivalence behavior, however, is largely
consistent with my theoretical expectations. In general, I find that subjective decision
ambivalence significantly increases the likelihood that one acquires information about
the candidates. In contrast, objective decision ambivalence is largely insignificant.
As was noted in the discussion of the abstention results, it is likewise not
surprising that decision ambivalence increases information-seeking behavior, while
candidate ambivalence does not. The key problem facing people in an election is
which candidate to support on the ballot and feelings of ambivalence about one
93
candidate need not hinder one’s ability to form a candidate preference in an election.
Therefore, so long as a person has a preference for one candidate over the other, then
there is no need for additional information, even if the person is ambivalent about the
candidate. Just as before, it is only when one is conflicted about the key attitude
object – the vote choice itself – that one feels the need to obtain more information
before making a decision on the ballot.
While the differing effects of candidate and decision ambivalence make sense,
the stark difference in the effects of subjective and objective decision ambivalence is
surprising. Upon further reflection, however, there is also a reasonable explanation
for this finding. I argue that ambivalent individuals are more likely to seek
information because they want to resolve any possible anxiety or discomfort they
experience as a result of their conflicted feelings. Thus, it is not surprising that those
people who express a greater subjective awareness of a conflicted attitude about
which candidate to support are also more likely to seek more information about the
two candidates. A person who has a greater awareness of her decision ambivalence is
presumably more likely to feel uncomfortable in stating a preference for one
candidate over the other in an election. In turn, this person becomes motivated to
seek out more information in an attempt to determine which candidate she prefers.
This suggests that subjective awareness of one’s conflicted attitude is more important
than objective conflict in motivating one to seek out information on the candidates.
The evidence indicates that the decision to seek information is a dichotomous
choice – will one or won’t one acquire information? While people may search for
different amounts of information, there does not appear to be any systematic
94
differences among those who seek a small amount of information to those who seek a
lot of information. For candidate ambivalence, subjects become more likely to skip
reading any of the candidate statements, and for decision ambivalence subjects
become more likely to acquire some amount of information. In sum, ambivalence
influences whether or not one seeks any information, but does not influence how
much information one seeks. Combined, the effects of decision ambivalence
documented in the information-seeking and abstention models present a somewhat
paradoxical process. While the information-seeking results indicate that decision
ambivalence makes one more likely to engage the campaign, the abstention results
indicate that decision ambivalence also causes one to walk away from the process on
Election Day. The fact that a group of individuals who are engaged in the campaign
would opt to abstain is worrisome, as it suggests their lack of participation is a
function of dissatisfaction with the candidates, and not apathy to the process.
Finally, if I find that candidate ambivalence does not cause information-seeking
behavior, then this raises the question as to why so many studies provide evidence that
suggests such a link exists? Rather than seek new information, I find that those with
ambivalent candidate attitudes are more likely to remember the information they
encounter; ambivalence causes memory-based processing. So the fact that ambivalence
is positively related to systematic processing or accuracy is not the product of some
normatively more desirable behaviors on the part of ambivalent voters, but is merely
the consequence of the different manner in which they store the information they
encounter. I find, holding the amount of information one is exposed to constant, that
ambivalent individuals are more likely to recall accurately the issue positions of the
95
two candidates. Additionally, an examination of feeling thermometer reaction times
reveals that ambivalent individuals take more time to evaluate candidates, further
suggesting they are more likely to engage in memory-based processing. Combined,
these results provide compelling evidence that ambivalence does not lead to more
thorough or rigorous use of information over the course of a campaign. The fact that
voters with ambivalent candidate attitudes process information in a manner that
ostensibly gives the appearance of greater engagement with their political environment
should not be confused with actually being more engaged with the political
environment. In fact, my results suggest that in all likelihood ambivalent and nonambivalent voters may be exposed to the same information during a campaign. The
only difference between the two is how they process and store this information.
Contrary to what other studies might suggest, the ambivalent are not better voters;
they are merely different voters.
Along with the normative issues raised above, taken together, my results also
raise an interesting question about how we should study voting behavior in general.
Specifically, it is intriguing that decision ambivalence largely behaved in accordance
with my expectations, while candidate ambivalence generally did not. These findings
suggest that political scientists may be giving too much attention to how the
electorate views the individual candidates themselves, and not enough time on
understanding how the electorate views the vote decision. In psychological terms, we
have focused too much attention on the wrong attitude object – we focus on how
candidate evaluations affect the decision, but fail to view the decision itself as its own
object. This is not to say that individual evaluations are not important, as decades of
96
voting behavior research demonstrates their utility. There is no denying that knowing
how a voter views both candidates individually will provide leverage for explaining
and understanding how this person will behave during a campaign. This knowledge
alone, however, will not reveal the whole story. It is also important to examine an
individual’s attitude about his or her vote preference itself.
As my findings above illustrate, a focus on only candidate ambivalence would
not provide further insights into which people are more likely to attain information
about the candidates or who is more likely to abstain in an election. This finding was
only discovered once I examined the properties of one’s attitude towards the vote
decision itself. There is no doubt that individual candidate evaluations influence
one’s vote decision. The key point here is that the decision itself is not merely a
product of these evaluations, but rather that it has its own unique attitude properties
that need to be examined. Thus, by focusing on the vote choice attitude itself, we
gain additional leverage for explaining voting behavior that we might otherwise miss
by looking only at candidate attitudes.
The role of ambivalence in voting behavior is complex. By manipulating
ambivalence itself in the above experiment, I am able to disentangle its causal effects.
First, I find that despite an abundance of suggestive evidence, candidate ambivalence
does not cause information-seeking behavior. Instead, it promotes memory-based
processing which, in turn, makes it easier for the ambivalent to recall candidate
specifics at a later time. Subjective decision ambivalence, however, does increase the
likelihood that one will acquire additional information about a candidate. Second, my
results show that candidate ambivalence is unrelated to abstention, but that decision
97
ambivalence decreases a person’s motivation to vote. As noted, this is a worrisome
outcome that needs to receive more attention. Finally, and perhaps most importantly,
my combined results demonstrate a need for scholars to devote more energy to
viewing the vote decision itself as its own attitude object, and not merely the product
of the evaluations of other objects (i.e. the candidates). Before doing so, however, it
is important to test these findings in a non-laboratory setting. Therefore, in the
following chapters I extend this analysis by assessing voting behavior in the 19802000 presidential elections.
98
Subjective
Sullivan Condition
Powell Condition
Decision Condition
Sex
Age
Education
Minority
Trust in Government
Efficacy
Media Use
Sophistication
Need for Cognition
Need for Evaluation
Partisan Strength
Constant
Adjusted R2
N
.170
.428***
-.170
Objective
.132
.429**
-.030
2.072***
.193
.428***
-.287**
-.166
.000
-.245***
-.376**
-.061
.007
.026
-.088*
.039
.007**
-.218***
2.922***
.383**
.139
.422**
-.104
-.016
.002
-.135
-.207
-.045
.004
.016
-.083
.038*
.010
-.168*
.826
.0489
162
.1781
162
.0290
162
.0370
162
Table 2.1: Predicting Powell Ambivalence: OLS Regression, (Robust Standard
Errors)
99
Subjective
Sullivan Condition
Powell Condition
Decision Condition
Sex
Age
Education
Minority
Trust in Government
Efficacy
Media Use
Sophistication
Need for Cognition
Need for Evaluation
Partisan Strength
Constant
Adjusted R2
N
.438***
.325**
-.120
Objective
1.925***
.413***
.387**
-.126
-.214
.035
-.161**
.032
-.106**
.040
-.019
.010
-.009
-.024
-.197**
2.334***
.417***
.014
.270*
.401***
.370**
-.016
.383
-.286*
-.009
.002
.230
-.065
.063*
-.025
-.045
.012
-.064
-.166**
1.600
.0598
162
.1120
162
.0521
162
.1020
162
Table 2.2: Predicting Sullivan Ambivalence: OLS Regression, (Robust Standard
Errors)
100
Subjective
Sullivan Condition
Powell Condition
Decision Condition
Sex
Age
Education
Minority
Trust in Government
Efficacy
Media Use
Sophistication
Need for Cognition
Need for Evaluation
Partisan Strength
Constant
Adjusted R2
N
.101
.147
.558***
Objective
-.038
-.088
.757***
.808***
.008
.122
.585***
.033
-.022
-.022
-.087
-.060
.036
-.021
.061
.024
-.020
-.149*
1.222**
-.037
-.102
-.160
.795***
.025
-.006
-.105
-.390
-.182**
.056
-.041
-.014
.059
-.096*
-.239**
1.687*
.0708
162
.0714
162
.0544
162
.1216
162
Table 2.3: Predicting Decision Ambivalence: OLS Regression, (Robust Standard
Errors)
101
Subjective Ambivalence
Objective Ambivalence
0.055
0.105
0.206 -0.067 -0.031
0.018
-0.271*** -0.26** -0.23* -0.208** -0.193** -0.229**
0.23** 0.254** 0.188 0.067
0.027
0.037
Sullivan Ambivalence
Powell Ambivalence
Decision Ambivalence
Ambiv. v Good
Condition
0.325
Good v Good Condition
0.42
Ambiv. v Bad Condition
0.298
8 Issue Dummy
-0.051
-0.035
Trust in Government
Efficacy
Media
Sophistication
Need for Cognition
Need for Evaluation
Female
Age
PID (6=SR)
Minority
Constant
1.676*** 1.235***
N
162
162
* p < .10; ** p < .05; *** p < .01
0.323 0.086
0.05
0.532
0.364
0.513
0.107
0.041
0.104
0.1
-0.021
-0.008
0.167*
0.035
0.077
0.252
0.028
0.052
0.066
-2.319* 1.576*** 1.403***
162
162
162
0.097
0.444
0.337
0.134
0.088
-0.006
-0.009
0.186**
0.039
0.054
0.258
0.028
0.005
0.026
-1.863
162
Table 2.4: Effect of Ambivalence on the Number of Any Candidate Statements
Searches: Negative Binomial Results, (Robust Standard Errors Clustered on
Individual)
102
Subjective Ambivalence
Objective Ambivalence
-0.07
-0.03
0.013 -0.137** -0.097 -0.095
-0.13*
-0.12* -0.064 -0.132** -0.126* -0.112
0.193** 0.209** 0.157
0.065
0.032
0.031
Candidate Ambivalence
Opponent Ambivalence
Decision Ambivalence
Ambiv. v Good
Condition
Good v. Ambiv
Condition
Good v. Good Condition
Ambiv v Bad Condition
Bad v. Ambiv Condition
8 Issue Dummy
-0.025
Trust in Government
Efficacy
Media
Sophistication
Need for Cognition
Need for Evaluation
Female
Age
PID (6=SR)
Minority
Constant
0.987***
N
324
* p < .10; ** p < .05; *** p < .01
0.114
0.106
0.104
-0.071
-0.043
0.311
0.336
0.375
0.224
-0.013
0.336
0.129
0.176
0.439
0.327
0.427
0.533
0.18
0.348
0.36
0.064
0.199
0.073
0.114
0.133
0.075
0.068
-0.007
0.008
-0.02
-0.022
0.179**
0.191**
0.023
0.039
0.054
0.032
0.203
0.224
0.029
0.023
0.006
-0.001
0.095
0.115
0.633 -2.363* 0.879*** 0.726*** -2.122*
324 324
324
324 324
Table 2.5: Effect of Ambivalence on the Number of Candidate Specific Statement
Searches: Negative Binomial, (Robust Standard Errors Clustered on Individual)
103
Candidate Ambivalence
Opponent Ambivalence
Decision Ambivalence
Ambiv. v Good
Condition
Good v. Ambiv
Condition
Good v. Good Condition
Ambiv v Bad Condition
Bad v. Ambiv Condition
Trust in Government
Efficacy
Media
Sophistication
Need for Cognition
Need for Evaluation
Female
Age
PID (6=SR)
Minority
Subjective Ambivalence
Objective Ambivalence
-0.156
-0.122 -0.084 -0.191* -0.169* -0.116
-0.026
-0.014
0.037 -0.097 -0.105 -0.062
0.471*** 0.489*** 0.384** 0.25** 0.24** 0.233**
Cut 1
0.552
Cut 2
1.14
Pseudo R2
.0708
N
174
* p < .10; ** p < .05; *** p < .01
-0.02
-0.059
-0.189
-0.082
0.159
0.323
0.474
0.382
0.166
0.487
0.617
0.528
0.027
0.01
-0.011
0.118
0.053
0.047
-0.039
0.003
-0.032
0.009
-0.005
0.241
0.306
0.172
0.175
0.471
0.635
0.481
0.029
0.019
-0.017
0.163*
0.064
0.053
-0.004
-0.009
-0.047
-0.151
0.875
1.472
.0811
174
2.897
3.529
.1189
174
0.349
0.909
.0371
174
2.656
3.271
.0793
174
0.252
0.805
.0284
174
Table 2.6: Effect of Ambivalence on the Number of New Issue Statement Searches:
Ordered Probit, (Robust Standard Errors Clustered on Individual)
104
Subjective Ambivalence
Candidate Ambivalence
-0.093 -0.066 -0.037
Opponent Ambivalence
-0.155** -0.151* -0.106
Decision Ambivalence
0.16 0.211** 0.181
Ambiv. v Good Condition
0.09
0.095
Good v. Ambiv Condition
0.843
0.771
Good v. Good Condition
0.85
0.797
Ambiv v Bad Condition
0.357
0.546
Bad v. Ambiv Condition
-0.761 -0.512
# of Agree Issues
0.295*** -0.043 0.024
Trust in Government
0.031
Efficacy
-0.032
Media
-0.027
Sophistication
0.203**
Need for Cognition
0.027
Need for Evaluation
0.058
Female
0.285
Age
0.036*
PID (6=SR)
0.025
Minority
-0.044
Constant
-0.738* 0.199 -2.936**
Significance
Ambiv Good = Ambiv
Bad
.4074
.1854
Ambiv Good = Good
Amiv
.0946* .1460
Ambiv Bad = Bad Ambiv
.0153* .0320*
N
324
324 324
* p < .10; ** p < .05; *** p < .01
Objective Ambivalence
-0.152** -0.13* -0.109
-0.159** -0.137* -0.101
0.061
0.051 0.045
-0.07 -0.041
0.397 0.442
0.594 0.655
0.196 0.392
-0.61 -0.468
0.332*** 0.097 0.112
0.033
-0.016
-0.029
0.214**
0.039
0.047
0.295
0.031
0.021
-0.001
-1.054*** -0.286 -3.283**
Significance
.3802
.1877
.3157
.307
.0958* .0828*
324
324 324
Table 2.7: Effect of Ambivalence on the Number of Candidate Agreement Searches:
Negative Binomial, (Robust Standard Errors Clustered on Individual)
105
Subjective Ambivalence
Objective Ambivalence
Candidate Ambivalence
0.007
0.009
0.059
-0.075 -0.076 -0.061
Opponent Ambivalence
-0.078 -0.079 -0.036
-0.106 -0.103 -0.089
Decision Ambivalence
0.18* 0.213** 0.154
0.028
0.028 0.037
Ambiv. v Good Condition
0.15
0.185
-0.056 0.028
Good v. Ambiv Condition
-0.055
0.13
-0.201 -0.033
Good v. Good Condition
-0.017 0.273
-0.013 0.197
Ambiv v Bad Condition
0.386
0.425
0.185
0.27
Bad v. Ambiv Condition
0.274
0.158
0.027 0.013
# of Disagree Issues
0.256*** 0.192
0.267 0.256*** 0.229 0.274
Trust in Government
0.114
0.102
Efficacy
0.013
0.023
Media
-0.013
-0.018
Sophistication
0.189**
0.19***
Need for Cognition
0.012
0.021
Need for Evaluation
0.039
0.028
Female
0.009
-0.004
Age
0.009
0.004
PID (6=SR)
-0.029
-0.043
Minority
0.165
0.153
Constant
-0.966*** -0.904 -3.685*** -0.777*** -0.69 -3.121**
Significance
Significance
Ambiv Good = Ambiv
Bad
.4490
.4391
.4134 .4130
Ambiv Good = Good
Amiv
.6233
.8973
.7327 .8863
Ambiv Bad = Bad Ambiv
.7954
.5443
.7176 .5587
N
324
324 324
324
324 324
* p < .10; ** p < .05; *** p < .01
Table 2.8: Effect of Ambivalence on the Number of Candidate Disagreement
Searches: Negative Binomial, (Robust Standard Errors Clustered on Individual)
106
Subjective Ambivalence
0.048 0.13*
0.139**
0.092 0.126*
0.136**
0.021 -0.013
-0.025
Candidate Ambivalence
Opponent Ambivalence
Decision Ambivalence
Ambiv. v Good
Condition
-0.03
-0.06
Good v. Ambiv
Condition
0.401*
0.37
Good v. Good Condition
0.654*** 0.669***
Ambiv v Bad Condition
-0.028
-0.104
Bad v. Ambiv Condition
0.039
-0.038
Trust in Government
0.003
Efficacy
0.035
Media
-0.005
Sophistication
0.044
Need for Cognition
-0.028
Need for Evaluation
-0.02
Female
0.053
Age
-0.013
PID (6=SR)
-0.022
Minority
0.081
N
324
324
324
* p < .10; ** p < .05; *** p < .01
Objective Ambivalence
-0.131* -0.062
-0.06
-0.104
-0.104 -0.106
0.112
0.073
0.063
324
-0.116
-0.156
0.345
0.391*
-0.032
0.023
0.306
0.396*
-0.119
-0.066
-0.019
0.041
-0.004
0.016
-0.025
-0.021
-0.041
-0.011
-0.04
0.028
324
324
Table 2.9: Effect of Ambivalence on the Total Number of Issues Correctly Recalled:
Ordered Probit Regression, (Robust Standard Errors Clustered on Individual)
107
Subjective Ambivalence
Objective Ambivalence
0.579*** 0.475*** 0.541*** 0.451*** 0.33** 0.35**
-0.069
0.007
0.072
0.119
0.204
0.223
0.121
0.211
0.244
-0.092 -0.022
0.079
Candidate Ambivalence
Opponent Ambivalence
Decision Ambivalence
Ambiv. v Good
Condition
0.862 0.969*
0.898
1.037*
Good v. Ambiv
Condition
-0.066
0.043
-0.199 -0.059
Good v. Good Condition
0.432
0.437
0.323
0.226
Ambiv v Bad Condition
0.535
0.534
0.434
0.52
Bad v. Ambiv Condition
2.152** 2.15**
2.153** 2.238**
Mean Response Time
0.454*** 0.447*** 0.442*** 0.462*** 0.455*** 0.453***
Trust in Government
0.236*
0.208
Efficacy
0.007
0.004
Media
-0.003
0
Sophistication
0.157
0.176
Need for Cognition
-0.098**
-0.105**
Need for Evaluation
0.131*
0.141*
Female
0.1
0.096
Age
0.009
0.01
PID (6=SR)
0.015
0.027
Minority
0.721
0.584
Constant
0.915
0.43
-2.292 1.768*** 1.385** -1.146
2
Adjusted R
0.2866 0.2836 0.2999 0.2738 0.2918 0.2914
N
324
324
324
324
324
324
* p < .10; ** p < .05; *** p < .01
Table 2.10: Predicting Reaction Times to the Candidate Feeling Thermometer Scores:
OLS Regression, (Robust Standard Errors Clustered on Individual)
108
Subjective Ambivalence
Sullivan Ambivalence
-0.055
0.019
Powell Ambivalence
-0.082
-0.097
Decision Ambivalence
-0.124*
-0.092
Trust in Government
0.088**
Efficacy
0.111***
Media Use
0.05***
Sophistication
-0.079*
Vote in 2002
0.539***
Partisan Strength
0.111
Constant
2.185***
0.129
2
Adjusted R
.02
.3729
N
162
162
* p < .10; ** p < .05; *** p < .01
Objective Ambivalence
-0.128
-0.045
0.043
0.009
-0.129**
-0.082*
0.081*
0.112***
0.045***
-0.078*
0.532***
0.114
1.827***
-0.041
.0462
.3706
162
162
Table 2.11: Predicting Subjects Scores on the Motivation to Vote Index: OLS
Regression, (Robust Standard Errors)
109
Sullivan Ambivalence
0.16
Probability
0.12
0.08
0.04
Subjective Sullivan
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Sullivan
-0.08
-0.12
-0.16
# of Searches
Powell Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Powell
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Powell
-0.08
-0.12
-0.16
# of Searches
Decision Ambivalence
0.16
Probability
0.12
0.08
0.04
Subjective Decision
0
-0.04
0
1
2
3
4
5
6
7
8
9 10
Objective Decision
-0.08
-0.12
-0.16
# of Searches
Figure 2.1: First Differences for Probability of Reading any Issue Statement:
Maximum Ambivalence Probability – Minimum Ambivalence Probability
(All Non-ambivalence Variables Placed at Mean Value; Positive Values =
Ambivalence Makes Outcome More Likely)
110
Candidate Ambivalence
0.16
Probability
0.12
0.08
0.04
Subjective Candidate
0
-0.04
0
1
2
3
4
5
6
7
8
9 10
Objective Candidate
-0.08
-0.12
-0.16
# of Searches
Opponent Ambivalence
0.16
Probability
0.12
0.08
0.04
Subjective Opponent
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Opponent
-0.08
-0.12
-0.16
# of Searches
Decision Ambivalence
0.16
Probability
0.12
0.08
0.04
Subjective Decision
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Decision
-0.08
-0.12
-0.16
# of Searches
Figure 2.2: First Differences for Probability of Reading a Candidate Specific
Statement:
Maximum Ambivalence Probability – Minimum Ambivalence Probability
(All Non-ambivalence Variables Placed at Mean Value; Positive Values =
Ambivalence Makes Outcome More Likely)
111
Candidate Ambivalence
0.6
Probability
0.4
0.2
Subjective Candidate
0
-0.2
0
1
2+
Objective Candidate
-0.4
-0.6
# of Searches
Opponent Ambivalence
0.6
Probability
0.4
0.2
Subjective Opponent
0
-0.2
0
1
2+
Objective Opponent
-0.4
-0.6
# of Searches
Decision Ambivalence
0.6
Probability
0.4
0.2
Subjective Decision
0
-0.2
0
1
2+
Objective Decision
-0.4
-0.6
# of Searches
Figure 2.3: First Differences for Probability of Reading a New Candidate Issue
Statement: Maximum Ambivalence Probability – Minimum Ambivalence Probability
(All Non-ambivalence Variables Placed at Mean Value; Positive Values =
Ambivalence Makes Outcome More Likely)
112
Candidate Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Candidate
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Candidate
-0.08
-0.12
-0.16
# of Searches
Opponent Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Opponent
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Opponent
-0.08
-0.12
-0.16
# of Searches
Decision Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Decision
0
-0.04
0
1
2
3
4
5
6
7
8
9
10
Objective Decision
-0.08
-0.12
-0.16
# of Searches
Figure 2.4: First Differences for Probability of Reading a Candidate Agreement
Statement: Maximum Ambivalence Probability – Minimum Ambivalence Probability
(All Non-ambivalence Variables Placed at Mean Value; Positive Values =
Ambivalence Makes Outcome More Likely)
113
Candidate Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Candidate
0
-0.04
0
1
2
3
4
5
Objective Candidate
6
-0.08
-0.12
-0.16
# of Searches
Opponent Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Ambivalence
0
-0.04
0
1
2
3
4
5
Objective Ambivalence
6
-0.08
-0.12
-0.16
# of Searches
Decision Ambivalence
0.16
0.12
Probability
0.08
0.04
Subjective Decision
0
-0.04
0
1
2
3
4
5
6
Obective Decision
-0.08
-0.12
-0.16
# of Searches
Figure 2.5: First Differences for Probability of Reading a Candidate Disagreement
Statement: Maximum Ambivalence Probability – Minimum Ambivalence Probability
(All Non-ambivalence Variables Placed at Mean Value; Positive Values =
Ambivalence Makes Outcome More Likely)
114
Candidate Ambivalence
0.25
Probability
0.15
0.05
-0.05
Subjective Candidate
1
2
3
4
5
6
Objective Candidate
-0.15
-0.25
# of Issues
Opponent Ambivalence
0.25
Probability
0.15
0.05
-0.05
Subjective Opponent
1
2
3
4
5
6
Objective Opponent
-0.15
-0.25
# of Issues
Decision Ambivalence
0.25
Probability
0.15
0.05
-0.05
Subjective Decision
1
2
3
4
5
6
Objective Decision
-0.15
-0.25
# of Issues
Figure 2.6: First Differences for Probability of Correctly Recalling Candidate Issue
Positions: Maximum Ambivalence Probability – Minimum Ambivalence Probability
(All Non-ambivalence Variables Placed at Mean Value;
Positive Values = Ambivalence Makes Outcome More Likely)
115
CHAPTER 3
CAMPAIGN BEHAVIOR:
AN EXAMINATION OF THE 1980-2000 NATIONAL ELECTIONS
In this chapter I draw upon National Election Studies (NES) data from the
1980 to 2000 presidential elections to test the impact of ambivalence on electoral
decisions. These elections have been selected for both practical and theoretical
reasons. Practically, I chose these elections because many of the same questions are
asked in each year, allowing me to create and compare similar models in each
campaign. Theoretically, these elections are useful because they encompass a variety
of campaign types. First, incumbency status varies across these years, there is a
Democratic incumbent in two elections, a Republican incumbent in two elections, and
two elections in which there is no incumbent. These elections further vary in the
types of candidates (e.g. governors v. senators, small state v. large state, southern v.
northern) and ideological placement of the candidates, ranging from the ideologically
polarized 1984 campaign to the more tempered ideological differences of the 1996
election. Second, three of these elections include a challenge by a major independent
candidate, most notably Perot’s challenges in 1992 and 1996, which will be useful in
my examination of abstention. Combined, an analysis of these campaigns provides a
116
good cross section of the possible elections we might see in a given year, ensuring
that any consistent findings in these elections should be robust in other elections.
The analysis in this chapter also provides a nice complement to my
experimental findings. While my experimental analysis afforded me the luxury of
manipulating ambivalence itself, experimental research is often questioned on
grounds of external validity. In regards to my analysis, two concerns are likely to be
salient for many readers. First, there are concerns about population validity; the
extent to which I can generalize the findings from my study to the entire American
electorate. This raises the ubiquitous concern of drawing upon a student sample.
Disciples of this line of criticism argue that the typical American voter is not the same
as the typical college sophomore. I agree. There are (thankfully) many differences
between these two groups. One difference, however, that I do not see between these
two groups is the manner in which they process information. I see no reason to
believe that if I find that an ambivalent college student desires more information
about a candidate before making a decision that an ambivalent union worker, doctor,
or senior citizen would not do the same. No doubt, the cause of these ambivalence
attitudes will differ among groups (and individuals for that matter), but the behavioral
manifestations of these attitudes should not.
The second issue concerns ecological validity, namely the extent to which
behavior in the laboratory is the same as behavior in the “real world.” It is reasonable
to believe that subjects in a study modify their behavior, consciously or
unconsciously, while participating in a study. For instance, the fact that the
participants in my study knew they would eventually vote for one of the two
117
candidates may lead them to scrutinize more carefully the information presented than
they would with information they encounter over the course of an actual campaign.
To alleviate concerns with external validity I step out of the laboratory in this
chapter and draw upon national samples of the American electorate. While this
provides me with a representative sample of the public, it unfortunately does not all
me to test my theory with the same precision as in my experiment. I am nonetheless
able to look at a number of relationships that further test my theory. As I will
demonstrate below, the results from the NES data, by and large, comport well with
the results in the previous chapter. Combined, my findings (along with those in the
following chapter) provide a powerful set of findings that explain how ambivalence
affects electoral behavior.
In the first half of this chapter I examine the effects of ambivalence on
information-seeking behavior. My analysis shows a pattern of results that is largely
consistent with my experimental findings. Specifically, I am unable to find much
evidence to support the information-seeking hypothesis. In the second half of the
chapter I use a series of multinomial logit models to determine the effect of
ambivalence on abstention. I find that candidate ambivalence does not demobilize
the voters and, if anything, makes one more likely to support a candidate. Decision
ambivalence, however, consistently makes one more likely to abstain in an election.
Moreover, I find a positive relationship between decision ambivalence and
independent candidate support. Combined, these findings confirm the basic story
revealed by my experimental analysis.
118
Information-Seeking Behavior
In the last chapter I documented that ambivalence generally does not cause
information-seeking behavior, the one exception being the positive relationship
between subjective decision ambivalence and information acquisition. Instead, I
show that ambivalent individuals are more likely to engage in memory-based
processing than are those who are not ambivalent about the candidates. This is an
important discovery, as it counters those findings that suggest that the ambivalent are
more likely to actively engage their political environment. From the vantage point of
my findings, ambivalent voters are not lauded as ideal democratic citizens who more
thoroughly engage the process; they are not better, but merely different, citizens than
are the non-ambivalent. Of course, one study via one methodological approach does
definitively test a theory. Therefore it is important to verify these claims with
additional data. It is with this goal that I move into the survey world in order to
replicate my experimental results.
To test the information-seeking hypothesis in a survey setting I would ideally
want to conduct a panel study of a representative sample of the American electorate.
Such a survey would include a variety of questions about a respondent’s information
acquisition behavior and include multiple measures of ambivalence, such as measures
of subjective and objective ambivalence. Unfortunately, I am beholden to what is
currently available and to my awareness such a dataset does not yet exist.23 The
National Election Studies (NES), however, do provide data from a number of
23
In the following chapter, I do draw upon a two-wave panel survey of undergraduate students during
the late Spring of 2004 to test the dynamic relationship between ambivalence and information-seeking
behavior.
119
campaigns that satisfy some of these criteria and allow for a number of rough tests of
this hypothesis. First, they provide a representative sample of the American public in
each presidential election. Second, the NES asks a series of open-ended likes/dislikes
questions about each candidate that can be used to derive measures of objective
candidate and decision ambivalence. Third, in several years the NES asks questions
that assess the respondent’s interest and involvement with the election, such as
questions on media use and political discussions. Fourth, although most questions in
an NES survey are asked only in either the pre or the post survey, a handful of
questions are included in both studies. This allows me to see how ambivalence
measured in the pre-election survey is related to changes in responses over time.
Altogether the NES data enables me to conduct a number of tests that nicely augment
my experimental analysis.
Ambivalence and Control Variables
It is now common practice for the NES to ask a series of open-ended
likes/dislikes questions about the two main presidential candidates (and sometimes
about independent challengers) in each pre-election study. Specifically, the NES
asks:
“Now I’d like to ask you about the good and bad points of the major candidates
for president. Is there anything in particular about [Reagan] that might make you
want to vote for him?”
If the respondent answers yes, then she is asked to list a specific reason and is probed
for up to five responses. The respondent is then asked if there are any reasons why
120
she might want to vote against the candidate. The process is then repeated for the
other major contenders. I use the number of reasons to support and oppose each
candidate as a measure of each respondent’s positive and negative feelings about the
candidate. If a person lists three reasons why she would support Reagan and five
reasons to oppose him, then she receives a positive score of three and a negative score
of five. The positive and negative values are inputted into the Griffin formula
discussed in the previous chapter to get measures of candidate and decision
ambivalence.
In order to determine the effects of ambivalence on information-seeking
behavior it is important to control for a number of other factors. First, the typical
socioeconomic (SES) influences are accounted for: age, a five-point scale for
education, sex, minority status, and family income in thousands of dollars. I am
agnostic in my expectations about the effects of age, race and sex on political
involvement, though I expect education and income to be positively related to such
behavior (cf. Brady, Verba Schlozman 1995). Second, party identification is
controlled for with two separate dummy variables, one each for Democrats and
Republicans. Independent leaners are included with their respective party in line with
Keith et al’s (1992) findings that such people behave more like partisans than pure
independents. I also use a four-point folded party identification measure to assess
strength of partisanship. It is necessary to control for strength of partisanship in this
model, as strong partisans, regardless of party, are more likely to be engaged with and
attentive to the campaign. The use of a dummy variable for party identification
allows me to assess the general effects of partisanship while minimizing potential
121
collinearity with the four-point strength of partisanship measure. I expect that
strength of partisanship will be positively related to my measures of political
involvement and information-seeking behavior, while I am agnostic about the effects
of the party dummy variables. Fourth, political sophistication is controlled for
because numerous studies have documented its positive relationship with political
involvement, attention, and information-acquisition over the course of a campaign
(cf. Delli Carpini and Keeter 1993, 1996; Gwiasda 2001; Zaller 1992). Sophistication
is measured via an index of factual political questions in each year, with Cronbach’s
alpha scores for these indices ranging from .69 to .83. I expect it to be positively
related with my dependent variables. Finally, efficacy and trust in government are
also controlled for in each year via an index, with Cronbach’s alpha scores ranging
from .54 to.70 for efficacy and from .56 to .73 for trust in government. Those
individuals who are more trusting of government or who are more efficacious should
also be more likely to pay attention to political affairs; therefore, I expect these
indices to be positively related with my measures of political involvement.
Combined, these variables constitute the standard model for the analysis in this
chapter and any deviation from this model will be noted where appropriate.
Findings
The various media outlets available to the public are perhaps the easiest and
most likely means by which people acquire information about the candidates over the
course of a campaign. Therefore, the first relationship I explore is the effect of
ambivalence on general media use. In each year the NES asks a series of questions
about the media habits of the electorate, such as whether the respondent watched the
122
debates, read newspapers or paid attention to the campaign on television. Drawing
upon these questions I create an index of media use for the six elections from 1980 to
2000 and use these measures as my dependent variable.24 I run an OLS regression
model in each year to examine the relationship between ambivalence and media use.25
I expect that if ambivalence promotes information-seeking behavior in individuals,
then I am likely to find a positive relationship between ambivalence and general
media use.
The results are presented in Table 3.1, where the ambivalence measures are
placed above the dividing line and control variables are listed below. The terms
Democrat, Republican and Independent ambivalence are general names for candidate
ambivalence in each year. For example, in 1980 Democrat ambivalence refers to
Carter ambivalence, Republican ambivalence to Reagan and Independent
ambivalence to Anderson. First, I find that the control variables behave largely as
expected. For instance sophistication and age are positively related to media use in
each year. While not consistently significant across campaigns, minorities, wealthier,
efficacious individuals and those who have higher levels of trust in government also
tend to engage in higher media use. The 2000 election was the first year that the NES
included questions on need for cognition and it shows a positive relationship with
media use, those individuals with a propensity for giving thought to things in general
24
Two things should be pointed out at this point. First, while the exact same questions are not asked in
each year, each index should pick up on the same basic concept – media use. Second, all of the
questions in each year are drawn from the pre-election surveys. Media use questions are asked in four
of the post-elections surveys (1980, 1984, 1996 and 2000), but the same questions are not used in pre
and post surveys. Therefore, for the sake of continuity across election years, I have opted to examine
the pre-election indices in each year.
25
I include a scale for need for cognition as one of the independent variables in the 2000 election, as
this was the first year that the NES included such questions in its survey. This is the only deviation to
the standard control model.
123
are more likely to pay attention to campaign activities in the news. The one
counterintuitive result is the negative relationship between education and media use
in the 2000 election, but this is the only instance in which education behaves contrary
to expectations.
Second, the ambivalence measures provide marginal evidence in support of
the information-seeking hypothesis. Overall, the basic trend shows that candidate
ambivalence is unrelated to media use; the results are insignificant more often than
significant. However, when significant, Democratic and Republican ambivalence are
positively related to media use. For instance, increased feelings of ambivalence about
Clinton or Bush are associated with greater levels of media use in 1992. The
relationship between media use and independent candidate ambivalence is generally
insignificant. Only Perot ambivalence in 1996 is significant, showing a negative
relationship with media use. Turning to decision ambivalence, it is negatively related
with the media measure at the .01 level in four of the six campaigns. Individuals who
are conflicted in choosing between the two party candidates are less likely to pay
attention to the media than are those individuals who are not conflicted about their
candidate preference.26
While the media may be the easiest and most common supply of information,
it is not the only means by which one can gain knowledge in an election. Another
resource that the public might tap during a campaign is their social network of friends
26
Given the near zero value for the decision ambivalence coefficient in 2000 and the addition of the
need for cognition to this model, one might speculate that the need for cognition variable needs to be
controlled in the other years as it influenced the effects of decision ambivalence. In other words, the
earlier models may be improperly specified, as they do not control for need for cognition. This does
not appear to be the case, as dropping the need for cognition variable from the 2000 analysis has no
effect on the results. Moreover, need for cognition and decision ambivalence have only a negative .14
correlation.
124
and family. Fortunately the NES often includes questions about the extent to which
respondents engage in political discussions. In 1984 and 1992 respondents are asked
both how often they generally discuss politics with their friends and family, ranging
from never to every day, and the number of discussions they specifically had in the
past week. For these campaigns I add the responses to these two questions together
and create a discussion index as my dependent variable.27 My dependent variable in
1984 and 1996 is a single question that asks respondents how often they generally
discuss politics over the course of a week.28 Using these variables, I run OLS
regression in each year and present the results in Table 3.2.
As before, the control variables behave as expected, showing that
sophistication, efficacy and party strength are all strongly positively related to the
frequency of discussions. More importantly, the ambivalence measures show the
same pattern of results as found for media use. First, Republican and Democratic
candidate ambivalence is significant in half of the instances, and positive in these
cases. Second, Independent ambivalence is insignificant in 1992, but significantly
negative in the 1996 campaign. Finally, decision ambivalence consistently shows a
strong negative influence on how often a respondent discusses politics, attaining
significance at the .05 level in 1984 and .01 level in the other three elections. Those
individuals who are conflicted about their vote choice are not only less likely to use
the media, but are also less likely to talk politics with their friends and family.
27
Cronbach’s alpha equals .7170 and .7101 for 1984 and 1992 respectively.
The questions in 1984 and 1988 come from the pre-election surveys, while the 1992 and 1996
questions come from the post-election surveys.
28
125
Together, the results from the media use and discussion models provide
limited evidence in support of the information-seeking hypothesis. In general,
candidate ambivalence is unrelated to one’s engagement with the campaign via the
media or political discussions. Decision ambivalence is consistently significant, but
shows a negative relationship with media use and discussions. Of course, it is
important to keep in mind that these are not optimal tests of the information-seeking
hypothesis, namely as they are unable to examine the dynamic process between
ambivalence and information acquisition. A better test examines the how
ambivalence is related to changes in behavior. Fortunately, there are a few questions
in the NES that can be used to assess changes in behavior from the pre to post survey.
As noted earlier, it has been shown that ambivalence is positively related to
accurately placing candidates on issue scales (Meffert, Guge and Lodge 2004). One
explanation for this finding is that the ambivalent hold more accurate information on
the candidates because they are more likely to seek out information on the candidates.
As argued in the first chapter, I demonstrate that this need not be the case. It is not
necessary to acquire more information in order to recite accurate information; it is
merely necessary to remember the information one encounters. Consider two
students who study for a test. Both students may spend five hours studying, yet both
will not necessarily receive the same grade. One student may find it easier than the
other to retain the information from the previous night’s study session. Thus, a useful
test of whether ambivalence promotes information acquisition is not whether it is
related to accuracy itself, but rather if is related to increases in accuracy or certainty
over the course of a campaign.
126
It is now standard practice for the NES to ask respondents to place each of the
major candidates on a series of seven-point scales to measure the candidates’ issue
positions. For example, one might be asked to place a candidate on a government
services scale, where one end of the scale means government should provide many
fewer services and reduce spending and the other end that government should provide
many more services and increase spending. Previous studies have used these
questions to assess the accuracy of the issue information respondent’s hold about the
candidates (cf. Alvarez 1997; Meffert, Guge and Lodge 2004). Accuracy is defined
as the absolute value of the deviation between a respondent’s placement of the
candidate on the scale and the mean value of all respondents’ placement of the
candidate on the scale, where smaller numbers indicate greater accuracy. If a person
places Bush as a 7 on the scale and the mean score of the whole sample for Bush is
6.2, then this person would receive a score of .8 on this scale.
Unlike previous studies I recode each of the deviations on a 7-point scale so as
to account for one’s relative accuracy standing, rather than focus on the value of the
deviation itself. One of the problems with using the deviations is that a respondent
could provide the most accurate placement of the candidate in each survey, yet have a
larger deviation in the post-election survey than in the pre-election survey. For
example, consider the case in which Bush receives mean ideology scores of 3.3 and
3.4 in the pre and post surveys respectively. In each survey, placing Bush as a 3 on
the scale is the most accurate response available to a respondent; yet, in doing so, we
would say that this person misplaced Bush by .3 and .4 points in the pre and post
surveys respectively, a .1 decrease in accuracy. Conversely, a person who places
127
Bush as a 4 in both surveys, the second most accurate placement, would show an
increase in accuracy over this period by .1 points. Rather than penalize (or reward)
respondents for these potential problems, I opt to examine changes in one’s relative
position. Thus, a person who provides the most accurate placement of a candidate in
either survey, regardless of the actual value, is coded as a zero, a person who provides
the second best response is coded as a one and so forth. This recoding does not
substantively alter the findings reported below, but it makes it intuitively easier to
examine changes in accuracy.
While I have used the term accuracy in my discussion so far, the use of
deviations from the mean has also been referred to as uncertainty (e.g. Alvarez 1997)
in various studies. The idea of thinking of it as uncertainty comes from the game
theoretic notion that people who have less information about a candidate are more
uncertain about the candidate, but others prefer to refer to this simply as accuracy.29
Given these differing perspectives I examine changes in two dependent variables,
where the difference between them is a distinction on how non-responses are coded.
I define accuracy as the deviation from the mean and simply code all non-responses
as missing data. My uncertainty measure is largely the same as accuracy, but in
accordance to Alvarez’s approach, I recode non-responses as maximally uncertain.
Alvarez justifies this coding decision by asserting that people who are simply unable
to place the candidates on the scale have the least amount of information, are highly
29
One reason the term accuracy is preferred is that it allows for people to be certain in their
inaccuracy. For instance, Kerry’s true stance on an issue may be a five, but a person could be
convinced that Kerry is a one, and will maintain this belief regardless of any contrary evidence that is
provided. This person is subjectively 100% certain that Kerry is a one, while simultaneously being
objectively incorrect about Kerry’s position.
128
uncertain, about the candidates. Thus, the two measures are identical, except in the
how they treat non-responses.
While the NES asks many placement questions in each condition, it
unfortunately does not tend to ask about the same issues in both the pre and post
election surveys. Nonetheless, there are a few exceptions in which the same
questions have been asked in both waves of the survey. Drawing upon these items, I
assess changes in accuracy and uncertainty over time. Before turning to the analysis,
it is necessary to discuss briefly the methodological issues that occur when using
panel data. A key issue in panel data analysis is controlling for any possible negative
feedback in the system under investigation. Negative feedback essentially refers to
the problem of regression to the mean, where unusually large values of a measure are
often followed by unusually small values, and vice-versa. When such a problem
exists, this makes it more difficult to find significant relationships, even when they in
fact exist. One means of controlling for this problem is to include a lagged value of
the dependent variable as one of the independent variables (see Finkel 1995, for more
discussion on this problem).30
30
The extent to which this will fix the problem, however, is conditional upon the amount of
measurement error present in the variable. Simply stated, the more error exists, the less useful it will
be for controlling negative feedback problems. Unfortunately, there is no way to objectively determine
how much error exists in my accuracy and uncertainty measures, as we cannot say for certain precisely
where the candidates stand on these issues. Nonetheless, these measures have been used in a number
of studies (e.g. Alvarez 1997, Meffert Guge and Lodge forthcoming) and have been shown to be valid
indicators of accuracy and uncertainty (see Alvarez 1997, pp 69-75 for a discussion on the validity of
these measures). Thus, while I cannot claim that there is no measurement error in my measures, it is
reasonable to infer that they arenot plagued by a notably large amount of error.
129
Therefore, I regress post-election scores on pre-election scores, the
ambivalence measures, and the control variables used in the above analysis.31 This
model allows me to determine whether ambivalence recorded in the pre-election
survey is associated with increases or decreases in accuracy or uncertainty over time.
Thus, a positive coefficient for a variable indicates that the variable is associated with
increased inaccuracy or uncertainty (i.e. larger errors), or, in other words, with
decreased accuracy or certainty. The information-seeking hypothesis predicts
negative ambivalence coefficients (i.e. improved accuracy or certainty).
In the 1980 election, the NES only asked about candidate ideological
placement in both the pre and post election surveys. I present the results for the
accuracy models in Table 3.3, where the three columns provide the results for Carter,
Reagan and Anderson ideological placement respectively. Thus, the first column
examines shifts in Carter accuracy. For example, the -.507 coefficient for Democratic
party identification indicates that, on average, being a Democrat is associated with a
half-point increase in accurately placing Carter on the ideology scale. First, none of
the ambivalence measures are significantly related to changes in Reagan accuracy.
The results from the Carter and Anderson models, however, support the information-
31
There are two other issues that should be considered when using panel data – one methodological
and one theoretical. First, on the methodological side, there is the possibility of a reciprocal causal
relationship between the variables. That is, not only may ambivalence cause people to seek out
information, but the process of seeking out information may cause people to become ambivalent. If a
reciprocal process exists, then the use of OLS can yield biased and inconsistent errors. Unfortunately,
the data here does not permit me to test for this process. However, I do test for such a possibility with
the panel data used in the next chapter and find no evidence of a reciprocal process. The second
problem is whether one includes ambivalence variables measured in the pre or post election survey.
This is a theoretic, and not methodological, question. Unfortunately, ambivalence is only measured in
the pre-election survey, thereby dictating model choice in this case. I discuss both of these issues in
the next chapter, where I have data better suited to account for these issues. The evidence from that
analysis suggests that there is no reason to question the findings presented in this chapter.
130
seeking hypothesis. First, as indicated in the first column, Carter ambivalence leads
to increased accuracy (i.e. smaller deviations). Similarly, the final column shows that
Anderson ambivalence likewise produces more accurate ideological assessments. In
sum, the models for Carter and Anderson suggest that the ambivalent come to hold
more accurate information about the candidates over this period of time. Finally, and
in contrast to candidate ambivalence, decision ambivalence is positively related to
inaccuracy when placing Carter in the ideological scale; it is insignificant in the other
two models. The results for the uncertainty models are presented in Table 3.4 and
reveal no notable differences from the accuracy models. While Anderson and
Decision ambivalence are no longer significant, Carter ambivalence is associated with
increased certainty.
The 1996 NES also asks respondents to place Dole and Clinton on the
ideological placement scale as well as place Dole, Clinton, and Perot on the aid to
blacks scale in both surveys. The results for the accuracy and uncertainty models are
presented in Tables 3.5 and 3.6. Simply stated, none of the ambivalence measures are
significantly related to changes in accuracy or uncertainty during this period of time.
These results provide no support for the information-seeking hypothesis.
Lastly, I examine the 1984 NES dataset. In 1984, the NES includes by far the
greatest number of identical issue placement questions in both surveys. Specifically,
in both waves it asks respondents to place the two candidates on four scales: (1)
ideology, (2) government services, (3) policy towards Central America, and (4)
guaranteed jobs. In addition to these four questions, I examine the mean accuracy
and uncertainty score on these 4 questions for both the pre and post election surveys.
131
The results for Reagan accuracy and uncertainty are presented in Tables 3.7 and 3.8.
The dominant pattern of results is the general lack of significance among the
ambivalence measures. First, Reagan ambivalence is insignificant in every model. In
other words, ambivalence felt about the object (Reagan) is unrelated to information
about the object. The only significant candidate ambivalence measure is the negative
relationship between Mondale ambivalence and both accuracy and uncertainty scores
for the government services question. Mondale ambivalence is related to increased
accuracy and certainty when placing Reagan on this issue. Likewise, decision
ambivalence is generally insignificant. The one exception is that it is related to
increased accuracy and certainty when placing Reagan on the general ideology scale.
The results for Mondale accuracy and uncertainty are presented in Tables 3.9
and 3.10. As before, ambivalence about the object (Mondale) is unrelated to changes
in accuracy or uncertainty about the object. While there are a few instances in which
Reagan ambivalence (uncertainty on the Central America issue) and decision
ambivalence (ideological accuracy and Central America uncertainty) are significant,
the overwhelming pattern is the lack of significance among the ambivalence measures
and Mondale placement.
As a final test of the information-seeking hypothesis I examine changes in
general interest in the campaign. In each presidential campaign from 1980 to 2000
the NES has asked virtually the identical question in the pre and post surveys to
assess general interest in the election. Specifically, it asks (the words in parentheses
indicate the changes in pre and post survey wording respectively):
132
Some people don'
t pay much attention to political campaigns. How about you?
Would you say that you (have been/were) very much interested, somewhat interested or
not much interested in (the political campaigns so far/following the political campaign)
this year?
Drawing upon these questions, I examine changes in interest by regressing postelection interest on pre-election interest, ambivalence, and the control measures.32 The
results are presented in Table 3.11.
First, in line with the accuracy and uncertainty findings, I find that
candidate ambivalence is largely unrelated to changes in interest from the pre to post
election surveys. Of the 15 candidate ambivalence coefficients, only two are significant.
In the 1980 election, Anderson ambivalence is negatively related to increased interests,
indicating that individuals who are ambivalent about Anderson, on average, become less
interested in the campaign. Conversely, in the 1992 election, ambivalent feelings
towards Bush are related to increased interest in the campaign. Thus, not only is
candidate ambivalence generally insignificant, there is no consistent pattern among the
few significant findings that exist. The impact of decision ambivalence, however, is
unambiguous. Decision ambivalence is related to decreased interest in the campaign, as
indicated by the significant negative coefficients in the five of the six years. Those
individuals who are conflicted in choosing between the Democratic and Republican
candidate are more likely to lose interest in the campaign during this time.
Altogether, the NES results consistently provide little to no evidence in
support of the information-seeking hypothesis. First, drawing upon one-shot crosssectional data, my results indicate that candidate ambivalence is generally not related to
32
While this is a three category dependent variable, I have opted to use OLS regression as it is easier to
directly interpret its coefficients. I have analyzed each of the models using ordered probit procedures
and the results do not substantively differ.
133
media use or political discussions, whereas decision ambivalence showed a consistent
negative relationship with these measures. Second, these findings are verified when I
draw upon the available panel data. The results from the accuracy, certainty, and
interest questions likewise tend to show no relationship between ambivalence and
increased accuracy or certainty about the candidates’ positions.
Abstention
In this half of the chapter I turn my attention to decisions to abstain in an
election. While elections are typically depicted as battles between the candidates for the
public’s support, this is only half the story. A candidate must not only get the public to
prefer him or her over the other candidate(s), but also needs the public to record this
support at the ballot booth. When voters are conflicted (i.e. ambivalent) in their
assessments of each individual candidate and/or in choosing between them, then they
may be less motivated to vote in the election. While they may have thoughts about the
two (or three) candidates for office, these thoughts may pull them in multiple directions
and prevent them from elevating one of the candidates as their preferred choice. It is
therefore important to determine if ambivalence is related to the decision to abstain on
Election Day.
The first question is therefore how to incorporate the abstention decision into
my analysis. One option is to model the vote decision as a two-step process; an
individual first decides whether to vote and then chooses among the available
candidates (Dubin and Rivers 1989, Born 1990). This option, however, is
problematic because it fails to account for the fact that campaign dynamics
themselves likely determine whether or not a person opts to vote in the election.
134
Given the amount of information and learning that occurs within the context of a
campaign itself, it is unlikely that people first simply decide whether to vote before
even learning about their options. A more realistic process is that people come to
learn about the candidates during a campaign and then, based upon this information,
determine whether to abstain or support one of the candidates running for office.33 A
better option is to include and model abstention as one of the choices available to the
voters on Election Day (e.g. Lacy and Burden 1998). Thus, the dependent variable
for my analysis is a discrete choice variable that includes options for voting for the
two or three main candidates on the ballot as well as abstention.
The model used in this analysis is largely the same as that used in the first half
of the chapter, except that economic evaluations are now included.34 It is well
established that economic assessments influence voting behavior, namely that the
incumbent party fares better when the economy is perceived to be doing well (Fiorina
1981, Kinder and Kiewiet 1981; Markus 1988). To control for this effect, I include
each respondent’s retrospective evaluations of both one’s personal and national
economic conditions.
In line with previous research that shows a negative relationship between
candidate ambivalence and candidate evaluations (e.g. Lavine 2001; McGraw et al
2003), I expect that ambivalence towards an individual candidate will make a person
33
Another reason why this dynamic approach is more realistic is that a number of people may opt not
to vote based upon their own perceptions of the closeness of the race, either in their own state or
nationally. That is, a person who might prefer one candidate may opt not to take the time to vote if
their preferred candidate is virtually certain to win/lose in this person’s state. A two-step process for
modeling vote decisions would fail to account for this decision.
34
Each of the voting models has also been conducted by including each candidate’s feeling
thermometer scores to control for each respondent’s general attitudes towards each candidate. The
results do not substantively differ under this model specification.
135
less likely to vote for that candidate. I further expect that this decreased motivation to
support the candidate will make many individuals more likely to abstain in the
election. For example, holding all else constant, if someone is ambivalent about
Clinton in the 1992 election, then this person should become more likely to prefer
staying at home on election day than to vote for either Clinton or Bush. In other
words, while research has shown that ambivalence leads to negative evaluations of an
individual candidate, this negative evaluation of candidate X does not necessarily
generate support for candidate Y. Instead, this person should prefer to abstain from
the process altogether.
Similarly, I expect that decision ambivalence will also increase the likelihood
of abstention in lieu of supporting either candidate. For instance, when one is
conflicted about her choice between Kerry and Bush in 2004, she may prefer to avoid
resolving this internal conflict by simply opting not to vote. In this case, this person
prefers abstention to voting for either candidate. The addition of independent
challengers adds another level of complexity to the process. Ambivalent individuals
are not disinterested members of the electorate who abstain from elections due to
their apathy about the process. Ambivalent individuals abstain because of their
inability to resolve their conflict towards the two major party candidates. Thus, if an
independent candidate is available, then ambivalent people should be more willing to
select this “other” option and participate in an election. Therefore, I expect that,
when available, decision ambivalence will increase the likelihood of voting for an
independent candidate.
136
Results
~1980
The multinomial logit results for the 1980 election are presented in Table
3.12. In each election year the omitted category is abstention. The bottom of the
table provides the general fit measures and shows that this model correctly classifies
roughly 65% of the cases. A model that always predicted the modal outcome of a
Reagan vote would be accurate about 40% of the time and this model reduces the
number of errors one would make from this baseline prediction rate by roughly 42
percent.
Turning to the independent variables, the coefficients for each column
determine whether the independent variable makes one more or less likely to vote for
that candidate in relation to abstaining in the election. For instance, the positive and
significant coefficients for income and sophistication for each of the candidates
indicate that these people are all more likely to vote for any candidate than they are to
stay at home on Election Day. The coefficients, however, do not demonstrate if a
variable increases the likelihood of voting for one candidate vis-à-vis the other
candidates. Rather than attempt to discuss how each variable affects one’s decision to
vote for an individual candidate versus abstaining, I compute the first differences for
each variable to show how each variable simultaneously affects the probability of
falling into each possible outcome. This makes it easier to determine simultaneously
how each variable affects the probability of each outcome in relation to the other
three options. Specifically, I determine the change in probability that occurs from a
shift in each independent variable from its minimum to maximum value, holding all
137
other values at their mean.35 The results are presented in Table 3.13 and each column
displays the change in probability of the outcome occurring given the shift in the
independent variable. For instance, the .06 value in first row indicates that being
female significantly increases the likelihood of voting for Carter by .06.
The first column of Table 3.13 shows, as expected, that income, age,
education, partisan strength, sophistication, and efficacy are all negatively and
significantly related to abstention. This table also reveals that Reagan and Carter see
the largest gains in probability of support among these voters. For instance, a
movement in age from its minimum to maximum values increased the likelihood of
voting for Reagan by .272 while increasing Carter’s support by .17 (Anderson’s
support decreases by -.082 as a result of this shift). Carter has a slight comparative
advantage among the sophisticates, seeing an increase in the likelihood of his support
by .271, to increases of approximately .2 and .1 for Reagan and Anderson
respectively. The variables where the Reagan and Carter see the most direct tradeoffs are race, party identification and economic perceptions. Being a minority
increases the probability of voting for Carter by about one-quarter, while decreasing
the likelihood of supporting Reagan by the same amount. Not surprisingly,
Democrats are significantly more likely to support Carter and less likely to support
Reagan, while the converse is true among Republicans. Finally, those individuals
35
The results have been generated via King et al.’s 2001 Clarify program. This program calculates
predicted probabilities by first simulating 1000 predicted probabilities by accounting for the variance
in each coefficient to simulate a number of predicted probability outcomes. A 95% confidence interval
is calculated for each first difference by reporting the values of the 25th and 975th simulated difference
predictions. First differences are deemed significant at the .05 level when the values of these two
predictions have the same sign; that is we can be 95% confident that the true effect of changing the
values of the independent variable are positive or negative when the 25th and 975th values are both
positive or negative respectively.
138
who believed the nation’s economic situation had worsened over the past year are less
likely to support Carter and more likely to vote for Reagan.
The bottom four rows of Table 3.13 show the effects of the ambivalence
measures on individual vote decisions.36 Contrary to my expectations, these results
indicate that ambivalence towards a major party candidate significantly and sizably
increases the likelihood of supporting that candidate while significantly decreasing
support for one’s opponent. For instance, moving ambivalence towards Reagan from
its minimum to it maximum yields a .62 increase in the probability of voting for him
and a .2 decrease in the probability of a Carter vote. The effects of Carter
ambivalence are essentially the same in relation to Carter support, namely a
comparable shift in Carter ambivalence produces a .5 increase in the likelihood of a
Carter vote. Finally, increased ambivalence about Carter or Reagan also decreases
the likelihood of supporting Anderson. In a word, these results indicate that
ambivalence towards a major party candidate does not diminish support in the ballot
booth, but rather improves the candidate’s chance for electoral success. The effect of
candidate ambivalence on abstention also counters my initial expectations. I find that
ambivalence towards Reagan or Carter decreases the likelihood that one will abstain
in the election. For both of the candidates the probability of abstaining drops by
about a third when candidate ambivalence moves from its minimum to its maximum.
In sum, far from demobilizing voters and diminishing candidate support, candidate
36
It should be noted that findings presented below are robust under a number of model specifications.
Specifically, I have examined 3 or 4 additional models in each year, namely the control variables plus:
(1) Democratic ambivalence, (2) Republican ambivalence, (3) Democratic and Republican
ambivalence, and (4) Democratic, Republican, and Independent ambivalence (when appropriate). The
effects of ambivalence do not notably differ in each of these models.
139
ambivalence tends to increase candidate support to the detriment of one’s opponent. I
will return to the implications of this finding at the end of the chapter.
In contrast to candidate ambivalence, I find that decision ambivalence behaves
in accordance with my theoretical expectations. First, increased ambivalence about
one’s decision produces a notable .59 decrease in the probability of voting for
Reagan. Although the decrease in Carter support is not significant, it should be noted
that probability of voting for Carter is generally much lower than voting for Reagan.
For instance, when decision ambivalence is set to its minimum, the probability of
voting for Carter and Reagan is .23 and .70 respectively. The probability of a Carter
or Reagan vote drops to .10 for each candidate when decision ambivalence is placed
at its maximum. Thus, the insignificant effect on Carter support may be the product
of Carter’s already low level of support in the campaign. Significance aside, in each
case, a shift to maximum decision ambivalence leaves both candidates with about a 1
in 10 chance of voter support. In other words, individuals who experience maximal
decision ambivalence are unlikely to vote for either candidate. Second, I also find
that increased decision ambivalence increases both the likelihood of abstaining and
voting for Anderson. Moving decision ambivalence from its minimum to its
maximum yields a .41 and .31 increase in the probability of abstaining and voting for
Anderson respectively. Simply stated, while a number of people who experience a
high degree of conflict about their vote choice decide not to participate in the
election, a sizable portion of such people supported Anderson.
140
~1984
The multinomial logit results for the 1984 election are presented in Table 3.14
and the first difference in Table 3.15. There is no notable change in the behavior of
the control variables and I will skip discussing their impact on voting behavior.37
First, unlike in the 1980 election, I find virtually no significant relationships between
any of the ambivalence measures and voting behavior. The only significant effect is
the .3 increase in the likelihood of voting for Reagan when Reagan ambivalence is
moved from its minimum to its maximum. This finding is consistent with the 1980
results which also shows that candidate ambivalence increases candidate support. Of
course, this again refutes my initial expectations about the detrimental impact of
candidate ambivalence for the candidate. Finally, decision ambivalence is not
significant, though the direction of its effect is consistent with my expectations.
Increased decision ambivalence is positively related to abstention (albeit
insignificantly).
~1988
The results for the 1988 election are presented in Tables 3.16 and 3.17
respectively. First, I again find that candidate ambivalence does not influence
abstention decisions, but rather increases the likelihood candidate support. For
instance, a shift from minimum to maximum Bush ambivalence produces a .25
increase in the probability of Bush support; the equivalent shift in Dukakis
ambivalence increases the probability of voting for Dukakis by roughly a third.
Finally, whereas candidate ambivalence did not affect turnout, decision ambivalence
37
The impact of the control variables do not notably change in any of the elections investigated. Thus,
for the sake of brevity, I will no longer discuss them when examining my findings.
141
increases the likelihood of abstention. A full shift in the decision ambivalence scale
increases the probability of staying at home on Election Day by about two-fifths.
People who are conflicted about a candidate are not demobilized, but rather are more
likely to support the candidate. In contrast, people who are ambivalent in choosing
between the two candidates are simply more likely to abstain.
~1992
The results for the 1992 election are found in Tables 3.18 and 3.19. First,
candidate ambivalence is unrelated to abstention, but again increases the probability
of candidate success. Although neither Bush nor Perot ambivalence is related to
Election Day behavior, Clinton ambivalence decreases the likelihood of a person
casting a vote for Bush. That is, a shift in Clinton ambivalence from its minimum to
its maximum is associated with a .17 decrease in the probability of voting for Bush.
Whereas candidate ambivalence in the previous elections was generally positively
related to candidate support, it is negatively related to opponent support in this case.
Ultimately, however, the result is the same; Clinton ambivalence enhances his
electoral fortunes by diminishing support for Bush
Turning to decision ambivalence, I find that it is positively linked to
abstention. A full shift along the decision ambivalence scale produces roughly a .2
increase in the probability of staying at home on Election Day. This shift has no
impact on the likelihood of casting a Bush vote, but drops the probability of a Clinton
vote by near two-thirds. As was the case in 1980, increased decision ambivalence
raises the likelihood of supporting an independent candidate on the ballot.
Specifically, the probability of supporting Perot when decision ambivalence is at its
142
minimum is roughly .06, but increases nearly six-fold to .36 when set to its
maximum. Thus, those individuals who are conflicted in their choice between
Clinton and Bush are generally less likely to support either candidate. Instead, these
people are more likely to abstain or back Perot’s candidacy.
~1996
The findings for the 1996 election are presented in Tables 3.21 and 3.22 and
generally accord with the earlier results. First, Dole and Perot ambivalence are
unrelated to either abstention or support for any candidate. Second, Clinton
ambivalence increases the chance of a Clinton vote while decreasing the likelihood of
abstention. For example, a shift in Clinton ambivalence from its minimum to its
maximum increases the probability of a Clinton vote by about a third and decreases
the likelihood of abstention about a fifth. Thus, for Clinton at least, candidate
ambivalence again works to the benefit of the candidate.
The effect of decision ambivalence is nearly identical to what was found in
the 1992 campaign. The first differences reveal that probability of supporting either
Dole or Clinton drops by about one-quarter to one-third as decision ambivalence
increases. In turn, this increase raises the likelihood of either abstaining or casting a
vote for Perot. The probability of abstaining moves from .10, when decision
ambivalence is at its minimum, to just over .5, when placed at its maximum.
Similarly, Perot support moves from a near-zero probability of .025 to .16 when one
moves from minimum to maximum decision ambivalence. Overall, I again find that
while many people who are conflicted about their vote choice decide not to abstain,
others opt to back Perot in the election.
143
~2000
Lastly, I examine the 2000 campaign and present the results in Tables 3.22
and 3.23. First, neither Bush nor Gore ambivalence is significantly related to
candidate support, though the direction of the effects shows that candidate
ambivalence is positively related to candidate support. Additionally, although Bush
ambivalence is unrelated to abstention, the more ambivalence one feels about Gore,
the less likely one is to abstain. Finally, as before, I find that increased decision
ambivalence significantly increases the likelihood of abstention. The probability that
a respondent abstained in the election increased by roughly .4 when decision
ambivalence shifts from its minimum to its maximum value.
Alternative Explanations for Abstention
Overall, the effects of decision ambivalence behave as expected. First, it is
positively related to abstention in five of the elections and is in the correct direction in
the other year. Further, it is also positively related to support for independent
candidates in each of the three relevant elections (1980, 1992, and 1996). This shows
that when available many people who are conflicted about their decision between the
two major party candidates will participate in the election by supporting a third party
candidate rather than stay at home. However, when no third party candidate is
available, such as in 2000, then these people are simply more likely to opt out of the
electoral process via abstention. Together, these findings suggest that a number of
people who stay at home during strictly two-party competitions would instead prefer
144
to participate in our elections, but fail to do so as they see no viable option on the
ballot.
There are, however, two other explanations for the decision ambivalence
findings that should be considered. First, there is always the risk in cross-sectional
analysis of reversing the causal process or picking up a spurious relationship.
Perhaps it is the case that those individuals who are most likely to develop conflicted
attitudes are the same people who are likely to abstain. From this perspective, it is
not decision ambivalence that drives abstention, but rather some other factor that
drives both decision ambivalence and motivation to abstain. If such a process is at
work, then it should also be the case that those individuals who experience high
decision ambivalence in one year will be less likely to vote in any presidential
elections. Fortunately, in five of the six surveys from 1980 to 2000 the NES also asks
respondents whether they recall voting in the previous presidential election. Using
this question I conduct logistic regression analysis to test this relationship and present
the results in Table 3.24. Quite simply, I find that in every year there is no
relationship between decision ambivalence and reported participation in the previous
election. The positive relationship I find between decision ambivalence and
abstention in each election year, therefore, is not driven by some general
predisposition to abstain in elections by the respondents.
It is also important to consider another explanation for the relationship
between decision ambivalence and independent candidate support. It is possible that
independent challengers, in the process of running for the presidency, raise a variety
of issues and considerations that creates higher levels of decision ambivalence for the
145
voters during these campaigns. From this perspective the reason I find a positive
relationship between decision ambivalence and independent candidate support is that
the factors that lead to support for independent candidates raises a set of consideration
that also leads to decision ambivalence. Simply stated, the causal process may be
reversed in these types of elections as independent challengers may create decision
ambivalence. If true, then I should find that there is a higher level of decision
ambivalence in elections in which independent candidates are present. To test this
proposition, I ran a simple difference of means test to determine if the level of
decision ambivalence in 1980, 1992 and 1996 is higher than in 1984, 1988 and 2000.
The results from this test are presented in the top half of Table 3.25 and show that the
mean level of decision ambivalence in these two types of elections is statistically
indistinguishable (p = .17). The bottom half of the table presents the mean and
confidence interval for decision ambivalence in each election year. These results also
show that although the level of decision ambivalence does vary across election years,
it does not appear to be a function of the number of candidates in the election. The
relationship between decision ambivalence and independent candidate support is not
driven by independent candidates creating such ambivalence among the electorate.
Discussion
Contrary to my initial expectations I find that candidate ambivalence does not
increase the likelihood of abstention, but rather it is unrelated to turnout decisions. In
none of the election years do I find a positive relationship between candidate
ambivalence and abstention. If anything, I find that candidate ambivalence may
increase the likelihood that one votes in the election. Moreover, my results also show
146
that candidate ambivalence often increases a candidate’s chance of electoral success.
So this raises an important question. How do I reconcile this finding with those
studies that show a negative relationship between ambivalence and feeling
thermometers scores (cf. Lavine 2001, McGraw et al., 2003)? The simple answer is
to simply assert that past studies use feeling thermometer scores to assess candidate
evaluations which do not account for the relative advantage one candidate has over
another. Ambivalence about Clinton may lead one to view him less positively on a
feeling thermometer scale, but may do nothing to diminish one’s desire to support
Clinton over his Republican challenger in the ballot booth.
A better explanation requires that we look more closely at who develops
ambivalent attitudes about a candidate. People likely acquire more information and
pay more attention to candidates that they initially are inclined to support. Voters
who start the campaign by favoring the Democratic candidate, whether slightly or
strongly, are more likely to pay attention to information about that candidate during
the campaign, and vice-versa for Republican supporters. The more attention people
pay to a candidate, the more they will be exposed to information, both positive and
negative, about the candidate. This in turn will increase the likelihood of feeling
ambivalent about the candidate, but not necessarily about feeling ambivalent about
one’s vote choice. Thus, if supporters are more likely to develop ambivalent attitudes
about their own candidate, then this would help explain the positive relationship
between candidate ambivalence and candidate support.
The initial evidence does, in fact, support such a process. To determine who
develops ambivalent attitudes, I regress candidate ambivalence on the variables used
147
in the above models. I generally find that partisans tend to hold ambivalent attitudes
about their own party’s candidate. Specifically, the Republican coefficient is
positively and significantly linked to Republican candidate ambivalence in three of
the six year and is insignificant in the other three years. The Democratic coefficient
is positively linked to Democratic candidate ambivalence in four of the campaigns
and is insignificant in the other two years.38 In short, Democrats are more likely to
view Democratic candidates ambivalently and Republicans are more likely to view
Republicans ambivalently.
In addition to examining who is more likely to develop ambivalent attitudes, it
is also informative to examine the content of these ambivalent attitudes. Recall that
ambivalence is defined as the presence of both positive and negative considerations.
While ambivalence in its purest form results from an equal number of positive and
negative feelings, many people will develop ambivalent attitudes that are skewed in
the positive or negative direction. For example, consider two people. The first
person has 4 positive and 2 negative considerations about Kerry, whereas the second
person has 2 positive and 4 negative considerations. In terms of their ambivalence,
both of these people are defined as equivalent. Yet, clearly differences exist between
these two people, as the former is skewed positively towards Kerry and the latter is
skewed negatively. Likewise, a person who experiences no ambivalence about Kerry
can do so by either having 5 positive and 0 negative considerations or 0 positive and 5
negative considerations. Again, while both people experience the same amount of
ambivalence, namely none, they do so for different reasons. More importantly, the
38
The one counter finding is that Democrats are more likely to hold ambivalent attitudes about Bush in
the 2000 campaign, while the Republican variable is insignificant.
148
former is strongly inclined to support Kerry and the latter strongly inclined not to do
so. Thus, given the differences in how ambivalence may materialize, it is useful to
see if such skewed distributions exist among the public.
To answer this question, I examine the ratio of likes to dislikes among the low
and high ambivalent respondents. First, I examine the distribution for those
individuals who have low ambivalence, specifically those with negative Griffin
scores (where negative scores indicate that a person generally views the candidate
either favorably or unfavorably). I compute the percentage of the low ambivalent
respondents who have a greater number of dislikes than likes, or, in other words, the
percentage of people who view the candidate negatively. The results are graphed and
presented in Figure 3.1 and show that low ambivalence scores tend to be the product
of attitudes that are skewed towards an unfavorable assessment. For example, in
1980, roughly 60% of the people with low Carter or Reagan ambivalence scores
reported more dislikes than likes about the candidate.
Next, I examine the ratio of likes to dislikes for those people with high
ambivalence scores (positive Griffin values), excluding those people who have an
equal number of likes and dislikes. In this case, I calculate the percentage of people
who have a greater number of likes than dislikes and present the results in Figure 3.2.
As these results show, people who view a candidate ambivalently are more likely to
report a greater number of likes than dislikes. For example, in 2000, roughly 60% of
those who viewed Gore or Bush ambivalently reported a higher number of openended likes than dislikes. Combined, these results help explain why I find a positive
relationship between candidate ambivalence and candidate support. Specifically,
149
those people who report low levels of ambivalence tend to view the candidate
unfavorably, while those who experience ambivalence are more likely to lean towards
a favorable candidate rating. Thus, given the negative bias among the low ambivalent
group and positive bias among the high ambivalent group, it is again not surprising
that I find a positive link between candidate ambivalence and candidate support.
In contrast to the candidate ambivalence results, my findings for decision
ambivalence provide strong evidence to support my theoretical expectations. In five
of the six elections I find a strong positive significant relationship between decision
ambivalence and abstention. Individuals who have conflicted feelings in regards to
which candidate to support in the campaign are more likely to sidestep the decision
by simply opting not to vote. Moreover, there is no relationship between decision
ambivalence and one’s self-report for voting in the previous presidential election.
This indicates that the relationship I find between decision ambivalence and
abstention is not driven by some general predisposition among these individuals to
abstain in elections.
The effect of decision ambivalence becomes even more interesting when one
expands the Election Day choice set to include independent candidates. In these
campaigns there is a strong positive relationship between decision ambivalence and
independent candidate support. This indicates that while many people will abstain
when conflicted between the two major party candidates, a number of these people
will instead throw their support behind the independent challenger. Of course this
option is not always available to the electorate. This implies that in every campaign
there is a segment of the population who wants to participate in our democratic
150
elections, but often do not because of a lack of candidates that represent their own
preferences. Scholars should continue to pursue and clarify the relationship between
decision ambivalence, abstention and independent candidate support. For instance,
are there systematic differences among those individuals who have high levels of
decision ambivalence and opt to abstain versus those who opt to support independent
challengers? Are decisionally ambivalent voters supporting independent candidates
because these candidates represent their own issue preferences or merely as protest
votes against the two main party candidates? The electorate is often chastised for an
unwillingness of lack of motivation to participate in politics, where low turnout levels
are cited as supportive evidence. My results demonstrate that it is not necessarily
lack of motivation that prevents everyone from voting, but rather problems with the
available options on the ballot.
Going back to the first question I explore in this chapter, my findings provide
little evidence in support of the information-seeking hypothesis. First, I find that
candidate ambivalence generally exerts no influence on media use or political
discussions. Second, the decision ambivalence findings directly counter my original
expectations and consistently show a negative relationship with media use and
discussions. More importantly, the information-seeking hypothesis is further
disconfirmed by the panel analysis. By and large, ambivalence measured in the preelection survey is unrelated to changes in accuracy or uncertainty in the weeks
leading up to the election. While there were a handful of significant findings, there is
no consistent trend among these few cases. Finally, my results also generally show
no relationship between candidate ambivalence and changes in campaign interest, but
151
that decision ambivalence is consistently related to decreased interest in the
campaign. This finding countered my original expectations, as it suggests that those
who are conflicted in choosing between the candidates shy away from getting
information about the campaign. Upon further reflection, however, the interest
question may be a better test of the ambivalence-abstention link than it is a test of the
information-seeking hypothesis. In line with my findings on abstention, these results
show that those individuals who experience high decision ambivalence are also more
likely to withdraw from the campaign by reporting a decreased interest in the process
itself.
Combined, my results on information-seeking behavior in the NES data do not
fully replicate my experimental results. In the previous chapter I found a positive
relationship between subjective decision ambivalence and information-seeking
behavior. In this chapter, I generally find no relationship between either objective
decision or candidate ambivalence and information-seeking behavior. It should be
noted, however, that the findings in these two chapters do not so much contradict
each other, but rather highlight some of the limitations of using the NES data to
examine the effects of ambivalence. Both the experimental and NES analysis tend to
show that objective decision ambivalence is unrelated to information-seeking
behavior. In the experimental analysis, however, I was able to further determine the
subjective decision ambivalence promotes information-seeking behavior.
Unfortunately, the NES does not ask questions about subjective ambivalence,
precluding any possibility of testing that relationship in an electoral context. Thus, it
152
may still be the case that subjective decision ambivalence leads to informationseeking behavior.
In summary, the results in this chapter generally accord with the story
revealed in the experimental analysis. Nonetheless, there are some limitations on my
ability to compare the analysis in the two chapters. This, in no small part, is a
function of the differences in the various measures and tests used in the two chapters.
In the following chapter, I examine a panel survey of undergraduate students that
records their attitudes and behaviors towards George W. Bush and John Kerry in the
2004 presidential campaign. The results from this chapter enable me to tie all my
findings together and present a clear picture for how ambivalence affects electoral
behavior.
153
1980
1984
1988
1992
1996
2000
Democrat
Ambivalence
.130*
-.462
.189
.304**
.209
-.177
Republican
Ambivalence
.063
-.056
.225
.318**
.222
-.111
Independent
Ambivalence
-.007
---
---
.038
-.432*
---
Decision
Ambivalence
-.152***
-.126
-.356***
-.434***
-.387**
-.020
Female
.151
-.325
-.425
-.132
-.270
.254
Non-white
.269
.846
.714*
.963**
1.300*
.819
Family Income
-.005
.0001
.018**
.016**
-.005
-.015
.022***
.161***
.101***
.120***
.182***
.223***
Education
-.095
.435*
.322**
.169
-.112
-1.040***
Democrat
Dummy
-.320
.466
.145
-.078
.685
1.970*
Republican
Dummy
-.393*
.505
-.369
-.360
.837
.723
Party Strength
.094
.265
.077
.220
.120
.258
Sophistication
.219***
.467***
.585***
.592***
.874***
.689***
Efficacy
-.015
.589**
.206***
.185***
-.005
.205***
Trust in Govt.
.070*
-.209
.050
-.073
.196**
.194
Need for
Cognition
---
---
---
---
---
.583***
Constant
3.591***
7.856***
2.652***
5.404***
2.090*
-3.683**
Adjusted R2
.1452
.2267
.2789
.2758
.2177
.2778
N
1138
626
1353
1262
1272
1269
Age
* p <.10; ** p < .05; *** p < .01
Table 3.1: Impact of Ambivalence on Pre-Election Media Use Indices: OLS
Regression
154
1984
(index)
-.083
1988
(days talk)
.121*
1992
(index)
-.026
1996
(disc week)
.078
Republican
Ambivalence
.171*
-.040
.183***
.187***
Independent
Ambivalence
---
---
-.031
-.108*
Decision
Ambivalence
-.178**
-.153***
-.181***
-.221***
.098
-.020
.109
-.117
-.771***
.227
-.208
-.105
.008
.011***
.000
-.001
-.013**
.011***
-.008*
.006
Education
.073
.100
.102
.142**
Democrat
Dummy
.071
-.343
-.053
.096
Republican
Dummy
-.024
-.245
.291
.194
Party Strength
.037
.157**
.206**
.210**
Sophistication
.322***
.238***
.331***
.110***
Efficacy
.383***
.081***
.146***
.073***
Trust in Govt.
-.039**
-.012
-.090**
-.069***
-1.145***
.2789
1534
.202
.2244
1927
-.068
.1089
1272
Democrat
Ambivalence
Female
Non-white
Family Income
Age
Constant
1.067
.2005
Adjusted R2
N
746
* p <.10; ** p < .05; *** p < .01
Table 3.2: Impact of Ambivalence on Personal Political Discussions: OLS Regression
155
Carter
Reagan
Anderson
Pre-Score
Carter Ambivalence
Reagan
Ambivalence
Anderson
Ambivalence
Decision
Ambivalence
.328***
-.132**
.309***
.018
.252***
.085
-.027
-.050
-.007
-.019
.016
-.183**
.073*
-.009
-.020
Female
Non-white
Family Income
Age
Education
Democrat Dummy
Republican Dummy
Party Strength
Sophistication
Efficacy
Trust in Govt.
Constant
-.119
.377*
.001
.003
-.044
-.507**
-.170
.072
-.024
-.040
-.003
1.54***
-.081
.288
-.008**
-.001
-.080
-.479**
-.514**
.036
-.019
-.022
-.062
2.02***
.063
.090
-.015***
-.001
-.056
.020
-.026
-.040
.020
.045
-.095*
1.78***
632
.163
510
.079
N
626
2
Adjusted R
.124
* p < .10; ** p < .05; *** p < .01
Table 3.3: Impact of Ambivalence on Changes in Accurately Placing the Candidates
on an Ideological Scale from the Pre-Election to Post-Election Surveys in 1980: OLS
Regression, (Positive Coefficients = Decreased Accuracy)
156
Carter
Reagan
Anderson
Pre-Score
Carter Ambivalence
Reagan
Ambivalence
Anderson
Ambivalence
Decision
Ambivalence
.372***
-.124**
.391***
-.033
.373***
-.006
.026
-.001
-.011
.076
.097
-.087
.067
.023
.059
Female
Non-white
Family Income
Age
Education
Democrat Dummy
Republican Dummy
Party Strength
Sophistication
Efficacy
Trust in Govt.
Constant
-.055
.554***
-.008
.008**
-.219***
-.476*
-.221
.138+
-.146***
-.114**
.055
2.90***
.123
.361*
-.015***
.006*
-.246***
-.725***
-.725***
.167**
-.129***
-.099***
.016
3.14***
.159
.376*
-.014
.005
-.272***
.016
-.092
.018
-.102***
-.053
.030
3.05***
1159
.377
1158
.312
N
1158
Adjusted R2
.348
* p < .10; ** p < .05; *** p < .01
Table 3.4: Impact of Ambivalence on Changes in Uncertainty when Placing the
Candidates on an Ideological Scale from the Pre-Election to Post-Election Surveys in
1980: OLS Regression, (Positive Coefficients = Increased Uncertainty)
157
Pre-Score
Clinton
Ambivalence
Dole
Ambivalence
Perot
Ambivalence
Decision
Ambivalence
Ideology
Clinton
Dole
Clinton
Aid to Blacks
Dole
Perot
.419***
.272***
.252***
.263***
.233***
-.033
-.029
-.026
-.024
-.042
.037
-.020
-.032
-.019
.018
.011
-.008
-.044
-.006
-.008
-.032
.010
-.014
-.021
.018
-.173*
.252*
.036
.180
.117
.404**
.000
.010***
-.100*
-.001
.005*
.018
-.002
.005
.057
-.251
-.021
-.341
.049
-.274
-.343
.157**
-.062**
-.012
.024
1.25***
1118
.126
.074
-.025
-.011
-.010
1.12***
1052
.095
.014
-.026
-.011
-.012
1.74***
831
.058
Female
.029
-.076
Non-white
.213*
.495***
Family
Income
-.004**
-.003*
Age
.002
.003
Education
.072*
-.045
Democrat
Dummy
-.595***
-.172
Republican
Dummy
-.259
-.327*
Party
Strength
.102*
.075
Sophistication
-.061***
-.077***
Efficacy
-.002
-.013
Trust in Govt.
-.016
-.011
Constant
1.51***
1.79***
N
1179
1132
2
Adjusted R
.254
.195
* p < .10; ** p < .05; *** p < .01
Table 3.5: Impact of Ambivalence on Changes in Accurately Placing the Candidates
on an Ideology and Aid to Blacks Issue Position Scales from the Pre-Election to PostElection Surveys in 1996: OLS Regression, (Positive Coefficients = Decreased
Accuracy)
158
Ideology
Clinton
Dole
Clinton
Aid to Blacks
Dole
Perot
Pre-Score
Clinton
Ambivalence
Dole
Ambivalence
Perot
Ambivalence
Decision
Ambivalence
.360***
.269***
.216***
.250***
.255***
-.065+
-.034
-.043
-.025
-.055
.008
-.050
-.019
-.069
-.028
-.005
-.029
-.035
-.012
.007
-.001
.037
-.026
.002
.010
Female
Non-white
Family
Income
Age
Education
Democrat
Dummy
Republican
Dummy
Party
Strength
Sophistication
Efficacy
Trust in Govt.
Constant
-.043
.210
-.134
.349***
-.186*
.061
-.070
-.019
.299**
.066
-.004**
.004
.054
-.003*
.009***
-.099**
-.001
.011***
-.081
-.001
.008**
-.007
-.001
.008**
.045
-.515**
-.124
-.085
-.019
-.286
-.155
-.313
.152
-.248
-.252
.138**
-.100***
-.004
-.032+
1.89***
.119**
-.138***
-.001
-.037*
2.17***
.101
-.114***
-.004
-.018
1.89***
.045
-.099***
-.009
-.008
1.85***
.045
-.094***
.009
.012
1.91***
1270
.113
1263
.114
1257
.095
N
1267
1266
Adjusted R2
.222
.240
* p < .10; ** p < .05; *** p < .01
Table 3.6: Impact of Ambivalence on Changes in Uncertainty in Placing the
Candidates on the Ideology and Aid to Blacks Issue Position Scales from the PreElection to Post-Election Surveys in 1996: OLS Regression, (Positive Coefficients =
Increased Uncertainty)
159
Ideology
Services
Central
America
Guaranteed
Jobs
Mean
Score
Pre-Score
Mondale
Ambivalence
Reagan
Ambivalence
Decision
Ambivalence
.237***
.230***
.234***
.510***
.385***
.020
-.141*
-.094
-.024
-.036
.087
-.036
-.046
-.078
-.019
-.083*
.035
.011
.000
-.011
Female
Non-white
Family Income
Age
Education
Democrat
Dummy
Republican
Dummy
Partisan
Strength
Sophistication
Efficacy
Trust in Govt.
Constant
-.176
-.043
.000*
-.009**
-.032
-.006
.071
.000**
.000
-.123*
-.091
.518**
.000
.011**
-.219***
-.240**
.349**
.000
.000
-.162***
-.081
.334**
.000**
.001
-.134***
-.110
.593**
.375
.103
.244
-.404
.033
-.128
.015
-.100
.193**
-.060*
.093
-.007
2.88***
.008
.038
.122*
.053
1.05***
-.148
-.044
-.004
.054
1.68***
-.012
-.024
.000
.004
1.63***
.017
-.030
.044
.021
1.65***
627
.095
570
.149
632
.159
410
.270
N
630
Adjusted R2
.109
* p < .10; ** p < .05; *** p < .01
Table 3.7: Impact of Ambivalence on Changes in Accurately Placing Reagan on
Ideology and Issue Position Scales from the Pre-Election to Post-Election Surveys in
1984: OLS Regression, (Positive Coefficients = Decreased Accuracy)
160
Ideology
Services
Central
America
Guaranteed
Jobs
Mean
Score
Pre-Score
Mondale
Ambivalence
Reagan
Ambivalence
Decision
Ambivalence
.242***
.268***
.210***
.441***
.363***
.067
-.140*
-.069
-.041
-.025
.095
-.021
-.030
-.047
-.001
-.096**
.033
.003
-.001
-.025
Female
Non-white
Family Income
Age
Education
Democrat
Dummy
Republican
Dummy
Partisan Strength
Sophistication
Efficacy
Trust in Govt.
Constant
-.143
.000
.000**
-.008**
-.116*
.019
.144
.000***
.003
-.135*
-.094
.505**
.000
.014***
-.211***
-.157
.518***
.000**
.004
-.153**
-.026
.367***
.000***
.002
-.132***
-.466*
.459
.313
-.003
.177
-.666**
.220***
-.113***
.054
-.027
3.72***
-.083
.013
.000
.119
.054
1.25***
-.088
-.161*
-.052
.053
.032
1.79***
-.105
.005
-.073**
-.027
-.002
2.01***
-.101
.024
-.032
.043
.031
1.66***
647
.125
598
.142
688
.175
476
.286
N
781
2
Adjusted R
.161
* p < .10; ** p < .05; *** p < .01
Table 3.8: Impact of Ambivalence on Changes in Uncertainty When Placing Reagan
on Ideology and Issue Position Scales from the Pre-Election to Post-Election Surveys
in 1984: OLS Regression, (Positive Coefficients = Increased Uncertainty)
161
Ideology
Services
Central
America
Guaranteed
Jobs
Mean
Score
Pre-Score
Mondale
Ambivalence
Reagan
Ambivalence
Decision
Ambivalence
.322***
.194***
.223***
.312***
.378***
.090
-.054
.000
.053
.046
.081
-.054
-.015
-.082
-.038
-.106*
.013
-.028
.008
-.022
Female
Non-white
Family Income
Age
Education
Democrat
Dummy
Republican
Dummy
Party Strength
Sophistication
Efficacy
Trust in Govt.
Constant
-.007
.591***
.000
.008
-.016
.041
.038
.000
-.003
-.049
.020
.063
.000
.002
-.015
-.106
.281
.000
.006
-.052
.145
.274**
.000
.004
.006
.123
-.510*
-.161
-.475*
-.347
.307
.045
-.078**
-.090
-.039
1.88***
-.598**
.151*
-.056*
-.041
-.047
1.92***
.013
.045
-.001
-.129*
-.024
1.34***
-.308
.145*
.031
-.147**
-.024
1.50***
-.204
.134**
-.025
-.056
-.022
1.23***
580
.070
576
.057
590
.114
350
.234
N
603
2
Adjusted R
.130
* p < .10; ** p < .05; *** p < .01
Table 3.9: Impact of Ambivalence on Changes in Accurately Placing Mondale on
Ideology and Issue Position Scales from the Pre-Election to Post-Election Surveys in
1984: OLS Regression, (Positive Coefficients = Decreased Accuracy)
162
Ideology
Services
Central
America
Guaranteed
Jobs
Mean
Score
Pre-Score
Mondale
Ambivalence
Reagan
Ambivalence
Decision
Ambivalence
.271***
.205***
.219***
.276***
.350***
.089
-.076
-.017
.025
.009
.072
-.037
.190***
-.066
.013
-.077
.018
-.117**
.028
-.024
Female
Non-white
Family Income
Age
Education
Democrat
Dummy
Republican
Dummy
Party Strength
Sophistication
Efficacy
Trust in Govt.
Constant
.077
.616***
.000
.007*
-.109
-.011
.015
.000
.000
-.081
.227
-.055
.000
.013***
-.049
-.029
.396**
.000
.011***
-.109
.116
.281**
.000
.005*
-.042
-.449
-.352
-.290
-.508*
-.259
-.315
.145
-.118***
-.120*
-.033
2.95***
-.317
.106
-.141***
-.066
-.033
2.37***
.144
-.005
-.069*
-.021
-.065
1.44***
-.059
.081
-.042
-.178**
-.057
2.10***
-.026
.044
-.044*
-.103**
-.012
1.60***
645
.112
597
.116
688
.162
478
.243
N
797
2
Adjusted R
.17
* p < .10; ** p < .05; *** p < .01
Table 3.10: Impact of Ambivalence on Changes in Uncertainty When Placing
Mondale on Ideology and Issue Position Scales from the Pre-Election to PostElection Surveys in 1984: OLS Regression, (Positive Coefficients = Increased
Uncertainty)
163
Pre Score
Democrat
Ambivalence
Republican
Ambivalence
Independent
Ambivalence
Decision
Ambivalence
Female
Non-white
Family Income
Age
Education
Democrat
Dummy
Republican
Dummy
Party Strength
Sophistication
Efficacy
Trust in Govt.
Constant
1980
1984
1988
1992
1996
2000
.434***
.417***
.455***
.393***
.436***
.366***
.020
.013
.010
-.007
-.010
-.008
.002
.028
-.003
.027**
.001
-.010
-.013
-.037***
-.027**
-.070***
-.027**
-.035**
-.015
-.001
.026***
-.002
-.063
.000
-.001
-.010
.106**
-.014
.000
.003**
-.005
-.001
.016
.000
.002***
-.003
.030
.015
.000
.001
.014
-.033
.075*
-.001
.002**
-.028*
.012
.071*
.000
.001
-.035**
-.054
.102
-.004
.077
-.026
.014
.037
.072***
.057***
.013
.001
.282***
.123
-.043
.043***
.062***
.006
.228**
.052
.017
.048***
.021***
.012
-.120
.055
.026
.050***
.018***
-.011
.227***
.004
.060***
.036***
.010**
.015**
.127
.032
.029
.028***
.026***
-.005
.222***
1537
.438
1927
.402
1274
.396
1317
.391
N
1159
822
Adjusted R2
.377
.287
* p < .10; ** p < .05; *** p < .01
Table 3.11: Impact of Ambivalence on Changes in Campaign Interest from the PreElection to Post-Election Surveys in 1984: OLS Regression, (Positive Coefficients =
Increased Interest)
164
Female
Reagan
-.018
Carter
.354*
Anderson
.376
Non-white
-.992**
.645**
-.383
Family Income
.021**
.021**
.034***
Age
.032***
.033***
-.006
Education
.373***
.162
.499***
Democrat
-.786**
.160
.767
Republican
.403
-1.865***
.639
Partisan Strength
.167
.605***
-.443*
Sophistication
.272***
.327***
.372***
Efficacy
.172**
.189**
.370***
Trust in Government
-.037
.131*
-.123
Personal Economy
.062
-.105
.089
.247**
-.132
-.076
Reagan Ambivalence
.438***
.090
-.132
Carter Ambivalence
.188*
.440***
.036
Anderson Ambivalence
-.050
.091
-.146
Decision Ambivalence
-.345***
-.253***
.223**
Constant
-4.769***
-5.028***
-4.704***
Worse
National Economy
Worse
Log Likelihood = -973.16
% Correctly Predicted = 64.3; Proportionate Reduction in Error = 42.2%
N = 1105; *+ p < .10; ** p < .05; *** p < .01
Note: Abstention is the omitted category; Source 1980 NES
Table 3.12: Multinomial Logit Results for Candidate and Abstention Choices: 1980
Election
165
Change in Probability of Selecting
Abstention
-.026
.033
-.205*
-.361*
-.230*
.074
.045
-.145+
-.589*
-.149*
-.043
-.003
Reagan
-.048
-.263*
.099
.272*
.233*
-.220*
.238*
-.005
.220*
.055
-.160
.048
Carter
.060+
.237*
.055
.170*
-.064
.093
-.327*
.269*
.271*
.045
.246*
-.053
Anderson
.014
-.007
.051+
-.082*
.061*
.053*
.044
-.118*
.099*
.050*
-.042
.008
Female
Non-white
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Personal Economy
Worse
National Economy
-.048
.269*
-.192*
-.029
Worse
Reagan Ambivalence
-.335*
.618*
-.192*
-.091*
Carter Ambivalence
-.320*
-.115
.483*
-.049+
Anderson Ambivalence
.007
-.104
.133
-.036
Decision Ambivalence
.407*
-.590*
-.130
.312*
+ p < .10; * p < .05
First Differences Calculated via King et al.’s Clarify Program
Cell values indicate the change in probability for that event given a shift from the
minimum to maximum value of independent variable, holding all other variables at
their mean
Table 3.13: First Differences – Differences in Predicted Outcomes for Each Variable
in 1980: Probability of Maximum Value – Probability of Minimum Value, (All Other
Values Placed at their Mean)
166
Reagan
-.028
-1.022***
.034**
.036***
.469***
-1.434***
.042
.400
-.010
.265
-.156
-.358
-.125
.196
-.015
-.097
-1.720***
Mondale
.244
-.350
.011
.024***
.485***
.145
-2.083***
.640***
.178***
.269**
-.141*
-.129
.311**
.041
.058
-.154*
-4.005***
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Personal Economy Worse
National Economy Worse
Reagan Ambivalence
Mondale Ambivalence
Decision Ambivalence
Constant
Log Likelihood = -532.53
% Correctly Predicted = 72.7; Proportionate Reduction in Error = 50.2%
N = 768; * p < .10; ** p < .05; *** p < .01
Note: Abstention is the omitted category
Table 3.14: Multinomial Logit Results for Candidate and Abstention Choices: 1984
Election
167
Variable
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Change in Probability of Selecting
Abstention
Reagan
-.012
-.037
.156*
-.200*
-.315*
.457*
-.505*
.487*
-.335*
.208+
.161*
-.353*
.113
.234*
-.286*
.093
-.071
-.157+
-.160*
.106
.245*
-.182
Mondale
.049
.043
-.145*
.018
.127
.192*
-.347*
.193*
.228*
.053
-.064
Personal Economy Worse
.217*
-.288*
.071
National Economy Worse
.024
-.269*
.293*
Reagan Ambivalence
-.183
.293+
-.110
Mondale Ambivalence
-.007
-.088
.095
Decision Ambivalence
.221
-.058
-.163
+ p < .10; * p < .05
First Differences Calculated via King et al.’s Clarify Program
Cell values indicate the change in probability for that event given a shift from the
minimum to maximum value of independent variable, holding all other variables at
their mean
Table 3.15: First Differences – Differences in Predicted Outcomes for Each Variable
in 1984: Probability of Maximum Value – Probability of Minimum Value, (All Other
Values Placed at their Mean)
168
Bush
.382**
-.648***
.028***
.034***
.422***
-2.221***
-.407
.708***
.173***
.075***
.158***
.011
-.235**
.147
.045
-.172**
-5.249***
Dukakis
.443**
.565***
.020***
.027***
.424***
.184
-1.487***
.480***
.209***
.089***
-.028
.144*
.050
.002
.221**
-.169**
-5.788***
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Personal Economy Worse
National Economy Worse
Bush Ambivalence
Dukakis Ambivalence
Decision Ambivalence
Constant
Log Likelihood = -1021.30
% Correctly Predict = 72.0; Proportionate Reduction in Error = 55.3%
N = 1460; *p < .10; ** p < .05; *** p < .01
Note: Abstention is the omitted category
Table 3.16: Multinomial Logit Results for Candidate and Abstention Choices: 1988
Election
169
Change in Probability of Selecting
Abstention
Bush
-.087*
.034
.007
-.200*
-.366*
.325*
-.404*
.305*
-.314*
.159*
.198*
-.483*
.188*
.078
-.381*
.295*
-.451*
.164
-.380*
.138
-.146*
-.390*
-.067
-.060
.083
-.239*
-.120
.252+
-.193
-.138
.386*
-.208
Variable
Dukakis
Female
.054
Minority
.207*
Family Income
.040
Age
.099
Education
.155*
Democrat
.285*
Republican
-.267*
Partisan Strength
.086
Sophistication
.287*
Efficacy
.242*
Trust in Government
-.244*
Personal Economy Worse
.127+
National Economy Worse
.156*
Bush Ambivalence
-.132
Dukakis Ambivalence
.331*
Decision Ambivalence
-.178
+ p < .10; * p < .05
First Differences Calculated via King et al.’s Clarify Program
Cell values indicate the change in probability for that event given a shift from the
minimum to maximum value of independent variable, holding all other variables at
their mean
Table 3.17: First Differences – Differences in Predicted Outcomes for Each Variable
in 1988: Probability of Maximum Value – Probability of Minimum Value, (All Other
Values Placed at their Mean)
170
Bush
.821***
-.571**
.017***
.030***
.581***
-1.384***
.382
.440***
Clinton
.584***
.152
.015***
.025***
.523***
.447
-1.954***
.393***
Perot
.066
-1.269***
.017***
.008
.404***
.201
.059
-.122
Female
Non-white
Family Income
Age
Education
Democrat
Republican
Partisan
Strength
.193***
.251***
.273***
Sophistication
.065***
.089***
.053**
Efficacy
.027
.143***
-.060
Trust in
Government
Personal
-.031
.170**
.191**
Economy Worse
National
-.339***
.253***
.008
Economy Worse
Bush
.050
.129
.032
Ambivalence
Clinton
-.083
.113
.074
Ambivalence
Perot
.096
.107
.016
Ambivalence
Decision
-.018
-.257***
.062
Ambivalence
-4.347***
-6.915***
-3.488***
Constant
Log Likelihood = -1712.25
% Correctly Predicted = 64.5; Proportionate Reduction in Error= 43.4%
N = 1894; *p < .10; **p < .05; ** p < .01
Note: Abstention is the omitted category; Source 1992 NES
Table 3.18: Multinomial Logit Results for Candidate and Abstention Choices: 1992
Election
171
Change in Probability of Selecting
Abstention
-.094*
.052+
-.246*
-.261*
-.321*
.025
.117*
-.141*
-.416*
-.305*
-.114*
-.060+
Bush
.089*
-.070*
.089+
.203*
.158*
-.282*
.216*
.137*
.027
.040
-.057
-.081*
Clinton
.065*
.153*
.077
.172*
.164*
.205*
-.431*
.188*
.245*
.279*
.342*
.089+
Perot
-.060*
-.135*
.080+
-.114*
-.001
.052
.099*
-.184*
.144*
-.013
-.171*
.052
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Personal Economy
Worse
National Economy
.026
-.362*
.312*
.025
Worse
Bush Ambivalence
-.099
-.030
.173
-.045
Clinton Ambivalence
-.061
-.172*
.192
.041
Perot Ambivalence
-.096
.051
.105
-.060
Decision Ambivalence
.187+
.137
-.620*
.296*
+ p < .10; * p < .05
First Differences Calculated via King et al.’s Clarify Program
Cell values indicate the change in probability for that event given a shift from the
minimum to maximum value of independent variable, holding all other variables at
their mean
Table 3.19: First Differences – Differences in Predicted Outcomes for Each Variable
in 1992: Probability of Maximum Value – Probability of Minimum Value, (All Other
Values Placed at their Mean)
172
Dole
.070
-.826*
.015***
.035***
.635***
-2.382***
.612
.577***
Clinton
.527***
.649***
.013***
.026***
.335***
.641
-.632***
.475***
Perot
-.221
-.954
.008
.004
.230
.591
.572
-.326
Female
Non-white
Family Income
Age
Education
Democrat
Republican
Partisan
Strength
.269***
.289
.324***
Sophistication
.016
.004
-.012
Efficacy
.022
.129***
-.022
Trust in
Government
Personal
.102
.039
.101
Economy Worse
National
.363***
-.303***
.344*
Economy Worse
Dole
.014
.062
.066
Ambivalence
Clinton
.058
.206**
-.064
Ambivalence
Perot
-.114
.004
.048
Ambivalence
Decision
-.283***
-.207***
.013
Ambivalence
-7.094***
-5.512***
-4.494***
Constant
Log Likelihood = -940.98
% Correctly Predicted = 69.6; Proportionate Reduction in Error = 48.2%
N = 1220; * p < .10; ** p < .05; *** p < .01
Note: Abstention is the omitted category; Source 1996 NES
Table 3.20: Multinomial Logit Results for Candidate and Abstention Choices: 1996
Election
173
Change in Probability of Selecting
Abstention
-.066*
-.054
-.231*
-.329*
-.285*
.045
.036
-.231*
-.551*
-.014
-.219*
-.048
Dole
-.034
-.131*
.104
.204*
.251*
-.436*
.148*
.135*
.112+
.033
-.104
.041
Clinton
.131*
.231*
.138
.183+
.049
.352*
-.229*
.247*
.379*
-.005
.393*
-.009
Perot
-.031+
-.044*
-.011
-.059+
-.015
.039
.045
-.151*
.061+
-.013
-.069
.016
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Personal Economy
Worse
National Economy
.034
.310*
-.445*
.101*
Worse
Dole Ambivalence
-.067
-.031
.076
.022
Clinton Ambivalence
-.189*
-.071
.319*
-.059
Perot Ambivalence
.029
-.113
.041
.044
Decision Ambivalence
.441*
-.253*
-.326+
.128+
+ p < .10; * p < .05
First Differences Calculated via King et al.’s Clarify Program
Cell values indicate the change in probability for that event given a shift from the
minimum to maximum value of independent variable, holding all other variables at
their mean
Table 3.21: First Differences – Differences in Predicted Outcomes for Each Variable
in 1996: Probability of Maximum Value – Probability of Minimum Value (All Other
Values Placed at their Mean)
174
Bush
.204
-.608
.001
.029***
.289*
-.589
.939*
.181
.167***
.098***
.007
-.236*
.239**
.056
.236
-.207*
-4.832***
Gore
.545*
.362
-.002
.026***
.575***
.951*
-1.987***
.533***
.236***
.036
.092
-.023
-.085
-.136
.300*
-.246**
-6.120***
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Personal Economy Worse
National Economy Worse
Bush Ambivalence
Gore Ambivalence
Decision Ambivalence
Constant
Log Likelihood = -402.28
% Correctly Predicted = 74.8%; Proportionate Reduction in Error = 58.3%
N = 650; * p < .10; ** p < .05; *** p < .01
Note: Abstention is the omitted category
Table 3.22: Multinomial Logit Results for Candidate and Abstention Choices: 2000
Election
175
Variable
Female
Minority
Family Income
Age
Education
Democrat
Republican
Partisan Strength
Sophistication
Efficacy
Trust in Government
Change in Probability of Selecting
Abstention
Bush
-.058
-.029
.009
-.175*
.021*
.081
-.325*
.204
-.302*
-.046
-.029
-.264*
.055
.444*
-.196*
-.083
-.578*
.095
-.275*
.388*
-.098
-.125
Gore
.097
.167*
-.102
.120
.348*
.293*
-.499*
.278*
.483*
-.113
.222
Personal Economy Worse
.092
-.191+
.098
National Economy Worse
.054
.263*
-.209*
Bush Ambivalence
.043
.161
-.204
Gore Ambivalence
-.254+
-.040
.214
Decision Ambivalence
.394*
-.129
-.265
+ p < .10; * p < .05
First Differences Calculated via King et al.’s Clarify Program
Cell values indicate the change in probability for that event given a shift from the
minimum to maximum value of independent variable, holding all other variables at
their mean
Table 3.23: First Differences – Differences in Predicted Outcomes for Each Variable
in 2000: Probability of Maximum Value – Probability of Minimum Value, (All Other
Values Placed at their Mean)
176
Ambivalence Coefficients1
Democrat
Republican
Independent
Decision
1980
1988
1992
1996
2000
.050
.143
.246*
-.086
-.078
.042
-.044
-.007
.022
.041
-.117
.198*
.017
-.028
.212**
-.125
-.093
-.097
First Differences of Ambivalence Variables:
Maximum Value – Minimum Value
Democrat
Republican
Independent
Decision
Log-Likelihood
N
.048
-.145
.204*
-.161
.043
-.113
-.131
.141*
.009
-.044
.172**
-.173
-.178
-.072
-.016
.030
.091
-499.96
1101
-663.67
1517
-820.45
1788
-500.53
1269
-528.27
1265
-.171
* p <.10; ** p < .05; *** p < .01
1
Other Variables Included in Model: Sex, Race, Income, Education, Age, Democrat
Dummy, Republican Dummy, Partisan Strength, Sophistication, Trust in
Government, Efficacy & (Cognition in 2000 only)
Table 3.24: Predicting Self-Reported Turnout in Previous Presidential Election
Logit Regression
177
T-test for Difference of Means
Year
N
Mean
(1984, ’88, 00)
(1980, ’92, ’96)
6071
5715
-2.08
-2.03
X1 − X 2
-.05
Significance
.1719
Confidence Intervals for Decision Ambivalence
Year
N
Mean
95% CI
1980
1984
1988
1992
1996
2000
1561
2237
2035
2444
1710
1799
-1.68
-2.28
-1.87
-2.15
-2.17
-2.07
-1.77, -1.59
-2.37, -2.20
-1.96, -1.78
-2.24, -2.07
-2.27, -2.07
-2.16, -1.97
Table 3.25: T-Test for Difference of Means in Decision Ambivalence Scores:
Comparing Values from Two-Candidate Elections to Three Candidate Elections
178
Percent
100
80
60
40
1980
1984
1988
1992
1996
2000
Mean for
all Years
Year
Republican Candidates
Democrats Candidates
Figure 3.1: Percent of Respondents with Negative Griffin Ambivalence Scores (e.g. Low
Ambivalence) Who Have More Unfavorable than Favorable Considerations
179
Percent
100
80
60
40
1980
1984
1988
1992
1996
2000
Mean for
all Years
Year
Republican Candidates
Democrats Candidates
Figure 3.2: Percent of Respondents with Positive Griffin Ambivalence Scores (e.g. High
Ambivalence) Who Have More Favorable than Unfavorable Considerations
180
CHAPTER 4
THE 2004 PRESIDENTIAL CAMPAIGN – PANEL DATA
As a final piece of analysis, I employ a third methodological approach in this
chapter to test my hypotheses. Specifically I draw upon a two-wave panel study of
undergraduates during late spring of the 2004 presidential campaign. While the
analysis in the previous chapters helps unravel how ambivalence affects electoral
behavior, each method is limited in some regard. First, when drawing upon NES data
I was beholden to a dataset that was not designed to test my hypotheses.
Consequently, I had to utilize the questions available to measure my concepts, such as
using the open-ended likes/dislikes questions to measure ambivalence and general
interest in the campaign to assess information-seeking behavior. In my experimental
chapter, I was constrained by the fact that my subjects are asked to examine fictional
candidates. I was able to isolate the causal effects of ambivalence by using these
candidates, but also accepted the inherent concerns over external validity the fictional
candidates create. The panel data used in this chapter addresses any potential
methodological concerns that remain. Although panel data has its own limitations, it
has a number of strengths that makes it an excellent complement to the previous
chapters.
181
Perhaps the most beneficial aspect of this dataset is that it was explicitly
designed to test the relationship between ambivalence and both information-seeking
behavior and abstention. Therefore, I am able to include a number of different
questions when measuring each concept. Specifically, I use four sets of questions to
operationalize objective ambivalence, including the measures used in both the NES
and experimental chapters. Additionally, and unlike the NES data, I also assess
feelings of subjective ambivalence.39 Although I examine the effects of objective
ambivalence in both my experimental and NES analysis, the actual objective
ambivalence measure differed in the two chapters. There are theoretical reasons to
believe that each of these variables are simply means of operationalizing the concept,
but this assumption remains untested. By including a number of different measures
of ambivalence I can determine whether the different measures I have used in the
previous chapters are simply different ways of measuring objective ambivalence, or
are measures of two different, albeit related, forms of ambivalence.
The benefits of this are twofold. First, I have used different measures of
ambivalence in the above analysis, but have treated them as equivalent. Clearly, it is
important to verify this treatment. Second, there has been little, if any, work done in
political science on how best to measure ambivalence. For instance, scholars have
used the open-ended likes/dislikes questions to operationalize positive and negative
feelings, but these measures have yet to be compared to other more direct measures of
positive and negative feelings. Therefore, this analysis will not only bolster the
confidence in my study of ambivalence, but can increase our confidence in other
39
Unfortunately, the subjective ambivalence questions were left off the first wave questionnaire. The
implications of this problem will be discussed in more detail below.
182
studies as well. Fortunately, I, and my fellow ambivalence inquisitors, can breathe a
collective sigh of relief as the evidence indicates that each measure is a different
means of operationalizing objective ambivalence.
In addition to the ambivalence measures used, I ask a number of questions that
assess the sundry ways one might engage in information-seeking behavior during a
campaign. Recall that the information-seeking hypothesis states that ambivalence
about an object, such as a candidate, will lead one to seek out more information about
the object. In turn, a test of this hypothesis should determine whether candidate
ambivalence generates increased attention to that specific candidate. In addition to
the standard questions that assess general attention to the campaign, this survey also
asks respondents about the extent to which they pay attention to information about
each candidate. Overall, this dataset provides me with a number of opportunities to
test my hypotheses, thereby increasing the confidence I have in my results.
Overall, the panel data provides a number of measures that allow testing of
my hypotheses with more precision. Combined, these measures enable me to test the
robustness of my earlier findings via a set of more precise tests. Equally important,
by collecting data at two time points, I am afforded the opportunity to test for causal
relationships that is unavailable in standard cross-sectional surveys. In the analysis
below, I let the campaign process itself serve as the causal stimulus for change.
Granted, this results in less control over the process than I have in an experimental
setting, but allows me to determine how ambivalence affects behaviors in relation to
actual candidates. In sum, the results presented below complement the findings in the
previous two chapters.
183
Ultimately, in this chapter I am unable to find any evidence that consistently
supports the information-seeking hypothesis. Simply stated, both candidate and
decision ambivalence are unrelated to increased information-seeking behavior. Also,
candidate ambivalence is unrelated to turnout decisions, whereas decision
ambivalence is positively linked to abstention. Ultimately, the results from this panel
study, coupled with those in chapters two and three, demonstrate that ambivalence
does not cause people to seek out more information during a campaign, but can lead
them to withdraw from the process come Election Day.
Panel Design
The participants in this panel study consisted of undergraduate students
enrolled in political science courses at Ohio State University during the spring 2004
quarter. The first wave took place during the first week of April and the second wave
was completed over the last week of May and first week of June. In the first survey,
students filled out a pencil and paper survey at the start or end of their class period
and received no compensation for their time.40 Before deciding whether or not to
complete this survey, however, they were told that those who filled it out would be
contacted at the end of the quarter about the opportunity to receive $10 for
completing a similar follow-up survey. At the end of the quarter, each participant
was solicited via email about the opportunity to participate in the second wave of the
study. The second survey was completed on a computer in the political science
40
In total, seven political science courses were used for this study. For administrative reasons,
students in one of the classes did not complete the first wave questionnaire until April 14. They
completed the second wave at the same time as did the other students. Also, in accordance with the
instructor’s wishes, this same class did receive extra credit for participating in the first wave of the
study.
184
department’s experimental computer lab. The decision to participate in either wave
of the study was completely voluntary. Ultimately, this process yielded 291 students
in the first wave and 155 students in the second wave.
The primary benefit of panel data is that it enables one to examine changes in
attitudes and behaviors over time. The period of time under study here, however, is
rather early in the campaign process, which may raise some questions. First, one
might justifiably ask if anyone was paying much attention to the candidates at that
point in the campaign. By the start of April, Kerry had for all practical purposes
locked up the Democratic nomination, the Ohio primary concluded nearly a month
earlier, and the conventions were two months away. Historically these months have
constituted a “down-time” period of the campaign. While many in the nation may not
have devoted much attention to the candidates in the spring of 2004, there is reason to
believe that this was not the case for the participants in this panel.
First, at the start of April, it was already clear to the candidates and pundits
that Ohio was going to be a major battleground state in the upcoming election. As a
result, both candidates and a myriad of political organizations wasted little time in
getting their campaign messages out to the Ohio electorate. In fact, both President
Bush and Senator Kerry opted to fund fully their own primary campaigns, declining
matching federal funds, and thereby bypassing any campaign spending restrictions.
Consequently, the 2004 campaign brought new meaning to the term “constant”
campaign. It essentially started with the Iowa caucus in January and, for many states,
ran nearly continuously until Election Day. Ohio was by no means an exception to
this process; if anything, the state bore a large portion of the brunt of this new
185
extended campaign season. Whether wanted or unwanted, the residents of Ohio had
ample opportunity to engage the presidential campaign in the spring of 2004.
Another reason why it is reasonable to believe my participants were paying
attention to the campaign is that they were undergraduate students enrolled in
political science courses. They may have a predisposition towards finding politics
more interesting than the average American citizen. Of course, this also raises the
question about the utility of drawing upon a sample of political science students to
test my hypotheses. Admittedly, the optimal dataset for this investigation is a random
sample of the national electorate, but this simply was not a viable option for this
study. While there are always legitimate concerns about generalizing to the general
population from a student sample, these concerns are not terribly problematic for my
analysis. First, if it is in fact the case that these students are predisposed to be
interested in politics, then this would work against finding evidence in favor of the
information-seeking hypothesis. Simply stated, a high level of initial interest will
present a ceiling constraint to my analysis. If my participants are paying a lot of
attention to politics, then it will be harder to explain changes in this behavior, as there
will be little room for change.41 Second, and more important, the initial level of
information-seeking behavior is not as critical as is the change in this behavior over
time. For instance, if ambivalence is positively related to increased informationseeking behavior, it does not matter if an individual moved from low to moderate
41
Conversely, there is also concern that this may unfairly stack the deck against the informationseeking hypothesis. That is, if I find no evidence of information-seeking behavior among students,
then this does not necessarily mean that the less attentive public is not motivated to seek information as
a result of ambivalent attitudes. I will return to this issue again in the final section of this chapter.
186
seeking behavior or from moderate to high seeking behavior. It is only important to
document a change in such behavior, irrespective of the starting point.
Overall, the evidence indicates that the participants in this panel were, in fact,
at least nominally involved in the campaign as early as April 2004. For instance,
when asked “how much attention do you generally pay to news about the presidential
campaign?,” roughly 55% of the sample selected one of the top two choices (“quite a
bit” or “a great deal) on the five-point scale. Likewise, when asked “in general, how
interested are you in the presidential campaign?,” nearly 80% of the sample opted for
“fairly” or “very” as opposed to the 20% who reported “not at all” or “somewhat
interested.” Albeit self-reports of political interest are low threshold measures of
actual attentiveness to the campaign, this evidence indicates a reasonable level of
awareness.
Another measure of political engagement is a willingness to place the
candidates on the standard 101-point feeling thermometer score. In the first wave,
just 3 people out of 291 were unwilling to place Kerry on the scale and only 32 more
placed him at the mid-point. Similarly, just 2 people did not place Bush on this scale
with only 14 more placing him at the mid-point. Thus, if we count each person who
places a candidate at the mid-point as having no attitude on the candidate, then this
means that only 12% and 6% of the sample had no opinion on Kerry and Bush
respectively at the start of the panel. Finally, if we expand this region of “no opinion”
to include anyone who places Kerry of Bush between 40 and 60 (i.e. only weakly
negative or positive), then this still only includes 31% and 19% of the sample
respectively. In other words, by drawing upon a very generous measure of
187
indifference to the candidates, only one-fifth to one-third of the sample had no
assessment of the candidates at the start of this panel study. Clearly, as of April of
2004 a vast majority of the respondents in this panel had at least a minimal amount of
awareness of the two men running for the Oval Office.
Ambivalence
While scholars agree that ambivalence consists of both a positive and negative
component, there is also an appreciation that there are a number of ways to measure
these traits. As discussed earlier, I included a number of different measures of
ambivalence in this survey. First, I measure positive and negative feelings via the
standard NES open-ended likes/dislikes questions. The question reads, “Thinking
about Bush/Kerry, is there anything in particular about him that might make you want
to vote for/against him?” Participants in the study, like respondents in the NES, are
probed for up to five responses for each question.
In addition to open-ended responses, I measure positive and negative
considerations via the direct questions I developed for the experimental analysis.
This first question used is:
First, thinking about George W. Bush/John Kerry, please consider only your
positive feelings towards him, ignoring any negative thoughts you may
have about him, and place yourself on the following scale.
I have no positive feelings towards Bush/Kerry
I have a few positive feelings towards Bush/Kerry
I have some positive feelings towards Bush/Kerry
I have a lot of positive feelings towards Bush/Kerry
188
The questions are then repeated to assess negative feelings. Next, I asked
respondents the extent to which they personally believed there were reasons to
support or oppose each candidate:
Please think about the reasons why you might support George W. Bush/John
Kerry in the November election, ignoring any reasons why you might
oppose him. Now read the following statement and tell us how much you
agree with it:
There are many reasons why I would support Bush in the November
election.
The response options range from “do not at all agree” to “strongly agree”. As before,
the question is repeated to assess the reasons why one might also oppose each
candidate.
Finally, I record each person’s positive and negative affective feelings
towards the two candidates. Although it has only received limited attention, a
handful of studies have also begun to explore the consequences of affective
ambivalence (cf. Steenbergen and Ellis). Therefore, I include these questions in this
study to determine how they compare to the more standard measures of ambivalence.
I ask respondents the extent to which each candidate makes them feel uneasy,
anxious, worried, angry, hopeful, and proud. For example, to assess anxiousness,
respondents are asked how much they disagree or agree with the statement, "When
thinking about Bush as a candidate, I feel anxious.” In line with previous work on
emotional responses (cf. Marcus and MacKuen 1993), the first four questions tap
one’s negative affective response, while the last two record a person’s positive
affective response. I get a summary measure of positive and negative affect by
calculating the mean value for each set of questions.
189
Combined, this yields four unique measures of positivity and negativity for
each candidate. Rather than create and examine the effects of four unique measures, I
first determine if these questions are simply different variables for the same
underlying concept or, instead, if they are measuring different types of positive and
negative feelings. If the former, then this indicates that I can use all of these
measures to create one global measure of ambivalence. If it is the latter, then it is
necessary to assess the effects of each type of ambivalence separately. I test the
dimensionality of these questions by first examining a simple correlation matrix of
the positive and negative scores for each candidate. As can be seen in Table 4.1, each
set of the measures are moderately to strongly correlated with each other, suggesting
each is a measure of the same latent concept. The correlations for the Bush measures
range from a low of .65 to a high of .85. The correlations for Kerry are not quite as
high, but overall are still strong, ranging from a low of .51 to a high of .77.
Next, I use factor analysis to determine more precisely whether each set of
measures is tapping a single underlying dimension. The results are presented in Table
4.2 and show that the Eigenvalues for the first dimension range from a low of 2.41 to
a high of 3.01 for the four sets of measures. An Eigenvalue that exceeds one is
generally considered to be the minimum criteria for inclusion. Thus, if the factor
analysis results produce two Eigenvalue that are greater than one, then this would
indicate that the responses are driven by two distinct latent factors. The Eigenvalues
for determining the existence of a second latent dimension for my measures all hover
around zero. These results overwhelmingly indicate that a one-dimensional solution
best explains responses to each set of positive and negative candidate questions.
190
Given this evidence, I have opted to combine each set of four variables into a
single measure each for Bush, Kerry, and decision ambivalence. First, I use the
Griffin formula to compute four separate ambivalence scores. That is, I calculate an
ambivalence values for each set of questions: (1) open-ended, (2) positive-negative,
(3) support-oppose, and (4) affective. I then use the mean of these four values and as
my measure of objective ambivalence in the analysis below. A factor analysis of the
Bush, Kerry, and decision ambivalence scores again shows that each set of four
ambivalence measures does not tap different types of ambivalence, but rather are all
simply different ways of measuring one form of ambivalence. The Eigenvalues for
Bush, Kerry, and decision ambivalence are 2.21, 1.53, and 2.88 respectively,
supporting my decision to combine these measures into a single variable.
In addition to objective ambivalence, one might also experience subjective
ambivalence about a candidate or over one’s vote decision. Subjective ambivalence
is measured with a direct question that asks respondents to state the extent to which
they do or do not feel conflicted about the candidates or over their vote decision. For
each candidate, participants are asked how much they agree or disagree with the
statement, “I have mixed feelings about Bush/Kerry.” To measure decision
ambivalence, the respondents were first asked to think about which candidate they
would vote for if the election were held at the time they completed the survey. Then
they are asked to state how conflicted they felt when choosing which candidate to
support.
At this point, it is necessary to mention that subjective candidate ambivalence
was not included in the first wave survey due to an unintentional oversight. While
191
this limits the extent to which I can assess the temporal effects of subjective
ambivalence, fortunately it does not preclude me from examining its effect on a
number of dependent variables. More importantly, the available data reveals a
consistent pattern of results. Consequently, the story that emerges from this data is
unlikely to change had this information been included. Thus, while unfortunate, the
exclusion of subjective ambivalence in the first survey is not a hurdle than cannot be
cleared.
Information-Seeking Behavior
There are a number of ways in which to measure information-seeking
behavior. For instance, an individual may simply pay more attention to the campaign
in general or instead might specifically focus on information about just one of the
candidates. Given the multiple paths one can take to acquire more information, I
included several questions that seek to assess the sundry ways in which one might
engage the campaign. First, I examine each respondent’s general level of attention to
politics and the presidential campaign with the following questions:
(1) How much attention do you generally pay to politics in the news?
(2) How much attention do you generally pay to news about the presidential
campaign?
(3) How much attention do you generally pay to news specifically about John
Kerry?
(4) How much attention do you generally pay to news specifically about
George W. Bush?
For each question, respondents could place themselves on a 5-point scale, ranging
from “none” to “a great deal”. I use the mean score on these four questions as my
first dependent variable. The Cronbach’s alpha scores for this index are .894 and
.860 in the two waves respectively.
192
For my second variable, I create an index of media use. The advantage of this
measure is two-fold. First, the media are the easiest and most common means by
which the public gets information about the candidates. Second, I am able to ask
respondents about a set of specific behaviors – media use – as opposed to general
attitudes, such as interest in the campaign. It is possible that the ambivalent may
come to increase their media use, yet feel no more interested in the campaign itself.
If true, then they would be seeking more information, but the general interest index
would not detect this change in behavior. Specifically, I use three questions to assess
the extent to which respondents utilize the media.
(1) How many days in the past week did you watch the news on TV?
(2) How many days in the past week did you read a daily newspaper?
(3) How much time do you spend getting political information about politics
from the Internet?
For the first two questions respondents reported the actual number of days (0 – 7) and
in the last question they placed themselves on the same 5-point scale used above. The
mean score is calculated and used as my dependent variable; alpha scores for the two
waves are .631 and .634 respectively.
The above indices enable me to examine the relationship between
ambivalence and both general political attentiveness and media use. Although useful,
these indices may not be the optimal measures for determining if candidate
ambivalence affects information-seeking behavior. The information-seeking
hypothesis states that ambivalence about a given object will lead one to seek out
information about that specific object. Thus, when people feel conflicted about Kerry
or Bush, then we should not necessarily expect them to seek out general political
193
information, but rather to seek information about the candidate himself. To test this
question, I ask respondents how attentive and interested they are to information about
the two candidates:
(1) How much attention do you generally pay to news specifically about John
Kerry/George W. Bush?
(2) In general how interested are you in Kerry’s/Bush’s presidential
campaign?
The first question was also used in the general attention index; the second question is
measured via a 4-point scale, where responses range from “not at all interested” to
“very interested”. The mean scores for these two questions are used as my dependent
variables. Alpha scores range from .75 and .80 in the two waves.
Combined, these four measures assess a number of ways in which the
electorate might seek more information about the candidates. When conflicted,
people may simply become more attentive to political information in general or they
might focus on information about a given candidate. Of course, these are not the only
means by which one may acquire information, but they are the most likely candidates.
Thus, while not an exhaustive list of the ways in which the electorate can gain
information during a campaign, it should provide a clear test of information-seeking
hypothesis. Simply stated, if I am unable to find evidence of information-seeking
behavior via these measures, then it is unlikely that another set of measures would
reveal such behavior.
194
Panel Data
Although panel studies have many benefits, one potential drawback is the risk
of selection bias in the participants of the two waves. That is, those participants who
opt to participate in both waves of the study may be systematically different than
those who participated in only the first wave. If true, then it is possible that the
observed “changes” in behavior observed over the course of the study may simply be
an artifact of this selection bias. Consequently, before turning to the analysis, it is
first useful to compare the attributes of the participants who completed both surveys
to those who only completed the first survey.
To compare the two groups, I examine the differences of means or proportions
on a number of questions asked in the first survey. The results are presented in Table
4.3, where the first column indicates the mean values for those participants who only
completed the first survey and the second column the values for those who completed
both surveys. By and large, there are no significant differences between the two
groups. Among the control variables only need for evaluation and sophistication
show significant differences; participants in both waves tend to score slightly higher
on each measure. No other control variables come close to being significant and are
often nearly identical to each other. For the control variables, it is safe to say that the
participants in this study are not notably different than are those who opted out of the
second wave.
An examination of the information-seeking and candidate attitude measures
also shows a general similarity between the two groups. The one exception is that
there is a small difference in assessments of Bush. First, those who completed both
195
surveys report significantly less ambivalence about Bush. Related to this finding,
participants in both waves also reported less decision ambivalence. This, of course,
makes sense, as the decision ambivalence measure is computed from the questions
used to assess ambivalence about each candidate. Overall, a person who is less
conflicted about one candidate should find it easier to select between both candidates.
A comparison of feeling thermometer scores indicates that the lower levels of Bush
ambivalence by the individuals who completed both waves is a function of viewing
him more negatively. The mean Bush feeling thermometer score for wave-one only
respondents was 52.31, while the mean score for those who completed both waves
was 45.22. While this difference is statistically significant, the substantive difference
here is minimal. On the whole, both groups show a high degree of variance in their
distribution of responses. The central tendencies of the two groups differ, but both
groups report a large range of scores. The high degree of variance in responses
among the individuals who participated in both waves ensures that this group is not
uniformly biased against Bush. As a group, they may be slightly more negative in
their assessments of Bush, but as a group there are members who hold negative,
moderate, and positive opinions of the President. In sum, while there are a few minor
differences in the participants who completed both surveys to those who completed
the first, the substantive implications are at, at best, minimal.
In addition to the issue of sample attrition, it is also useful to look at the
stability of responses for those participants who completed both surveys. This is
important because if there are no changes in responses over time, then it is impossible
to determine what factors make change more likely. I assess stability by looking at
196
the correlations in responses to the questions in both waves of the study, where higher
correlations indicate higher stability. It should be noted that by stability, I simply
mean stability in each person’s relative standing on these measures. Recall, that the
information-seeking hypothesis states that ambivalence makes individuals more likely
to seek out information. This means that even if all people generally become more
interested in the campaign over time, the ambivalent should be associated with the
largest increases. In fact, a high correlation in these measures can be seen as initial
evidence against the information-seeking hypothesis, as it would suggest that
decisions to seek out information are based upon stable predisposition and not driven
by other factors such as ambivalence.
As Table 4.4 shows, there is a high level of stability in the ambivalence and
information-seeking variables. First, the correlations among the ambivalence
measures range from about .75 for Kerry ambivalence to roughly .85 for both Bush
and decision ambivalence. Those people who experience high levels of ambivalence
in the first wave are the same people who experience such feelings in the second
wave. The same can be said for information-seeking behavior, as it also shows a high
level of stability. The correlations range from .67 for attention to Kerry to .79 for
general attention to the presidential campaign. In sum, a person who was more likely
to be attentive to the campaign at the time of the first wave was also likely to be
paying attention to the campaign a few months later.
Overall, the data shows a remarkable level of stability in candidate attitudes
and information-seeking behavior. This stability, in turn, will make it more difficult
to explain changes in behavior over time, as there is not much change to explain.
197
This concern noted, it remains important to examine whether the ambivalent were
more likely to increase their information-seeking behaviors.
Information-Seeking Behavior: Statistical Model
Panel data are exceptionally useful for examining changes in behavior, but its
use raises some important methodological issues. The first issue is the possibility of
reciprocal causation between the dependent and independent variables. While there is
reason to believe that ambivalence might cause information-seeking behavior, it is
also possible that those individuals who seek information are likely to develop
ambivalent attitudes. The use of OLS in the presence of reciprocal causality is
inappropriate as the resulting coefficients can be biased and inconsistent. While not
shown here, I conducted a number of structural equation models to test for reciprocal
causality and was unable to find any evidence that such a process occurs. The lack of
a reciprocal relationship makes standard regression techniques appropriate.
A second concern is the problem of regression to the mean. While we hope
that a variable provides an unadulterated value of the concept of interest, this is rarely
the case. In some cases we will get measures that lie above the true value and at other
times we will get values that fall below the true value. As Finkel states, this in turn
produces a "tendency of individuals or units with large values on Y at one point in
time to have smaller values at a subsequent time, and the tendency of individuals with
small values on Y to have larger subsequent values” (8). This process generates a
negative correlation between values taken at two points in time and a failure to
account for this problem can underestimate the impact that an independent variable
has on changes in the dependent variable. For example, in my analysis, I risk
198
concluding that there is no relationship between ambivalence and informationseeking behavior, despite the actual existence of such a relationship (see Finkel 1995,
for more discussion of this issue). One means of controlling for this problem is to
include a lagged value of the dependent variable as an independent variable. The
extent to which this will be an effective solution, however, is attenuated by how much
measurement error plagues the dependent variable. Simply stated, the more
measurement error exists, the less useful it will be in controlling for the problem of
negative correlation. One effective means of eliminating, or at least reducing,
measurement error is to draw upon multiple indicators when operationalizing the
concept. As discussed earlier, I employ indices for each of my dependent variables.
Therefore, while I cannot claim to have removed all measurement error from my
variables, it is reasonable to believe that I purged a sizable portion of this error from
my analysis.
In addition to the methodological issue of measurement error, there is also a
theoretical concern about the appropriate model to use when assessing panel data.
While one must include a lagged measure of the dependent variable as one of the
independent variables in the model, one has the option of either (1) including lagged
measures of the key independent variables or (2) including an independent variable
measured during the second wave. In other words, I can examine either of the
following two models:
(1) Information (t) =
+ Information (t-1) + Ambivalence (t-1) + Controls + error
(2) Information (t) =
+ Information (t-1) + Ambivalence (t) + Controls + error
199
Methodologically, neither model is “better” than the other. For both, the ambivalence
variable included on the right hand side of the equation will determine whether
ambivalence is related to changes in information. The key factor when choosing
between the two is the length of time one theoretically believes it should take for
ambivalence to exert such an influence. If the amount of time it takes the
independent variable (i.e. ambivalence) to influence the dependent variable (i.e.
information-seeking behavior) roughly equals the gap between the data collection
time points, then the lagged model is appropriate. Conversely, if it assumed the effect
of the independent variable on the dependent variable is virtually contemporaneous,
or at least much shorter than the gap between the two measures, then the latter
contemporaneous model is appropriate. This choice, however, is based purely on
one’s theoretical expectorations.
Unfortunately, there is no clear-cut answer to which model is appropriate for
my analysis as this is the first study that systematically tests this process. One can
make a reasonable argument to justify either model. In support of the first model, it
could be said that the time period between the two waves is not terribly long and that
it is reasonable to assume behaviors measured in early April will influence behaviors
measured at the end of May. Conversely, it could also be argued that the difficulty in
documenting attitude-behavior links in general suggests that I should opt for models
that allow for the closest possible linkage between the two measures. Rather than try
to resolve this issue here, I will instead acknowledge the legitimacy of both
approaches and estimate both models to see if the data itself can provide an answer.
Finally, it should be noted that the choice between models becomes somewhat
200
irrelevant when there is a high degree of stability in the independent variables over
time – as clearly is the case with the data employed here.
Information-Seeking Behavior: Results
In order to determine the relationship between ambivalence and informationseeking behavior it is necessary to control for other factors. First, I include dummy
variables to control for the sex and race of the participants, where values of one
indicate females and non-whites.42 Second, I include a dummy variable to determine
the effects of both Democratic and Republican party identification, where
independent leaners are included as partisans. Although I do not anticipate a partisan
difference when assessing general attention or media use, it is possible that
Democrats and Republicans will pay more attention to Kerry and Bush respectively.
Third, I use the folded party-identification measure to assess partisan strength and
expect strong partisans to pay more attention to the campaign. Next, I include indices
for both trust in government and efficacy. Individuals who have more favorable
views of government or who believe they are more likely to impact the government
should be more likely to pay attention to information about the election. Similarly, I
include indices that assess both the need for cognition and evaluation. I expect
people who have a general predisposition for thinking or evaluating issues to be more
likely to heed the events of the campaign. Finally, I include a political sophistication
index and expect that it will be positively related to information-seeking behavior.
The first dependent variable I examine is general attention to the presidential
campaign. The results for the effects of objective ambivalence are presented in Table
42
Given that this is a student sample, I have opted not to include the standard SES controls of age,
education and income, as there will be little variance in these measures.
201
4.5. The first two columns provide the simple cross-sectional OLS analysis for each
wave of the study. Although these results cannot be used to determine causality, they
are instructive for determining which people are generally more likely to pay
attention to the campaign. Looking at the control variables, the results show that
efficacy, cognition, sophistication, and partisan strength are positively related with
attention to the campaign in the first wave, while only race and sophistication are
positively related in the second wave. Conversely, women are less likely to report
paying attention to the campaign in either wave. More importantly, virtually all of
the ambivalence measures are significantly related to attention to the campaign. Bush
ambivalence is positively related to attention in both waves, while Kerry ambivalence
is positively related only in the second wave. In contrast to the experimental findings,
decision ambivalence is negatively related to attention to the campaign in both waves.
Simply stated, people who show higher levels of conflicted feelings when choosing
between Kerry and Bush are also less likely to report paying attention to the
campaign.
The last two columns of Table 4.5 examine changes in attention to the
campaign between the two waves. Specifically, the third column presents the results
from the lagged model (equation 1 above) and the final column presents the findings
of the contemporaneous model (equation 2 above). Interestingly, Republican Party
identification is the only control variable that predicts changes in attention;
Republicans become less attentive to the campaign during this period of time.
Turning to the effects of ambivalence, I find that Kerry ambivalence is unrelated to
changes in attention, whereas Bush ambivalence is positively related to increased
202
attention in the contemporaneous model. Lastly, decision ambivalence is negatively
related to increased attention in both models. Thus, not only do the decisionally
ambivalent report paying less attention to the campaign in a standard cross-sectional
survey, they also become less attentive to the campaign over time. In other words,
they start with low levels of campaign attentiveness and end with even lower levels.
As discussed earlier, due to their absence in first wave questionnaire, I can
only examine the cross-sectional relationship between subjective ambivalence and
information-seeking behavior in the second wave as well as the contemporaneous
model. The results are presented in Table 4.6. In contrast to the objective
ambivalence measures, none of the subjective ambivalence measures are significantly
related to attention in either model.
It is possible that a measure of general attention is not the best means of
picking up on information-seeking behavior. Therefore, I use a more direct measure
of information-seeking behavior by asking respondents to report on their media use.
Rather than rely on one’s sense of his or her attentiveness to the campaign, these
questions ask respondents more objective questions about their actual media habits.
The results for objective ambivalence’s impact on media use are presented in Table
4.7 and are similar to those reported above. In the second survey, the cross-sectional
models again show a positive impact of both Bush and Kerry ambivalence and a
negative impact of decision ambivalence. In sum, an individual who is ambivalent
about Bush or Kerry is more likely to report media use, while one who is conflicted
between the two is less likely to do so. An examination in changes in media use (the
last two columns) shows that both Bush and Kerry ambivalence do not impact
203
changes in media use. Decision ambivalence, however, is significantly associated
with decreased media use in the contemporaneous model. As was the case with
general attention, a person who is conflicted in his or her choice for the presidency is
more likely to walk away from the media, than to engage them. Finally, as presented
in Table 4.8, I find that subjective ambivalence is unrelated to media use in either
model.
As a final test of the information-seeking hypothesis, I examine the
relationship between ambivalence and attention to each candidate. A person who is
ambivalent about Kerry may not become generally attentive to the campaign, but
rather become interested in information about Kerry. The results for objective
ambivalence are presented in Table 4.9 and do not notably change from the above
analysis. First, the cross-sectional results show that candidate ambivalence is
positively related to attention to Kerry’s campaign, namely Kerry ambivalence is
significant in both models and Bush ambivalence is significant in the first wave. In
both waves, decision ambivalence is negatively related to attention to information
about Kerry. As shown in the last two columns, neither Bush nor Kerry ambivalence
are significantly related to changes in attention to Kerry’s campaign. Conversely, in
both models, decision ambivalence is associated with decreased attention to Kerry.
Consistent with the above findings, those respondents who were conflicted between
Bush and Kerry are generally more likely to report paying less attention to Kerry’s
campaign. Furthermore, these people are more likely to become less attentive to this
information over time.
204
It is also worth pointing out the consistent negative impact of Republican
Party identification on attention to Kerry. Republicans are less likely to report an
interest in information about Kerry in the cross-sectional models and are more likely
to decrease their interest in such information as the campaign progressed. This
suggests that partisans are, in fact, more likely to pay attention to their own candidate
during a campaign. Finally, Table 4.10 provides the results for subjective
ambivalence and indicates that there is no significant relationship between subjective
ambivalence and attention to Kerry’s campaign.
Finally, the last dependent variable I examine is attention to Bush’s campaign.
By and large, the results are similar to the other findings (see Table 4.11). Candidate
ambivalence is again found to be positively related to attention to Bush in the crosssectional analysis, while the decision ambivalence coefficient is negative. In contrast,
however, to the other models, none of the ambivalence measures are significantly
related to changes in attention to the Bush campaign. Thus, while ambivalence is not
found to increase attention to information about Bush, there is no evidence that it
suppressed it. It is again worth noting the effects of partisanship, namely the negative
relationship between Democratic identification and attention to Bush in the crosssectional analysis for the first wave of the panel. Albeit the negative effect of
Democratic identification on attention to Bush is not as consistently significant as the
negative impact of Republican identification on attention to Kerry, the trend is the
same. This further suggests a partisan bias in the amount of attention the electorate
devotes to the candidates. Simply stated, partisans are more likely to pay attention to
their own party’s candidate during the campaign. Finally, as shown in Table 4.12,
205
subjective ambivalence is generally unrelated to attention to Bush. The one exception
is the positive relationship between Kerry ambivalence and attention to Bush in the
second wave cross-sectional analysis. Those respondents who felt subjectively
conflicted about Kerry were more likely to report that they were paying attention to
the Bush campaign.
In sum, the findings presented above are highly consistent. First, the results
from the cross-sectional surveys indicate that candidate ambivalence is positively
related to information-seeking behavior. Conversely, the panel results reveal only
one instance of a significant relationship between candidate ambivalence and
information-seeking behavior. This indicates that while candidate ambivalence is
related to information-seeking behavior, it does not lead to increases in such
behavior. That is, the candidate ambivalent may pay more attention to the campaign
than do the non-ambivalent at any given time; however, the relative size of this
“attention gap” does not change over the course of the campaign. This, coupled with
the stability of ambivalence over the course of this panel, suggests that there is some
third factor that leads these people to develop both ambivalent attitudes and pay
attention to the campaign. Perhaps the candidates have raised a set of issues that the
individual deems important, but that pull this person in both directions. As a result,
this person is drawn to the campaign as a function of which issues are highlighted, but
is simultaneously conflicted by what he is learning.
Second, there is consistent evidence that objective decision ambivalence
decreases information-seeking behavior. Overall, objective decision ambivalence
exerts a negative impact in five of the eight panel models. Simply stated, people who
206
are more conflicted in choosing between the candidates are more likely to become
less attentive to information about the candidates over the course of the campaign.
There is no evidence, however, linking subjective ambivalence with informationseeking behavior. The impact of decision ambivalence presented in this chapter
counters the results in the experimental chapter. In that analysis, I found that decision
ambivalence had a positive impact on information-seeking behavior. There are a
couple of possible explanations for this discrepancy, and I will return to this issue in
more detail in the final section of this chapter.
Caveats
There is one potential concern with the above analysis that merits some
further attention, namely that correlation between decision ambivalence and both
Kerry and Bush ambivalence hovers around .8 in each wave. Consequently, by
including each variable in the analysis, this may generate multicollinearity, thereby
potentially masking the true effects of each variable.43 Although there is no statistical
solution for multicollinearity, I have attempted to assess the extent of this problem by
running two additional models. In the first, model I simply exclude the decision
ambivalence measure, while in the second model I drop both Kerry and Bush
ambivalence. I estimate both models for each of the four information-seeking
variables examined in this chapter. While I do not present the actual results of these
estimates, I have summarized the findings and present them in Table 4.13. First, as
indicated in the first column (model 1), I find a negative relationship between
43
This concern was not raised in the previous chapters because, unlike here, the effects of each
ambivalence measure are largely insensitive to model specification. In other words, I have conducted
the analysis discussed below for the other datasets, but this is the only instance in which the analysis
yielded substantive differences that merits discussion.
207
candidate ambivalence and information-seeking behavior when decision ambivalence
is dropped from the model. Second, as indicated in the second column (model 2), I
find that the effects of decision ambivalence do not change with model specification.
When compared to the third column (model 3), I find that the effects of candidate
ambivalence varies based on model specification, while the effect of decision
ambivalence does not.
Given these results, this begs the question – which model correctly indicates
the relationship between ambivalence and information-seeking behavior? A closer
inspection of the data indicates that the full model (model 3) is correct. First, there is
an unusually high correlation between the two objective candidate ambivalence
measures; specifically, Bush ambivalence and Kerry ambivalence have .41 and .54
correlations in the first and second waves respectively. In contrast, the mean
correlation between Democrat ambivalence and Republican ambivalence is .25 for
the six NES years examined in the previous chapter. This means that those panel
participants who held objective ambivalent attitudes about Kerry also tended to hold
similar ambivalent attitudes about Bush. In turn, this indicates that participants who
were ambivalent about a candidate were also likely to be ambivalent about their vote
choice. Thus, the negative relationship I find between candidate ambivalence and
information-seeking behavior in model 1 is likely a function of the fact that candidate
ambivalence serves as an imperfect measure of decision ambivalence. The robust
negative relationship between decision ambivalence and the information-seeking
measures further suggests that its impact, and not candidate ambivalence’s, is truly
negative. By including decision ambivalence in the full multivariate analysis (model
208
3), I now determine the impact of candidate ambivalence for those individuals who
are not conflicted between the two candidates. I find that individuals, who are
ambivalent about one candidate, but not the other, are more likely to pay attention to
the campaign. Though, as noted, they are not more likely to become more interested
in the campaign.
The discovery that the effect of candidate ambivalence can change when
controlling for decision ambivalence is important not only for this study of
ambivalence, but for all studies. It indicates that unless one includes a measure of
decision ambivalence, then one risks falsely attributing the effects of decision
ambivalence to candidate ambivalence.
Results: Abstention
In this section I conduct a final set of tests to examine the relationship
between ambivalence and turnout decisions. The first dependent variable I examine
is each person’s own assessment of his or her likelihood of voting in the upcoming
election. Specifically, I use responses to the following question:
While most people would prefer to vote in Presidential elections, there
are often many legitimate reasons why people are unable to vote in an
election at all, such as health reasons, time constraints, as well as lack of
interest in the candidates. At this point in the campaign, how likely do you
think it is that you will vote for any candidate in the Presidential election
this fall?
The respondents could report that they were not at all, somewhat, pretty, very, or
extremely likely to vote. Unfortunately, the extremely likely option was not included
in the second survey, thereby limiting to a certain extent my ability to directly
compare the results in the two waves. Nonetheless, as will be seen below, the
209
findings in both waves show a consistent and clear pattern of results. Thus, this
administrative error does not limit in any important way my ability to make firm
conclusions from this data.
Before turning to the analysis, it is first useful to look at the distribution of
responses to this question in the two waves. Not surprisingly, the distributions are
skewed towards responses indicating a high likelihood of voting. In the first wave, a
little over 60% of the respondents report that they are extremely likely to vote in the
upcoming election, while nearly 70% pick the top category of very likely in the
second wave. No doubt many of my respondents sincerely believe that they are going
to vote in the presidential election, but many likely overreported their vote intention
out of a desire to present themselves as good citizens. This skewed distribution limits
the variance in the data and makes it harder to find any significant relationships
between vote intention and the independent variables. In short, this provides a more
difficult test for the abstention hypothesis.
To examine the effects of ambivalence on turnout, I first run a simple model
that includes only the three ambivalence measures. For the second model, I add a
series of control variables to account for the other factors that might influence turnout
decisions. Specifically, I use dummy variables to control for both gender and
minority status. Next, I include the efficacy, trust in government, and sophistication
indices described above, where each should be positively related voter turnout.
Third, I include the folded partisan strength variable and anticipate stronger partisans
to be less likely to abstain. Finally, I include a dummy variable that measures past
turnout behavior. Specifically, a person is coded as a one whether he or she has voted
210
in either the 2000 or 2002 state or national elections. Respondents who have a recent
history of voting should be more likely to report an intention to vote in this election.
The small number of response options to the turnout question necessitates the
use of an ordered probit estimation procedure. The results for the first survey are
presented in Table 4.14 and show that candidate ambivalence is insignificant in both
models. Decision ambivalence, however, is significant and negatively related to selfreports of vote likelihood at the .01 level in the simple model and at the .10 level in
the full model. I transform these results into predicted probabilities in order to assess
better the impact of ambivalence on turnout decisions. Specifically, for each
ambivalence measure, I set each of other variables at their mean value and calculate
the probabilities of each response option when ambivalence is placed both at its
minimum and maximum. I then take the difference between these values by
subtracting the maximum values from the minimum values, where positive values
indicate that ambivalence makes the outcome more likely. The results are presented
graphically in Figure 4.1. Looking at this figure, it is worth noting that the impact of
decision ambivalence is concentrated on the likelihood of reporting that one is
extremely likely to vote. The probability of such a response drops by roughly .6-.8 in
both models. For instance, in the full model, the probability that a respondent reports
being extremely likely to vote is roughly .92 when experiencing no decision
ambivalence. The probability drops to .35 when one has the maximum amount of
decision ambivalence, a .57 decrease in the likelihood of such a response. To a
certain extent, it is not surprising the greatest impact is on the probability of
responding extremely likely. The data, after all, was highly skewed towards this
211
response. This noted, the magnitude of the effect is striking, clearly demonstrating
that increased decision ambivalence decreases motivation to vote.
The analysis is repeated in the second wave and the results are presented in
Table 4.15. Decision ambivalence again exerts a strong and consistent negative
impact on vote likelihood. The predicted probabilities are again calculated and
presented in Figure 4.2, showing that increased decision ambivalence strongly
decreases the likelihood that one reports being very likely to vote (the top response
option in this wave). The probability that one reports being very likely to vote drops
from a near certainty of .99 when no decision ambivalence is present to about .03
when one experiences the maximum level of decision ambivalence. In contrast to the
results from the first wave, candidate ambivalence is now found to significantly
influence turnout decisions. Specifically, Kerry ambivalence is positively related to
turnout in both models. For instance, a person who experiences the minimum amount
of ambivalence about Kerry has a .44 probability of reporting being very likely to
vote, while a person who experiences the maximum amount of ambivalence has a .95
probability of making such a statement. Although this counters my original
expectation, it does accord with the NES results.
Finally, I examine the effects of subjective ambivalence in the second wave
survey. These results are presented in the last two columns of Table 4.15 and the
predicted probabilities are presented in Figure 4.3. Simply stated, subjective decision
ambivalence reduces the likelihood that respondents believe they will vote, while
neither candidate ambivalence measure is significant. Figure 4.3 shows, as before,
that the primary impact of subjective decision ambivalence is on the likelihood that
212
one reports being very likely to vote. The probability that a person who experiences
no decision ambivalence reports being very likely to vote is roughly .9, but drops to
about .5 when decision ambivalence is set to its maximum.
As a second means of testing the abstention hypothesis, I employ the
propensity to vote index introduced in the experimental analysis. Scores on this index
are measured in increments of .2 and range from zero to four, thereby making OLS
regression the appropriate technique. The results for the impact of objective
ambivalence are presented in Table 4.16. Candidate ambivalence exerts no effect,
while decision ambivalence is negatively related to one’s propensity to vote at the .05
level in both models. Simply stated, a one unit increase in objective decision
ambivalence yields roughly a .35 decrease on this index. Thus, for example, a shift
from a decision ambivalence score of -2.8 (the 33rd percentile score) to a score of -1.2
(the 67th percentile score) yields roughly a .5 point decrease on this scale – roughly
the same effect as voting in a previous election (.64 points). In other words, a
moderate increase in decision ambivalence drops the vote propensity score by nearly
the same amount that past voting behavior increases it. Clearly, decision ambivalence
exerts both a substantive, as well as significant, influence on voter turnout.
Finally, the impact of subjective ambivalence is presented in Table 4.16.
There is marginal evidence that subjective decision ambivalence is negatively related
to turnout, as the coefficient is significant at the .11 level in the simple model but
insignificant in the full model. Individuals who feel subjectively torn when choosing
between Kerry and Bush were generally no more or less likely to report a higher
motivation to vote in the presidential election. Subjective ambivalent feelings about
213
Bush, however, are significantly and negatively related to the propensity to vote
index. Simply stated, individuals who reported having mixed feelings about Bush
were less likely to record a propensity to vote in the upcoming presidential election.
While the negative impact of subjective Bush ambivalence does confirm my
initial expectations, there are reasons to be cautious about inferring too much from
this one finding. First, it must be kept in mind that the bulk of evidence in all of the
chapters indicates the candidate ambivalence has either no effect on turnout decisions
or increases the likelihood that one votes in the election. As in the preceding chapter,
this case may simply be a statistical anomaly. However, assuming that this is
indicative of a real relationship, then there is a reasonable post-hoc explanation for
this relationship. More than most elections, the 2004 presidential campaign was seen
by many as a referendum on the incumbent, George W. Bush. Months before it was
known that John Kerry would be the Democratic nominee, there was a growing
“anyone but Bush” campaign.44 Additionally, while a number of people supported
many aspects of the Bush presidency, there was clearly a growing sense of concern
about his policy in Iraq. In fact, it was not uncommon to hear people state that they
were looking for a reason to vote for the Democratic candidate, but had yet to find
such a reason. No doubt, many of these people are those who were subjectively torn
about Bush. They could not completely back away from their support of his
candidacy, but had yet to be convinced that he deserved a second term (or, at least,
44
As an idiosyncratic example of this, one of my own friends worked in the summer of 2004 for the
Service Employee’s International Union (SEIU) to help register new voters in Pennsylvania. At one
point, I was speaking to him about his work in support of the Kerry campaign. He immediately
corrected me, informing me that he was not working as part of a pro-Kerry campaign, but on an antiBush campaign. To him, this was not an election about putting a Democrat in office, but rather about
removing the Republican.
214
that the opponent deserved a first). While many of these people surely made a
decision, it is equally likely that many simply opted not to vote. As just mentioned,
however, although this may be a plausible explanation for this finding, it is important
to note that this is but one finding indicating a positive relationship between candidate
ambivalence and abstention compared with a litany of evidence that indicates
candidate ambivalence is unrelated to turnout.
The effect of subjective Bush ambivalence aside, the results for the abstention
models are highly consistent with my findings in the previous chapters. First, I again
find virtually no evidence linking candidate ambivalence with turnout decisions. By
far, more often than not, candidate ambivalence is unrelated to any of the abstention
measures. Second, there is strong evidence showing a negative relationship between
decision ambivalence and abstention. Simply stated, the more conflicted one is when
choosing between Kerry and Bush, the less likely this person is to report a desire or
willingness to vote in the upcoming election.
Discussion
In this chapter I utilized a panel dataset to examine the electoral implications
of ambivalent attitudes. One of the striking findings in this dataset is the high degree
of stability in my measures of information-seeking behavior and candidate attitudes.
Quite simply, the same people who experience a high degree of ambivalence or are
paying a lot of attention to the campaign in the first wave are the same people who do
so two months later.
There are two possible explanations for this finding. First, I may have
collected data too early in the campaign process to assess changes in behaviors and
215
candidate attitudes. That is, while many people may be paying attention to the
candidates as early as spring 2004, they may not yet be invested enough in the
campaign to notably change their behaviors in response to such considerations as a
conflicted candidate attitude. From this standpoint, the stability in my variables is a
product of measuring these concepts just after the primaries commenced.
Ambivalence may ultimately lead to information-seeking behavior, but not until some
time during the summer or fall.
Conversely, the second explanation posits that ambivalence and attention to
the campaign are stable personality traits. If true, then in each election there is a
segment of the population that forms stable ambivalent attitudes about the candidates
and who are also attentive to information about the candidates throughout the
campaign season. Once established, these attitudes and behaviors remain fixed over
the course of the campaign. From this perspective, the stability I find in my measures
is a function of stable predispositions of the electorate. Unfortunately, the data I have
collected does not allow me to definitively test these hypotheses against each other.
Though an answer is worth pursuing, I will simply acknowledge the existence of
stability in my dataset at this point. The answer must be left for another study.
The stability issue aside, I find a consistent pattern of results that complement
the analysis in the two preceding chapters. First, I show that candidate ambivalent
attitudes do not affect turnout decisions, but that decision ambivalence is positively
linked to abstention. The more conflicted an individual is in choosing between the
candidates, the more likely this person is to bypass the decision and stay home on
216
Election Day. Moreover, this finding is robust; the pattern holds for both subjective
and objective ambivalence as well as in each measure of turnout.
Although mostly consistent with my past findings, the effect of ambivalence
on information-seeking behavior is more nuanced. First, I find that candidate
ambivalence is generally unrelated to information-seeking behavior. Individuals who
hold conflicted attitudes about either Bush or Kerry are no more (or less) likely to
increase the amount of attention they devote to the campaign. Subjective
ambivalence is likewise shown to have no effect on attention to the campaign. The
opposite is true for objective decision ambivalence; specifically, I find that it is
negatively related to information-seeking behavior. My results show that those
people who are objectively torn in choosing between Kerry and Bush become less
attentive to the campaign over time. In contrast, subjective decision ambivalence is
inconsequential. This means that having conflicted views on the vote decision makes
one less likely to see information. Conversely, a subjective awareness of being
conflicted does not have the same effect. This begs the question raised before – why
do I find a negative relationship between objective decision ambivalence and
information-seeking behavior in a survey dataset, but a positive relationship with
subjective decision ambivalence in my experimental analysis?
First, as I argued earlier, we should bear in mind that these findings do not so
much counter the experimental results, but rather confirm that subjective and
objective ambivalence are distinct concepts. In my experimental analysis I find that
subjective ambivalence affects information-seeking behavior, while objective
ambivalence has no influence. The converse is true in the two survey datasets;
217
objective ambivalence is a significant influence, while subjective ambivalence is
shown to be unrelated. The fact that objective and subjective ambivalence are distinct
factors, albeit interesting and important, does not fully answer why they behave
differently in separate arenas. Objective and subjective ambivalence may have
unique characteristics, but both are nonetheless forms of the larger concept of
ambivalence. Thus, one should still expect them to exert similar influences.
One possible explanation for the difference between the survey and
experimental findings is the lack of partisanship in the experimental analysis.
Specifically, it may be the case that knowing a candidate’s party affiliation alters
which types of people are more likely to develop ambivalent attitudes about the
candidate. As I previously noted, partisans appear to develop ambivalent attitudes
about their own party’s candidate; that is, Democrats are more likely to view Kerry
ambivalently and Republicans are more likely to be conflicted about Bush. Although
not shown, this panel data again provides evidence to support this claim. First, using
both Bush and Kerry objective ambivalence as my dependent variable, I run a series
of simple OLS regression models and generally find that partisans are more likely to
be ambivalent about their own candidate. More importantly, the results notably differ
when I use the same models to predict subjective candidate ambivalence. First, party
identification is unrelated to feeling subjectively torn about Bush; Republicans are no
more or less likely to hold subjective ambivalent attitudes. Interestingly, however, I
find a positive relationship between being a Republican and feeling subjectively
ambivalent about Kerry. That is, while Democrats are significantly more likely to be
218
associated with higher objective Kerry ambivalence scores, Republicans are
significantly more likely to feel subjectively torn about him.
This suggests that while partisans may be willing to acknowledge both the
existence of positive and negative traits of their own party’s candidate, thereby
producing objective ambivalence, this does not necessarily translate into feelings of
subjective ambivalence. Ultimately, this partisan effect means that the ambivalent
attitudes reported in my survey analysis are potentially fundamentally different than
the ambivalent attitudes created in my experimental analysis. Or, at a minimum,
demonstrates that partisanship attenuates the development and, in turn, the effects of
ambivalence in an electoral context.
Another possible explanation for the different effects of subjective and
objective ambivalence is that my panel survey was conducted immediately following
the primary campaign. It may have been too early in the process for the electorate to
give more than cursory attention to the candidates. The panel study results indicate
that subjective ambivalence does not cause an increase in information-seeking
behavior. This noted, it is nonetheless still possible that subjective ambivalence
causes such behavior, but that this does not occur until later in the campaign. In other
words, people who are subjectively torn in choosing between the two candidates may
in fact be more likely to engage the campaign and seek out information, but simply
not until a time nearer to Election Day. Although both my panel and NES analysis
reveals the same effects for objective ambivalence, it is important to remember that
the NES does not ask questions about subjective ambivalence. Thus, the lack of a
relationship between objective decision ambivalence and information-seeking
219
behavior in the NES analysis does not preclude the existence of a causal effect of
subjective ambivalence on information-seeking behavior in the two directly preceding
the election.
Caveats noted, the results in this chapter are highly congruent with the
previous findings. The similarities uniting these chapters far outstrip their
differences. In sum, candidate ambivalence does not affect turnout decisions, while
decision ambivalence increases the likelihood that one will abstain in an election.
Second, there is no evidence in support of the information-seeking behavior
hypothesis. In fact, the evidence indicates that decision ambivalence leads people to
become less engaged with the campaign. Nonetheless, there are some differences and
it is important to further pursue the reasons why they have emerged. While my
findings cast light on a number of important aspects of ambivalence, they also reveal
a number of new questions that merit attention in future ambivalence studies.
220
Open-Ended
Bush Positivity
Open-Ended
Positive Feelings
Reasons to Support
Positive Affect
Bush Negativity
Open-Ended
Negative Feelings
Reasons to Oppose
Negative Affect
Positive Feelings
1.00
0.67
1.00
0.70
0.85
0.65
0.84
n = 287; Cronbach'
s Alpha = .92
Reasons to Support
1.00
0.85
Open-Ended
Negative Feelings
Reasons to Oppose
1.00
0.65
0.66
0.66
1.00
0.83
0.79
1.00
0.85
n = 287; Cronbach'
s Alpha = .90
Kerry Positivity
Open-Ended
Positive Feelings
Reasons to Support
Affect
Open-Ended
Positive Feelings
Reasons to Support
1.00
0.53
0.64
0.51
1.00
0.75
0.73
1.00
0.74
n = 287; Cronbach'
s Alpha = .90
Kerry Negativity
Open-Ended
Negative Feelings
Reasons to Oppose
Negative Affect
Open-Ended
Negative Feelings
Reasons to Oppose
1.00
0.56
0.55
0.51
1.00
0.77
0.62
1.00
0.68
N= 285; Cronbach'
s Alpha = .85
Table 4.1. Correlation Among Ambivalence Measures
221
Bush
Factor
Positive
Negative
1.00
2.00
3.00
4.00
3.03
-0.02
-0.06
-0.07
2.93
-0.04
-0.05
-0.09
Kerry
Factor
Positive
Negative
1.00
2.00
3.00
4.00
2.56
0.03
-0.11
-0.13
2.41
-0.05
-0.07
-0.14
Table 4.2: Factor Analysis Results of Positive and Negative Measures: Eigenvalues
222
Wave 1
Wave 1 & 2
Probability
0.40
10.01
4.41
13.02
9.44
0.11
1.79
0.44
0.45
5.96
2.01
2.98
0.41
0.28
0.47
9.70
4.22
12.98
10.01
0.14
1.80
0.50
0.39
6.43
1.88
2.89
0.32
0.29
0.25
0.27
0.36
0.91
0.05
0.42
0.87
0.31
0.31
0.04
0.29
0.63
0.15
0.89
2.48
2.65
2.03
2.13
2.55
2.82
2.11
2.19
0.45
0.31
0.42
0.52
-0.26
-0.04
-1.71
52.31
53.33
-0.50
-0.12
-2.03
45.22
55.05
0.03
0.39
0.08
0.06
0.57
3.15
3.32
0.22
Control Variables
Female
Efficacy
Trust in Government
Need for Cognition
Need for Evaluation**
Non-white
Partisan Strength
Democrat
Republican
Sophistication**
Class Standing
Ideology
Vote in 2000
Vote in 2002
Information-Seeking Variables
General Attention
Media Attention
Kerry Attention
Bush Attention
Candidate Variables
Bush Ambivalence**
Kerry Ambivalence
Decision Ambivalence*
Bush FT*
Kerry FT
Turnout
Self-report Likelihood of
Voting
* P < .10; ** p < .05
Table 4.3: Difference of Means and Proportions Tests: Assessing Differences in
Subjects Who Completed Both Waves to Those Who Only Completed the First Wave
Survey
223
Correlation
Ambivalence Measures
Bush Ambivalence
Kerry Ambivalence
Decision Ambivalence
.854
.751
.854
Information-Seeking Measures
General Attention
Media Use
Attention to Kerry
Attention to Bush
.785
.764
.674
.688
Table 4.4: Wave 1 and Wave 2 Ambivalence and Information-Seeking Correlations
224
Wave 1
Attention
--Bush Ambivalence
0.207**
Kerry Ambivalence
0.095
Decision
Ambivalence
-0.231***
Female
-0.222***
Efficacy
0.040**
Trust in Government
0.003
Need for Cognition
0.035**
Need for Evaluation
0.014
Minority
0.197
Sophistication
0.119***
Partisan Strength
0.139**
Democrat Dummy
-0.122
Republican Dummy
-0.156
Constant
0.376
Adjusted R2
278
N
.347
* p < .10; ** p < .05; *** p < .01
Wave 2
--0.200*
0.256**
Lagged
Ambivalence
0.630***
0.129
-0.051
Contemporaneous
Ambivalence
0.636***
0.144*
0.049
-0.309***
-0.218**
0.020
0.002
0.016
0.020
0.322**
0.085***
0.066
-0.261
-0.323
1.010***
149
.246
-0.155**
-0.075
0.014
0.005
-0.015
-0.007
0.074
0.024
-0.065
-0.211
-0.381**
0.949***
149
.651
-0.177***
-0.104
0.008
0.006
-0.017
0.000
0.085
0.016
-0.049
-0.201
-0.312*
0.938***
149
.639
Table 4.5: Effects of Objective Ambivalence on the General Attention to the 2004
Presidential Campaign Index, OLS Regression
225
Wave 2
Attention
Bush Ambivalence
Kerry Ambivalence
Decision Ambivalence
Female
Efficacy
Trust in Government
Need for Cognition
Need for Evaluation
Minority
Sophistication
Partisan Strength
Democrat Dummy
Republican Dummy
Constant
Adjusted R2
N
* p < .10; ** p < .05; *** p < .01
-0.051
-0.020
0.018
-0.150
0.021
0.003
0.028
0.025
0.267*
0.114***
0.128
-0.240
-0.383
1.088**
.164
149
Contemporaneous
Ambivalence
0.655***
-0.007
-0.017
-0.028
-0.045
0.011
0.006
-0.012
0.007
0.058
0.026
-0.015
-0.207
-0.311*
0.934***
.592
149
Table 4.6: Effects of Subjective Ambivalence on the General Attention to the 2004
Presidential Campaign Index, OLS Regression
226
Wave 1
Attention
--Bush Ambivalence
0.217
Kerry Ambivalence
-0.235
Decision
Ambivalence
-0.100
Female
-0.419***
Efficacy
0.071**
Trust in Government
0.037
Need for Cognition
0.055
Need for Evaluation
0.006
Minority
0.397*
Sophistication
0.209***
Partisan Strength
0.286**
Democrat Dummy
-1.066***
Republican Dummy
-1.082***
Constant
0.258
Adjusted R2
.279
N
278
* p < .10; ** p < .05; *** p < .01
Wave 2
--0.389*
0.562**
Lagged
Ambivalence
0.661***
-0.106
-0.158
Contemporaneous
Ambivalence
0.657***
0.166
0.154
-0.514***
-0.519**
0.051
0.151**
0.037
0.053
0.721**
0.126**
0.182
-0.483
-0.666
-0.563
.272
149
0.030
-0.095
0.020
0.099**
-0.002
0.022
0.293
0.030
-0.059
0.415
0.118
-0.163
.607
149
-0.240*
-0.133
0.014
0.092**
-0.003
0.027
0.329
0.011
-0.079
0.396
0.149
-0.306
.615
149
Table 4.7: Effects of Objective Ambivalence on the Media Use Index in the 2004
Presidential Campaign, OLS Regression
227
Wave 2
Attention
Bush Ambivalence
-0.038
Kerry Ambivalence
-0.011
Decision Ambivalence
0.043
Female
-0.455**
Efficacy
0.049
Trust in Government
0.160***
Need for Cognition
0.053
Need for Evaluation
0.058
Minority
0.670**
Sophistication
0.167***
Partisan Strength
0.263*
Democrat Dummy
-0.444
Republican Dummy
-0.780+
Constant
-0.425
2
Adjusted R
.225
N
149
+ p < .11; * p < .10; ** p < .05; *** p < .01
Contemporaneous
Ambivalence
0.681***
0.035
-0.090
0.010
-0.086
0.012
0.091**
0.011
0.036
0.299
0.025
-0.028
0.427
0.161
-0.309
.604
149
Table 4.8: Effects of Subjective Ambivalence on the Media Use Index in the 2004
Presidential Campaign, OLS Regression
228
Wave 1
Wave 2
Attention
----Bush Ambivalence
0.303***
0.163
Kerry Ambivalence
0.176+
0.242*
Decision
Ambivalence
-0.320*** -0.290***
Female
-0.163**
-0.169
Efficacy
0.054**
0.012
Trust in Government
-0.033
0.002
Need for Cognition
0.033**
0.010
Need for Evaluation
0.008
0.006
Minority
0.146
0.026
Sophistication
0.112***
0.081**
Partisan Strength
0.057
-0.042
Democrat Dummy
0.126
0.063
Republican Dummy
-0.381*
-0.555**
Constant
0.060
1.083**
Adjusted R2
.301
.231
N
278
149
+ p < .11; * p < .10; ** p < .05; *** p < .01
Lagged
Ambivalence
0.538***
0.141
0.045
Contemporaneous
Ambivalence
0.542***
0.117
0.089
-0.176*
-0.105
-0.001
0.023
-0.008
-0.010
-0.135
0.021
-0.099
-0.019
-0.496**
1.072***
.494
149
-0.166**
-0.125
-0.006
0.026
-0.010
-0.006
-0.136
0.017
-0.085
-0.012
-0.444**
1.098***
.491
149
Table 4.9: Effects of Objective Ambivalence on the Attention to Kerry Information
Index in the 2004 Presidential Campaign, OLS Regression
229
Wave 2
Attention
Bush Ambivalence
Kerry Ambivalence
Decision Ambivalence
Female
Efficacy
Trust in Government
Need for Cognition
Need for Evaluation
Minority
Sophistication
Partisan Strength
Democrat Dummy
Republican Dummy
Constant
Adjusted R2
N
* p < .10; ** p < .05; *** p < .01
---0.005
-0.066
-0.023
-0.111
0.010
-0.002
0.025
0.013
-0.003
0.106***
0.011
0.073
-0.609**
1.248***
.169
149
Contemporaneous
Ambivalence
0.578***
0.024
-0.006
-0.058
-0.077
-0.006
0.028
-0.006
0.000
-0.148
0.022
-0.061
-0.032
-0.456**
1.097***
.467
149
Table 4.10: Effects of Subjective Ambivalence on the Attention to Kerry Information
Index in the 2004 Presidential Campaign, OLS Regression
230
Wave 1
Wave 2
Attention
----Bush Ambivalence
0.181*
0.049
Kerry Ambivalence
0.128
0.281*
Decision
Ambivalence
-0.243***
-0.241**
Female
-0.152*
-0.140
Efficacy
0.044**
-0.024
Trust in Government
0.030
0.090**
Need for Cognition
0.022
0.026
Need for Evaluation
0.015
0.036
Minority
0.144
0.185
Sophistication
0.059**
0.047
Partisan Strength
0.136**
0.064
Democrat Dummy
-0.300+
-0.312
Republican Dummy
0.112
0.152
Constant
0.310
0.581
Adjusted R2
.317
.226
N
278
149
+ p < .11; * p < .10; ** p < .05; *** p < .01
Lagged
Ambivalence
Contemporaneous
Ambivalence
0.609***
0.017
0.014
0.601***
0.015
0.135
-0.086
-0.022
-0.038
0.092***
-0.004
0.022
0.016
0.035
-0.078
-0.107
0.046
0.392
.486
149
-0.137
-0.038
-0.040+
0.095***
-0.007
0.025
0.031
0.027
-0.078
-0.115
0.099
0.381
.598
149
Table 4.11:Effects of Objective Ambivalence on the Attention to Bush Information
Index in the 2004 Presidential Campaign, OLS Regression
231
Wave 2
Attention
Bush Ambivalence
Kerry Ambivalence
Decision Ambivalence
Female
Efficacy
Trust in Government
Need for Cognition
Need for Evaluation
Minority
Sophistication
Partisan Strength
Democrat Dummy
Republican Dummy
Constant
Adjusted R2
N
* p < .10; ** p < .05; *** p < .01
Contemporaneous
Ambivalence
0.609***
-0.024
0.071
-0.036
0.023
-0.039
0.095***
-0.002
0.032
-0.010
0.045
-0.045
-0.115
0.002
0.229
.481
149
---0.082
0.121**
-0.026
-0.047
-0.022
0.090**
0.032
0.041
0.107
0.079**
0.114
-0.296
-0.008
0.452
.208
149
Table 4.12: Effects of Subjective Ambivalence on the Attention to Bush Information
Index in the 2004 Presidential Campaign, OLS Regression
Kerry Ambivalence
Bush Ambivalence
Decision
Ambivalence
Model 1
Negative
Negative
Model 2
Negative
Model 3
Positive
Positive
Negative
Table 4.13: Summary of Each Ambivalence Measures Affect on Information-Seeking
Behavior
232
Bush Ambivalence
Kerry Ambivalence
Decision Ambivalence
Partisan Strength
Vote Dummy
Efficacy
Trust in Government
Sophistication
Female
Minority
Cut 1
Cut 2
Cut 3
Cut 4
N
Log-likelihood
* p < .10; ** p < .05; *** p < .01
Model 1
0.246
0.077
-0.444***
Model 2
0.003
-0.061
-0.259*
0.047
0.818***
0.051
0.041
0.122***
0.112
0.019
-1.257
-0.617
-0.193
0.391
0.125
0.866
1.337
2.019
279
-296.774
275
-262.650
Table 4.14: Effect of Objective Ambivalence on Self-Reported Likelihood of Voting
– Wave 1, Ordered Probit Regression
233
Objective Ambivalence
Bush
Ambivalence
Kerry
Ambivalence
Decision
Ambivalence
Partisan
Strength
Vote Dummy
Efficacy
Trust in
Government
Sophistication
Female
Minority
Cut 1
Cut 2
Cut 3
Subjective Ambivalence
Model 1
Model 2
Model 1
Model 2
0.205
0.255
-0.087
-0.115
0.429*
0.488*
0.011
-0.005
-0.626***
-0.712***
-0.295**
-0.320***
-1.184
-0.543
0.494
N
155
Log-likelihood
-115.217
* p < .10; ** p < .05; *** p < .01
0.114
0.520**
-0.022
0.153
0.357
-0.006
0.010
0.013
0.192
-0.575*
-0.052
0.083
0.522**
-0.657**
-1.340
-0.325
0.886
-2.478
-1.888
-0.943
-2.363
-1.417
-0.306
149
-96.634
155
-125.411
149
-105.076
Table 4.15: Effect of Objective and Subjective Ambivalence on Self-Reported
Likelihood of Voting – Wave 1, Ordered Probit Regression
234
Objective Ambivalence
Bush
Ambivalence
Kerry
Ambivalence
Decision
Ambivalence
Partisan
Strength
Vote Dummy
Efficacy
Trust in
Government
Sophistication
Female
Minority
Constant
Subjective Ambivalence
Model 1
Model 2
Model 1
Model 2
0.033
-0.046
-0.154*
-0.139*
0.175
0.219
-0.101
-0.080
-0.351**
-0.318**
-0.158+
-0.107
1.753***
-0.011
0.638***
0.049
0.032
0.545***
0.057
0.057
0.009
-0.266
0.295
0.842*
3.044***
0.015
0.058
-0.098
0.153
1.672***
155
0.076
149
0.157
N
155
149
Adjusted R2
.124
0.219
+ p < .11; * p < .10; ** p < .05; *** p < .01
Table 4.16: Effect of Objective and Subjective Ambivalence on the Wave 2
Motivation to Vote Index, OLS Regression
235
Model 1
Change in Probability
0.4
0.2
0
-0.2
-0.4
Not at all Somewhat
likely
likely
Pretty
likely
Very likely Extremely
likely
Bush
Kerry
Decision
-0.6
-0.8
-1
Vote Likelihood
Model 2
Change in Probability
0.4
0.2
0
-0.2
-0.4
Not at all Somewhat
likely
likely
Pretty
likely
Very likely Extremely
likely
Bush
Kerry
Decision
-0.6
-0.8
-1
Vote Likelihood
Figure 4.1: First Differences – Effect of Objective Ambivalence Self-Reported Vote
Likelihood: Maximum Ambivalence Probability – Minimum Ambivalence
Probability, Wave 1
236
Model 1
Change in Probability
0.6
0.4
0.2
0
-0.2
-0.4
Bush
Not at all
likely
Somewhat
likely
Pretty likely
Very likely
Kerry
Decision
-0.6
-0.8
-1
Vote Likelihood
Model 2
Change in Probability
0.6
0.4
0.2
0
-0.2
-0.4
Bush
Not at all
likely
Somewhat
likely
Pretty likely
Very likely
Kerry
Decision
-0.6
-0.8
-1
Vote Likelihood
Figure 4.2: First Differences – Effect of Objective Ambivalence Self-Reported Vote
Likelihood: Maximum Ambivalence Probability – Minimum Ambivalence
Probability, Wave 2
237
Model 1
Change in Probability
0.4
0.2
Bush
0
-0.2
Kerry
Not at all
likely
Somewhat
likely
Pretty likely
Very likely
Decision
-0.4
Vote Likelihood
Model 2
Change in Probability
0.4
0.2
Bush
0
-0.2
Kerry
Not at all
likely
Somewhat
likely
Pretty likely
Very likely
Decision
-0.4
Vote Likelihood
Figure 4.3: First Differences – Effect of Subjective Ambivalence Self-Reported Vote
Likelihood: Maximum Ambivalence Probability – Minimum Ambivalence
Probability, Wave 2
238
CHAPTER 5
CONCLUSION:
AMBIVALENCE – THE GOOD AND BAD
The analysis conducted in the previous chapters examined the consequences
of ambivalent attitudes that heretofore had received little attention. Specifically, I
focused on two primary questions. First, does ambivalence cause one to engage in
information-seeking behavior? Recent work in social psychology demonstrates that
ambivalent individuals are more likely to systematically process information when
making an evaluation (cf. Cunningham et al. 2003; Jonas, Diehl, and Bromer 1997;
Maio, Bell and Esses 1996). At the same time, studies in political science have
shown that ambivalence is positively related to accurately identifying candidate issue
positions (Meffert, Guge, and Lodge 2004) and waiting longer during a campaign to
pick a candidate to back in the election (Lavine 2001). Combined, such findings
studies suggest that the ambivalent may be more likely to seek out information about
the candidates. It stands to reason that a person who takes more time to make a
decision, holds more accurate information, and is processing information more
carefully is also a person who has sought out and acquired more information about a
candidate. The problem, however, is that a person who engages in memory-based
processing would also be more likely to have more accurate information about the
239
candidate, take longer to decide, and process this information more carefully. Thus, it
is becomes necessary to test the relationship between ambivalence and information
acquisition to determine which process best explains such results.
The second question I explored was whether ambivalence increases the
likelihood of abstention during an election. There are a number of reasons why one
might expect such a relationship to exist. From a psychological perspective we know
that individuals find trade-off reasoning difficult and generally uncomfortable (cf.
Tetlock 2000). Individuals do not like to state how much clean air they will sacrifice
in order to attain economic growth or vice-versa. Rather than solve difficult
questions of resource allocation, a much simpler choice is simply to sidestep the
decision altogether. Rational choice theory also provides an argument for why
ambivalence might lead to abstention. If one faces a decision where neither option
yields a benefit that is greater than the other, then there is no reason to expend the
resources needed to make a decision. Although based on different assumptions, the
two perspectives are not incompatible. Both approaches show how social psychology
and rational choice theories can complement each other to explain why ambivalence
might lead to abstention.
Finally, as a corollary to the abstention hypothesis, I determine whether
ambivalence leads to support for independent presidential candidates. It is important
that we not equate ambivalence with indifference, uncertainty, or apathy towards the
candidates or campaign. By definition, one can only be ambivalent by having at least
one positive and one negative thought about the candidates. This indicates that the
ambivalent have at least some minimal level of awareness and engagement with the
240
candidates. The ambivalent may want to participate in the election, but are not happy
with the options on the ballot. If true, then they view voting for the independent
challenger as especially appealing, as it provides new means of staying involved in
our democratic processes.
I investigate these two questions via a multi-methodological approach,
drawing upon National Election Studies (NES) survey data as well as two original
studies: a laboratory experiment and a panel survey of undergraduate students
collected during the spring of 2004 campaign. Additionally, I focus on two types of
ambivalence – candidate and decision ambivalence. Candidate ambivalence occurs
when an individual is conflicted about how to evaluate a given candidate, whereas
decision ambivalence refers to the conflicted feelings one has when determining
whether to vote for the Democratic or Republican candidate. Also, when possible, I
compared the influence of both subjective and objective ambivalence. The former
occurs when a person is consciously aware of his or her conflicted feelings. The
latter occurs when a person has internalized a set of positive and negative feelings,
but may not consciously feel conflicted. Combined, the results are consistent and
further elucidate how ambivalence affects electoral behavior.
Ambivalence and Abstention
The relationship between candidate ambivalence and abstention is
unambiguous. Counter to my expectations, I did not find that candidate ambivalence
increases the likelihood of abstention in my analysis. I did, however, find a positive
relationship between candidate ambivalence and candidate support in the NES data.
The more ambivalent one is about a candidate, the more likely this person is to vote
241
for that candidate. While this finding was initially vexing, I show that it is likely a
consequence of the positive skew in considerations among high ambivalence scores
and a negative skew among low ambivalence scores. High ambivalent attitudes were
generally comprised of more favorable than unfavorable considerations, while the
converse was true for non-ambivalent attitudes. I will return to the implications of
this in more detail below.
In contrast, the evidence overwhelmingly reveals that decision ambivalence
leads to abstention. My experimental analysis, the first study to my awareness that
manipulates feelings of ambivalence, demonstrates a clear causal linkage between
decision ambivalence and abstention. This relationship is verified via NES analysis
of self-reported turnout and in the undergraduate panel analysis of self-reported
likelihood of voting in an upcoming election. Overall, objective ambivalence is
consistently significant, whereas subjective decision ambivalence provides sporadic
evidence of such a link. This difference noted, both measures clearly indicate that
decision ambivalence increases the likelihood of abstention.
I also find that decision ambivalence increases the likelihood of supporting
independent candidates. This is an important, yet worrisome, finding. It suggests
that the decsionally ambivalent want to participate in our elections, but often fail to
do so as they see no viable option on the ballot. Unfortunately, it is the exception,
and not the norm, to see a viable independent candidate run for office and challenge
the Democratic and Republican nominees. Thus, we must worry that a segment of
the electorate fails to vote in many of our elections out of their dissatisfaction with the
system, and not out of apathy to the process.
242
The importance of this finding should not be overlooked. Far too often we
talk about the ways in which our citizens fail in their duties as democratic citizens.
They are portrayed as uninformed, apathetic, unengaged, or quite simply lazy. From
this vantage point, low voter turnout falls at the disinterested foot of the American
voter. There is no denying that voter apathy is a real problem in our elections and
that often our citizens could more faithfully serve democracy. But the process runs
both ways and often our democratic institutions likewise fail to serve its citizens
properly. One obligation of the system is to provide its citizens with desirable
candidates. The evidence linking decision ambivalence to independent candidate
support, however, indicates some of the electorate is in fact dissatisfied with the
candidates on the ballot. In a word, these results portray an instance in which
democracy fails to serve the needs of its citizens.
Ultimately, this finding demonstrates that we must question the extent to
which our electoral outcomes are truly representative of the public will. Given the
importance that elections play in our democratic system for connecting public desires
with governmental outputs, it is always worrisome when we identify those groups
that are underrepresented by these linking mechanisms. While we generally worry
about the representation of specific demographic groups (e.g., women, minorities,
etc.), my findings reveal that decision ambivalence mutes the voice of an important
segment of the public. The voice of the ambivalent is important because, by
definition, the ambivalent are engaged and vested in the process at some level. It is
never good to find a segment of the electorate that systematically opts not to
participate in our elections, but the fact that one of these groups is defined on the
243
basis of its attitudes towards the candidates themselves is especially troubling. This
indicates that the ambivalent have in fact engaged the process at some point, but
ultimately determined that their best choice is to simply walk away from it come
Election Day.
Ambivalence and Information-Seeking Behavior
The information-seeking findings are more complex than are the abstention
results. First, I find little evidence that candidate ambivalence is related (positively or
negatively) to information-seeking behavior. My experimental results, however,
indicate that candidate ambivalence causes memory-based processing. Specifically, I
demonstrate that candidate ambivalence is positively related both to recognition of
candidate specific information and to larger reaction times to candidate evaluations.
Combined, these results indicate that the evidence from other studies that suggest
information-seeking behavior is, instead, better explained as the consequence of
memory-based processing. The ambivalent do not seek more information, but rather
are more likely to recall the information they encounter.
Second, the relationship between decision ambivalence and informationseeking behavior is mixed. My experimental results reveal that subjective decision
ambivalence makes information-seeking behavior more likely, while objective
decision ambivalence has no effect. Conversely, my survey results show that
objective decision ambivalence, when significant, is related to decreased engagement
with the campaign. In the NES data and the panel survey of undergraduates, I find
that objective decision ambivalence decreased interest and attention in the
presidential campaigns. Moreover, in the undergraduate panel survey, I find
244
subjective decision ambivalence is unrelated to changes in interest or attention to the
campaign. Thus, the experimental and survey results appear to be nearly mirror
opposites of each other. The experimental results show no effect for objective
decision ambivalence, whereas the survey results show no effect for subjective
decision ambivalence. Conversely, where the experimental results indicate that
subjective decision ambivalence increases information-seeking behavior, the survey
results show that objective decision ambivalence decreases information-seeking
behavior. How can these seemingly contradictory findings be reconciled?
To start, one can argue to a certain extent that these are not inconsistent
findings. The experimental analysis finds a significant relationship between
subjective ambivalence and information acquisition, while the panel analysis does
not. Likewise, the survey analysis finds a significant relationship between objective
ambivalence and information-seeking behavior, while the experimental results do not.
This explanation, at best, tells only half the story and questions still remain. First,
why would one form of ambivalence be significant in one domain, but not in another?
Second, why does one set of findings show a positive relationship between
ambivalence and information-seeking behavior, while the other shows a negative
relationship?
While I cannot definitively resolve these issues, I suspect part of the answer
lies in the fact that party identification information was not included as part of the
experiment. In both the NES and undergraduate panel surveys I find that partisans
are more likely to hold objective ambivalent attitudes about their own party’s
candidate. Interestingly, I also find some evidence in the panel of undergraduates that
245
suggests that partisans may be less likely to hold subjectively ambivalent attitudes
about their own party’s candidate. Finally, in contrast, there is no relationship
between party identification and either form of ambivalence in the experimental
analysis. Simply stated, partisanship is related to ambivalence when assessing
partisan candidates, but is unrelated when assessing non-partisan candidates.
One of the implications here is that partisans may be more likely to
objectively acknowledge the potential strengths and weaknesses of their preferred
candidate, but see no reason to support the opposing candidate. That is, they may be
willing to state some of the limitations of their own candidate, but are highly unlikely
to see any positive traits in the opposing party’s candidate. Such a process would
generate ambivalent attitudes about one’s own party candidate and unfavorable
attitudes about the opposing party’s candidate. An examination of positive and
negative considerations supports the existence of such a process. I found that high
ambivalence scores tend to be skewed towards positive considerations, while lower
scores are skewed towards negative considerations. In other words, people who hold
objectively ambivalent attitudes are more likely to lean positively when viewing the
candidate; people who are not conflicted are more likely to lean negatively when
viewing the candidate.
Interestingly, while partisans are more likely to develop objective ambivalent
attitudes, there is evidence that indicates that they are less likely to develop subjective
ambivalent attitudes. One possible explanation for this finding is that subjective
ambivalence arises when an individual considers voting for the opposing party’s
candidate, such as a Republican who is contemplating a Democratic vote. From this
246
perspective, the person feels subjectively conflicted when conceding that the
opposing party’s candidate does, in fact, have some legitimate traits as a candidate. If
true, then this would explain the positive relationship between Republican
identification and subjective Kerry ambivalence. These Republicans surely saw
many downsides to a Kerry presidency, but also may have seen some positive
aspects, most notably a desire to see a change in policy in the war in Iraq. This said,
it must be noted that at this stage this is a speculative argument as there is not yet
enough evidence to provide a clear answer to this question. At this point, we must
simply bear in mind that fact that partisanship may play a significant role in the
development of ambivalent attitudes.
The above findings raise another question. Namely, if ambivalence in a
campaign setting is different than in an experimental setting, then can experimental
findings on ambivalence shed light on the campaign effects of ambivalence? Like
many academic questions, the answer is a qualified yes. First, experimental studies
on ambivalence are important because they are better suited to create pure feelings of
ambivalence; that is, ambivalence that is not a function of skewed positive or negative
considerations. Thus, if we want to isolate the unadulterated influence of pure
ambivalent attitudes, then we need experimental analysis. However, it is imperative
that we understand that “real world” ambivalence may not always be pure
ambivalence, but might be biased by other factors in our political environment. If
ambivalence is different in these two domains, then this would help explain why I
find experimental evidence showing that ambivalence causes information-seeking
behavior, but survey evidence indicating that ambivalence diminishes the likelihood
247
of such activities. Clearly, both more experimental and survey research are needed in
order to fully appreciate how ambivalence affects electoral behavior.
What Have We Learned?
Combined, this research helps illuminate the role of ambivalence for voting
behavior. As outlined above, it extends a growing body of work on the effects of
ambivalence by identifying two additional consequences of ambivalent attitudes.
Beyond identifying additional consequences of such attitudes, my analysis makes a
number of larger contributions to our general understanding of electoral decisionmaking.
First, while scholars are beginning to appreciate the effects of candidate
ambivalence, virtually no work has been done on the effects of decision
ambivalence.45 This is unfortunate, as there are reasons to suspect that it is as
equally, if not more, important as candidate ambivalence. Elections are battles
between two (or possibly three) candidates in which each seeks to demonstrate why
he or she is a better choice for office than are the others in the campaign. Thus, the
key decision facing the electorate in each campaign is not determining whether or not
they like the Democratic or Republican candidate, but whether they like one
candidate more than the other. Decision ambivalence assesses the extent to which
one feels conflicted about this latter judgment.
My research addresses this deficiency by devoting significant attention to the
effects of decision ambivalence. In fact, my findings consistently show that decision
ambivalence is much more likely than candidate ambivalence to affect electoral
45
Lavine (2001), who first developed the concept of decision ambivalence, devotes only a few
paragraphs to the concept in his study. To my awareness, this is the only study to use this concept.
248
behavior. Given the nature of elections themselves, the consistent effects of decision
ambivalence should come as no surprise. Yet far too often we fail to account for this
aspect of an election. In psychological terms, we have a myopic focus on only one
type of attitude object (the candidate) and are overlooking another (the vote decision
itself) that can further our understanding of voting behavior.
Perhaps the most notable example of how decision ambivalence adds to our
theoretical understanding is the positive relationship I find between decision
ambivalence and both abstention and independent candidate support, such as the
positive relationship between decision ambivalence and abstention. This finding not
only sheds light on what happens during our elections, but also fundamentally alters
the manner in which we interpret these outcomes. Rather than scold nonvoters for
their apathy or indifference to the campaign, the decision ambivalence results suggest
that a segment of the population is more likely to abstain because of dissatisfaction
and frustration with the political process. Thus, by including decision ambivalence in
my analysis I am able to gain theoretical leverage to explain voting behavior that is
unavailable to studies that focus solely on candidate attitudes. Clearly, future studies
of ambivalence should devote more attention to the effect of the electorate’s
conflicted attitudes about their vote choice.
Second, my results contribute to our understanding of electoral behavior by
taking seriously the distinctions between different forms of ambivalence, most
notably the distinction between objective and subjective ambivalence. Despite the
recent work in social psychology that indicates that ambivalence is not a monolithic
concept, political scientists by and large still treat ambivalence as such. Unlike other
249
studies that examine one form of ambivalence (but see McGraw, Hasecke, and
Conger 2003), much of my analysis directly compares the effects of objective and
subjective ambivalent attitudes. Ultimately, my results demonstrate the importance of
differentiating not only between objective and subjective ambivalence, but also
between candidate and decision ambivalence, as well as between “true” ambivalence
and ambivalence that develops as a product of partisan politics.
Too often, political scientists jump headfirst into the “consequences” pool
without first testing the waters. That is, we tend to examine the effects of a concept
before we fully unravel and understand the concept itself. This is not surprising, as it
is much easier to take a measure and include it as one of the right hand side variables
in our equations and seek out statistical significance. The ambivalence literature
appears to be headed down this path as well. Yet, until we acknowledge and
understand the substantive differences in these types of ambivalence, we, at best, risk
underestimating the effects of ambivalence and, at worst, risk making erroneous
assessments about its influence.
Unfortunately, while I was able to pit a number of forms of ambivalence
against each other in my investigation, I was unable to collect data on each form of
ambivalence from a representative sample of the American public. Clearly, the next
critical step is a study that compares the effects of subjective and objective
ambivalent attitudes of a representative sample of the American public about actual
candidates running for office. My panel survey of undergraduate attitudes of the
2004 presidential candidates suggests a sharp difference in the impact of the
subjective and objective ambivalence on candidate attitudes – a difference that
250
appears to be attenuated by the effects of partisanship. Therefore, if the same pattern
of results is replicated with a more representative sample, then this would go a long
way in reconciling some of the ostensibly contradictory findings noted above.
Finally, my analysis makes a key methodological contribution to the study of
ambivalent attitudes. It is the first study to my awareness in political science to
manipulate explicitly individual feelings of ambivalence. Specifically, I presented
subjects with candidate descriptions that vary based upon the subject’s own policy
preferences. That is, if I want a subject to view the candidate favorably, then I have
the candidate take policy positions that are generally congruent with the subject’s
own preferences; if I want the subject to view the candidate ambivalently, then I have
the candidate take positions that agree with the subject on exactly half of the issues.
In contrast, most studies of ambivalence present subjects with a host of information
about a candidate in hopes that some of the subjects will develop ambivalent
attitudes, while others will not. This approach, however, potentially conflates the
effects of ambivalence with some other factors, such as personality traits, that may
also covary with ambivalence. Thus, any relationship one finds between ambivalence
and the dependent variable may be driven by some other spurious factor. In other
words, while people with high levels of ambivalence in these studies may behave
differently than do people with low levels of ambivalence, one cannot definitely state
that ambivalence causing this behavior.
By directly manipulating the issue positions of the candidates in my study I
can be confident that the feelings of ambivalence developed in my investigation are in
fact caused by my manipulation. This in turn enables me to make stronger causal
251
claims about the effects of ambivalence that is unavailable to previous studies of
ambivalence. As a result, this is the first study in political science that can explicitly
make causal claims about the impact of ambivalent attitudes.
In sum, the above analysis contributes to our understanding of the effects of
ambivalence on voting behavior in a myriad of ways. Most directly, it contributes to
a growing body of literature that seeks to document the various ways in which
ambivalence affects behavior. Such studies are important and work should continue
along these lines, as there is no doubt that future studies will reveal an even greater
number of ways that ambivalence influences electoral decisions. More importantly,
this research provides a framework from which future ambivalence scholars should
follow if they truly seek to integrate the concept of ambivalence into theories of
voting behavior. My work demonstrates that ambivalence studies must move beyond
a myopic focus on candidate attitudes and a generic treatment of all ambivalent
attitudes as equal. Ambivalence, as a concept, has the potential to greatly enhance
our theoretical understanding of political behavior, but this contribution will be
muted, if not inaccurate, until we begin to appreciate the complexity of ambivalence
itself. Simply stated, we must move beyond simply providing a laundry list of which
dependent variables are affected by ambivalence, but pinpoint which type and form of
ambivalence exerts the greatest impact. Of course, the development of such studies
will not be easy, but the pay-off should far outweigh the cost. So while there are
definite positive and negatives to traversing this path, there should be no ambivalence
about embarking on such a journey.
252
APPENDIX A
DISTRIBUTION OF SEARCH VARIABLES IN EXPERIMENTAL ANALYSIS
# of
Searches
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Total
Searches
160
(49%)
4
(1%)
10
(3%)
8
(2%)
14
(4%)
10
(3%)
22
(7%)
8
(2%)
16
(5%)
12
(4%)
4
(1%)
0
20
(6%)
8
(2%)
6
(2%)
6
(2%)
4
Candidate
Searches
165
(51%)
16
(5%)
26
(8%)
25
(8%)
19
(6%)
15
(5%)
25
(8%)
14
(4%)
11
(3%)
3
(1%)
4
(1%)
1
(.5%)
New
Searches
112
(65%)
30
(17%)
30
(17%)
1
(1%)
1
(1%)
Agree
Searches
190
(59%)
34
(10%)
29
(9%)
23
(7%)
15
(5%)
22
(7%)
27
(.5%)
2
(.5%)
0
1
(.5%)
1
(%)
253
Disagree
Searches
173
(53%)
72
(22%)
40
(12%)
19
(6%)
11
(3%)
6
(2%)
2
(.5%)
1
(.5%)
17
18
19
20
21
Mean
Std. Dev.
N
(1%)
6
(2%)
4
(1%)
0
0
2
(1%)
4.37
5.47
324
2.19
2.80
324
.56
.84
174
254
1.25
1.90
324
.93
1.33
324
APPENDIX B
CANDIDATE INFORMATION AND POLICY STATEMENT APPENDIX FOR
EXPERIMENTAL ANALYSIS
Biographical Information:
James Sullivan
Born: June 19, 1946
Hometown: Salem, Oregon
Education: University of Oregon: B.A., 1958; J.D. 1962
Career Highlights: City Councilman, (1976-84), State Senator (1986-94),
Lieutenant Governor (1994-2002)
Married to Lauren, 2 children - Mark and Lisa
David Powell
Born October 6, 1949
Hometown: Eugene, Oregon
Education: Oregon State University, B.A., 1961; University or Washington,
M.B.A, 1965
Career Highlights: County Commissioner (1978-82), State Assemblyman
(1986-90), State Attorney General (1992-2000)
Married to Barbara, 1 child - Anna
Candidate Statements:
1. Abortion:
Powell/Sullivan supports a woman’s right to choose
1) “I believe that women should have control over their own bodies and that the
decision of whether or not to have an abortion should remain a personal
choice. I am against the partial birth abortion ban and believe the Supreme
Court should overturn this unconstitutional law. If elected I will oppose any
restrictions placed on a woman’s right to choose.”
2) “As Senator I will work hard to ensure that women never lose their right to
choose. The decision to have an abortion is something that should remain a
personal choice. It is not something that should be dictated by government
officials. I am opposed to the current partial birth abortion law and look
forward to its overturn by the Supreme Court.”
255
Powell/Sullivan is a pro-life candidate.
1) “I believe that we should do all we can to protect every life. Only in rare
instances, such as in rape, incest and when a woman’s own life is at risk, is
abortion a viable option. Except in these extreme cases, I am opposed to
legalized abortions. I support the partial birth abortion ban and believe that
the Supreme Court will and should uphold this law.”
2) “I am a pro-life candidate because I believe in the sanctity of every human
life. I was happy to see progress made on this issue with the recent passage of
the partial birth abortion ban by Congress. As Senator, I will work hard to
uphold such laws the restrict abortion and will propose legislation that will
further reduce the number of abortions performed each year.”
2. Environment:
Powell/Sullivan supports strong federal environmental regulations
1) “I am strongly committed to protecting our environment. As Senator, I will
rigorously fight for strict enforcement of our Clean Air and Water Acts and
work to eliminate loopholes for corporate polluters. In addition, I will
propose stricter emission standards for utility companies and industrial plants
in order to increase air and water quality in our communities. And I will call
upon American automakers to embrace new standards and technologies for
fuel efficiency.”
2) “I believe that it is imperative that we protect and strengthen the quality of our
environment. I will oppose any proposals to drill for oil in the Alaskan Arctic
National Wildlife Refuge and I will protect our national forests by banning
logging on these lands. Also, as Senator I will seek environmental protections
in our trade agreements with other nations.”
Powell/Sullivan supports market approaches to the environment
1) “I support common-sense approaches to improving the environment while
protecting the quality of American life. I propose a market based clean air act
that cuts pollution by using an emissions trading program that creates
economic incentives for companies to cut emissions. As an additional
incentive, I also want to provide tax-breaks for companies that have low
emission rates. Programs, such as these, will increase air and water quality
through market incentives, rather than dictate them via governmental
mandates and regulations.”
2) “I support a cleaner and healthier environment. I will help us achieve this
goal by supporting legislation that rewards those companies that improve our
environment through efficiency and innovation. I will work to enact laws that
allow for flexibility and responsiveness to our environmental goals, rather
than seek laws that mire our businesses in unnecessary and cumbersome
256
regulatory obstacles. These policies reward companies that achieve the goal
of cleaner productivity, but do not dictate to companies how they must attain
these goals.”
3. Health Care Coverage:
Powell/Sullivan supports government funded health care
1) “I believe that all Americans have the right to health care coverage that is
affordable and of high quality. One of my primary concerns in office will be
to provide health insurance for every child in America and offer real relief for
families struggling to deal with the rising costs of doctor visits, insurance
premiums, prescription drugs and other health care costs. My first priority
will be to provide federal funds to states so that they can subsidize and
provide coverage for a greater number of Americans.”
2) “Quite simply, there are far too many Americans who are going without
health insurance. As Senator, I will make it one of my top priorities to see
that every American has access to basic medical coverage. My philosophy
will be guided by two main objectives. First, I want to guarantee that every
child is provided health insurance via our Medicaid program. Second, I want
to strengthen the Medicaid safety net so that more working families are
covered by insurance.”
Powell/Sullivan supports giving people tax credits for health care
1) “As Senator, I will work tirelessly to provide health care coverage to the
millions of Americans that currently lack health insurance. My plan will
improve the accessibility and affordability of health care while also ensuring
that Americans retain their ability to choose the plan that best fits their own
needs. My policy proposes a refundable tax credit of up to 50 percent of
coverage to individuals to help subsidize the cost of health insurance. This
plan stresses individual choice, and does not force the public into a “one size
fits all” governmental health plan.”
2) “There are simply far too many Americans who can’t currently afford heath
care. This must change. I believe that everyone should be able to choose a
health care plan that meets their own needs at a price they can afford. When
people choose their own policies then health care providers have to compete
for their business – which results in higher quality and better care. My policy
proposes a series of new individual income tax credits to make private health
insurance more affordable for low- and middle-income American families and
workers.”
4. Education – Lower
Powell/Sullivan opposes school voucher programs
1) “I am opposed to voucher programs that pull money out of our public school
systems and funnel them to private institutions. Instead, I want to improve
our public schools by investing more money into our education system. I will
propose a bill that provides funds to modernize and rebuild our school
systems. I also want to develop a program that increases the total number of
257
teachers in the nation, such as providing scholarships for college students who
promise to teach for five years after graduation.”
2) “Improving education is important, but I believe that improvement will not
come via vouchers that take money out of our public school systems.
Improvement will occur when we increase our investments in our school
systems. The most important tool in our classrooms is the people who work
in them. I want to support teachers with better training and better pay, with
more resources, and with a better teaching environment. I pledge that I will
work hard to make sure that the money is there to achieve these goals.”
Powell/Sullivan supports school vouchers programs
1) “As Senator, I will work to provide parents the tools to ensure that their own
children are receiving a proper education. Towards this end, we must set high
standards for achievement in key subject areas such as reading and math. My
plan requires that we test every child to verify that students are making
progress. When schools fail to reach these standards, my plan allows parent’s
to transfer their children to higher-performing schools. This includes granting
parent’s access to vouchers so that they may enroll their children in private
schools.”
2) ”I promise to make educating every child a top priority while I am in office.
My policy is grounded in two key principles. First, we must have stronger
accountability for results from our schools. Second, we must provide
expanded options and choices for parents of children in failing schools. By
holding schools accountable to high standards we also ensure that children
will no longer be trapped in failing schools. If a school continually fails to
meet its standards, then parents will be given the resources, such as vouchers,
to transfer their children to better schools, including private institutions, of
their own choice.”
5. Patriot Act (Terrorism):
Powell/Sullivan wants to repeal certain aspects of the Patriot Act because of concerns
about civil liberties
1) “The most basic responsibility of government is to provide for the common
defense of its citizens. The fight against terrorism can be won without losing
our civil liberties. We must stop those aspects of the Patriot Act that violates
a person’s basic constitutional rights. As Senator I will seek to repeal such
portions of the Patriot Act that are unconstitutional and I will oppose
expansions of the Patriot Act that further threaten our civil liberties and
rights.”
2) “As Senator, I will devote myself to protecting Americans from terrorism. But
as we fight the war on terror, we must be vigilant in protecting civil rights and
liberties. There is no contradiction between protecting the country from
terrorism and ensuring the protection of our basic civil liberties every step of
the way. As a result, I am troubled by some provisions in the Patriot Act,
258
which limit many people’s constitutional rights in the name of fighting terror.
If elected, I will work to decrease the threat of terror while never sacrificing
people’s civil liberties.”
Powell/Sullivan wants to strengthen the Patriot Act
1) “There is no goal that is more important to this nation than its national
security. In today’s world we face many threats and it is imperative that we
remain vigilant in protecting ourselves from terrorists. The Patriot Act has
given the Justice Department a set of resources that are both powerful and
flexible enough to act swiftly in order to prevent any future attacks. So long
as terrorist threats exist for this nation, I will fight to maintain and, if needed,
expand these tools in the fight against terrorism.”
2) “The government’s most important job is to protect and defend the American
homeland. Unfortunately, today that means we must protect the nation from
the threat of terror. I support the Patriot Act as a powerful tool in the fight
against terror. This essential law tore down the walls that blocked America'
s
intelligence and law enforcement officials from sharing intelligence. As
Senator, I will work tirelessly to uphold all aspects of the Patriot Act.
Moreover, I will work to expand the powers included in this act so that we can
increase the tools available in the fight against terror.”
6. Affirmative Action (Civil Rights):
Powell/Sullivan supports affirmative action
1) “I strongly support affirmative action programs. All Americans deserve an
equal opportunity to succeed, and these policies help the nation move
toward that ideal. If elected, I will pursue policies that encourage racial
diversity on campuses because I know that diversity serves important
goals -- it produces benefits for all students, and for society as a whole. I
also support affirmative action in employment, and in programs that aid
small disadvantaged businesses, both to remedy discrimination and to
build greater diversity and opportunity in all sectors of our society.”
2) “As a nation, we must take active steps to ensure that our schools and
workplaces reflect the full face of America. I was happy to see the
Supreme Court uphold the University of Michigan’s affirmative action
program, recognizing that affirmative action benefits both minorities and
non-minorities. As Senator, I will continue to support affirmative action
in education as well as in other areas where minorities are historically
underrepresented, such as in the work place and in gaining federal
contracts. I will vote against any bills that undermine or eliminate
affirmative action programs.”
Powell/Sullivan opposes affirmative action
1) “I strongly support diversity of all kinds, including racial diversity in
higher education and our workplaces, but we cannot mandate diversity via
affirmative action programs. Our Constitution makes it clear that people
259
of all races must be treated equally under the law. As we work to address
the wrongs of racial prejudice, we must not use means that create another
wrong, and thus perpetuate our divisions. As Senator I will promote
diversity by creating programs that provide disadvantaged individuals the
skills and opportunities to compete for entrance into colleges and
workplaces on an equal footing.”
2) “As Senator, I will work hard to support and promote diversity in our
nation. Unfortunately, there are many people in our society who, simply
because of the color of their skin, are not given equal treatment. This must
stop and as a nation we must be vigilant in responding to prejudice
wherever we find it. The solution, however, is not to enact divisive quota
systems that use race to include or exclude people from higher education,
jobs, and other opportunities. Instead, we must work to provide more
people with a greater number of opportunities for success.”
7. Tax-Cuts (Economy):
Powell/Sullivan wants to repeal the tax-cuts
1) “The recent tax-cuts are not the way to stimulate the economy for the
American public. I believe we should roll back the tax cuts for the wealthiest
segment of the population, close corporate loopholes, and eliminate wasteful
spending. I understand that America needs a highly skilled and highly trained
workforce to allow our economy to adapt and grow in the new global
marketplace. Therefore, I will use the money from the tax-cuts to expand
programs such as the School-to-Work Opportunities Act, which funds job
training programs, apprenticeships, and vocational education through
partnerships with public schools.”
2) ”As Senator, I will secure America’s economic future and ensure that workers
can achieve the American dream in our changing economy. In order to
achieve this goal we must repeal the recent tax-cuts and use that money more
effectively to promote economic growth. Rather than give this money to
wealthiest segment of society, I will use it to invest much needed support back
to into our state and local governments to help fund social programs that
benefit all working families.”
Powell/Sullivan supports the tax-cuts
1) “The recent tax-cuts were the first step towards improving our economic
growth. As Senator, I will fight for greater tax relief that will allow the
American people to keep more of their own money to spend, save and invest;
encourage individuals and businesses to make new investments that will lead
to economic growth and job creation; and deliver critical help to unemployed
citizens. By cutting taxes we put more money into the hands of the American
public which, in turn, generates more money for economic growth. As
Senator, I will support such tax-cuts that are vital for continued growth.”
2) “As Senator, I will not be satisfied until our economy is strong and
prosperous. The recent tax-cuts passed by Congress are moving us in the right
260
direction, but there is more work to be done. We need to take money out of
the hands of government and put it back into the hands of the American
public. While in office, I will work to further reduce the tax burden of
working Americans. This will bolster our economic strength by both
increasing people’s personal income and encouraging more Americans to
invest their money back into the economy.”
8. Gay Unions (Gay Rights):
Powell/Sullivan supports gay civil unions.
1) “I support laws that provide federal recognition of state-sanctioned civil
unions or civil marriages between gay couples. If elected, I will work to enact
laws that grant gay couples the same rights granted to heterosexual couples,
such as adoption rights, social security benefits, and domestic partner health
care coverage. I believe that we must ensure that the United States is a nation
of equality and democracy for all its people. This means working hard to that
every couple is guaranteed the same rights and privileges as is any other
couple. Therefore, I will continue to support gay rights while in office.
2) “We don’t ask Americans what their sexual orientation is before collecting
their taxes, seeking their service on juries or demanding that they register with
Selective Service. And we shouldn’t discriminate when we provide the rights
and privileges of citizenship between the lesbian, gay, and bisexual
communities and their heterosexual counterparts. I believe that same-sex
couples should be granted the same rights as all other Americans. This means
allowing gay couples to enter legal state sanctioned civil unions, including all
the rights and privileges that go along with such unions, such as health
insurance, family medical leave, and other basic legal protections that all
families and children need.”
Powell/Sullivan opposes gay civil unions.
1) “I believe that marriage is a sacred institution, and its protection is essential to
the continued strength of our society. I also believe that marriage is a union
between a man and a woman, and I will work to support the institution of
marriage by helping couples build successful marriages and be good parents.
I support legislation that defines marriage as a bond between a man and a
woman and that does not force states to recognize same-sex marriages and
civil unions performed in other states. As Senator, I will continually support
the institution of marriage and oppose efforts to allow same-sex marriages.”
2) “I support the traditional family, one that is founded on a marriage between a
man and woman. I do not believe the government should legally recognize
same-sex marriages because they, by definition, do not constitute a marriage.
I support the recent passage of the Defense of Marriage Act because it is an
important and necessary law for protecting and strengthening family bonds.
This law defines marriage as a union between a man and a woman and does
not force states to legally recognize same-sex unions that are formed in other
states. If elected, I will support this law.”
261
APPENDIX C
QUESTIONS USED IN EXPERIMENTAL ANALYSIS
~ Media Use and /Attention to Politics Index:
Now we'
d like to ask you some questions about what types of media you use.
1. In general, how much attention do you pay to coverage of the candidates
running for the Democratic Presidential nomination?
a. None, Very Little, Some Quite a Bit, A Great Deal
2. How much attention do you generally pay to any type of politics in the news?
a. None, Very Little, Some Quite a Bit, A Great Deal
3. How many days in the past week did you watch the news on TV?
a. 0 – 7 days
4. How many days in the past week did you read a daily newspaper?
a. 0 – 7 days
5. How much time do you spend getting political information about politics from
the Internet?
a. None, Very Little, Some, Quite a Bit, A Great Deal
~ Need for Cognition & Evaluation
At this point in the study, you will read a number of statements dealing with
political, social, and personal matters. Indicate the extent to which you agree or
disagree with each statement. There are no right or wrong answers to the statements.
Although some of the statements appear to be quite similar, it is important that you
respond to each one carefully and honestly. The first statement is:
Need for Cognition Index Questions:
1. I only think as hard as I have to.
2. I like tasks that require little thought once I have learned them.
3. Thinking is not my idea of fun.
4. I prefer my life to be filled with puzzles that I must solve.
5. I prefer complex to simple problems.
262
Need to Evaluate Questions:
6. I enjoy strongly liking and disliking new things.
7. I often prefer to remain neutral about complex issues.
8. I like to decide that new things are really good or really bad.
9. I only form strong opinions when I have to.
~ Political Efficacy:
Now we'
d like you to read some of the kinds of things people say when they are
asked about government. After reading each statement, please tell us whether you
agree or disagree with it. The first statement is:
1. People like me don'
t have any say about what the government does.
2. Sometimes politics and government seem so complicated that a person like
me can'
t really understand what'
s going on.
3. The way people vote is the main thing that decides how things are run in this
country.
4. Voting is the only way that people like me can have any say about how the
government runs things.
Trust in Government:
We would now like to ask you some general questions about government.
People have different ideas about the government in Washington. These ideas don'
t
refer to Democrats or Republicans in particular, but just to the government in general.
We want to see how you feel about these ideas.
1. How much of the time do you think you can trust the government in
Washington to do what is right--just about always, most of the time, some of
the time, or none of the time?
a. All of the Time, Most of the Time, Some of the Time, None of the
Time
2. Would you say the government is pretty much run by a few big interests
looking out for themselves or that it is run for the benefit of all the people?
a. For a Few Big Interests, For the Benefit of All
3. Do you think that people in government waste a lot of the money we pay in
taxes, waste some of it, or don'
t waste very much of it?
a. A Lot, Some, Not Very Much,
263
4. Do you think that quite a few of the people running the government are
crooked, not very many, hardly any, or do you think none of them are
crooked?
a. Quite a few, Not very many, Hardly any, None
~ Political Sophistication:
Now we have a set of questions concerning various public figures. We want
to see how much information about them gets out to the public from television,
newspapers and the like.
1. The first name is Dick Cheney. What job or political office does he now
hold?
2. What job or political office does Bill Frist now hold?
3. What job or political office does John Ashcroft now hold?
4. What job or political office does William Rehnquist now hold?
We would now like to turn to some questions about governmental rules and
procedures.
5. Who has the final responsibility to decide if a law is constitutional or not?
6. Whose responsibility is it to nominate judges to the Federal Courts?
7. How much of a majority is required for the US Senate and House to override a
Presidential veto?
8. Do you happen to know which party currently has the most members in the
House of Representatives in Washington?
9. Would you say that one of the parties is more conservative than the other at
the national level? Which party is more conservative?
264
BIBLIOGRAPHY
Allport, Gordon. 1935. “Attitudes.” Handbook of Social Psychology. Eds. Carl
Murchinson. Worchester, MA: Clark University Press.
Alvarez, R. Michael. 1997. Information and Elections. Ann Arbor: Univeristy of
Michigan Press.
Alvarez, R. Michael, and John Brehm. 1995. "American Ambivalence Towards
Abortion Policy: Development Of A Heteroskedastic Probit Model Of
Competing Values." American Journal Of Political Science 35:1055-1082.
-----. 1997. “Are Americans Ambivalent Towards Racial Policies?” American Journal of
Political Science 41:345-374.
-----. 1998. "Speaking in Two Voices: American Equivocation about the Internal
Revenue Service." American Journal of Political Science 42:418-452.
-----. 2002. Hard Choices, Easy Answers: Values, Information, and American Public
Opinion. Princeton, NJ: Princeton University Press.
Alvarez, R. Michael, and Charles H. Franklin. 1994. "Uncertainty and Political
Perceptions." The Journal of Politics 56:671-688.
Bartels, Larry M. 1988. Presidential Primaries and the Dynamics of Public Choice.
Princeton, NJ: Princeton University Press.
Basilli, John N. 1996. “Meta-Judgmental Versus Operational Indexes of Psychological
Attributes: the Case of Measures of Attitude Strength.” Journal of Personality
and Social Psychology 71:637-653.
Bem, Daryl J. 1967. “Self-Perception: An Alternative Interpretation of Cognitive
Dissonance Phenomena.” Psychological Review 74: 183-200.
Born, Richard. 1990. "Surge and Decline, Negative Voting, and the Midterm Loss
Phenomenon: A Simultaneous Choice Analysis." American Journal of
Political Science 33:615-645.
265
Brady, Henry E., Sidney Verba, and Kay Lehman Schlozman. 1995. “Beyond Ses: A
Resource Model of Political Participation.” The American Political Science
Review 89:271-294.
Brown, Judson S., and I.E. Farber. 1951. “Emotions Conceptualized as Intervening
Variables with Suggestions Toward a Theory of Frustration.” Psychological
Bulletin 48:465-480.
Cacioppo, John T., and Gary G. Bernston. 1994. “Relationship Between Attitudes and
Evaluative Space: A Critical Review, With Emphasis on the Separability of
Positive and Negative Substrates.” Pscyhological Bulletin 115:401-423.
Cacioppo, John T., Wendi L. Gardner, and Gary G. Bernston. 1997. “Beyond Bipolar
Conceptualizations and Measures: The Case of Attitudes in Evaluative
Space.” Personality and Social Psychology Review 1:3-25.
Campbell, Angus, Philip E. Converse, Warren E. Miller, and Donald E. Stokes. 1960.
The American Voters (Midway Reprint). Chicago, IL: University of Chicago
Press.
Converse, Philip E. 1964. "The Nature Of Belief Systems In Mass Publics." In
Ideology and Discontent, Ed. David E. Apter, 206-261. New York: Free Press.
Craig, Stephen C., James G. Kane, and Michael D. Martinez. 2003. “Sometimes You
Feel Like a Nut, Sometimes You Don’t: Citizens’ Ambivalence About
Abortion.” Political Psychology 23:281-301.
Cunningham, William A., Marcia K. Johnson, J. Chris Gatenby, John C. Gore, and
Mahzarin R. Banaji. 2003. “Neural Components of Social Evaluation.” Journal
of Personality and Social Psychology 85:639-649.
Delli Carpini, Michael X., and Scott Keeter. 1993. "Measuring Political Knowledge:
Putting First Things First." American Journal of Political Science 37:1179-1206.
-----. 1996. What Americans Know About Politics and Why it Matters. New Haven, CT:
Yale University Press.
Downs, Anthony. 1957. An Economic Theory of Democracy. New York: Harper and
Row.
Enelow, James M., and Melvin J. Hinich. 1984. The Spatial Theory of Voting: an
Introduction. New York, NY: Cambridge University Press.
266
Feldman, Stanley. 1995. “Answering Survey Questions: The Measurement and Meaning
of Public Opinion.” Political Judgment: Structure and Process. Eds. Milton
Lodge and Kathleen McGraw. Ann Arbor: University of Michigan Press
Feldman, Stanley, and John Zaller. 1992. "The Political Culture Of Ambivalence:
Ideological Responses To The Welfare State." American Journal Of Political
Science 361: 268-307.
Fiorina, Morris P. 1981. Retrospective Voting in American National Elections. New
Haven, CT: Yale University Press.
Finkel, Steven L., 1995. Causal Analysis with Panel Data. Thousand Oaks, CA: Sage
Publishing.
Franklin, Charles H. 1991. "Eschewing Obfuscation? Campaigns and the Perception
of U.S. Senate Incumbents." The American Political Science Review 85:11931214.
Gwiasda, Gregory W. 2001. “Network News Coverage of Campaign Advertisements:
Media’s Ability to Reinforce Campaign Messages.” American Politics
Research 29:461-482.
Heider, Fritz. 1958. The Psychology of Interpersonal Relations. New York, NY: John
Wiley and Sons.
Hochschild, Jennifer. 1981. What’s Fair? Princeton, NJ: Princeton University Press.
Holbrook, Allyson L, Jon A. Krosnick, Penny S. Visser, Wendi L. Gardner, and John T.
Cacioppo. 2001. "Attitudes toward Presidential Candidates and Political Parties:
Initial Optimism, Inertial First Impressions, and a Focus on Flaws." American
Journal of Political Science 45:930-950.
Jonas, Klaus, Michael Diehl, and Philip Brömer. 1997. “Effects of Attitudinal
Ambivalence on Information Processing and Attitude-Intention Consistency.”
Journal of Experimental Social Psychology 33:190-210.
Kahneman, Daniel, and Amos Tversky. 1979. "Prospect Theory: An Analysis of
Decision on Risk." Econometrica 47:263-291.
Kahneman, Daniel, and Amos Tversky. 1984. "Choices, Values, and Frames." American
Psychologist 39:341-350.
Kaplan, K.J. 1972. On the Ambivalence-Indifference Problem in Attitude Theory and
Measurement: A Suggested Modification of the Semantic Differential
Technique. Psychological Bulletin 77: 361-372.
267
Keith, Bruce E., David Magleby, Candice J. Nelson, Elizabeth Orr, Mark C. Westlye,
Raymond Wolfinger. 1992. The Myth of the Independent Voter. Berkeley:
University of California Press.
Kinder, Donald R and D. Roderick Kiewiet. 1981. “ Sociotropic Politics: The
American Case.”British Journal of Political Science 11: 129-161.
King, Gary, Michael Tomz, and Jason Wittenberg. 2000. “Making the Most of
Statistical Analysis: Improving Interpretation and Presentation.” American
Journal of Political Science 44: 347-361.
Knack, Stephen. 1995. "Does "Motor Voter" Work? Evidence from State-Level
Data." The Journal of Politics 57:796-811.
Lacy, Dean, and Barry C. Burden. 1999. "The Vote-Stealing and Turnout Effects of
Ross Perot in the 1992 U.S. Presidential Election." American Journal of
Political Science 43:233-255.
Lasswell, Harold D. 1936. Who Gets What, When, How. New York, NY: McGraw-Hill.
Lavine, Howard. 2001. “The Electoral Consequences of Ambivalence Toward
Presidential Candidates.” American Journal of Political Science 45: 915-929.
Lazarsfeld, Paul F., Bernard Berelson, and Hazel Gaudet. 1944. The Peoples Choice:
How the Voter Makes Up his Mind in a Presidential Campaign. New York,
NY: Columbia University Press.
Long, J. Scott, and Jeremy Freese. 2003. Regression Models for Categorical
Dependent Variables Using State (revised edition). College Station, TX: Stata
Press Publication.
Maio, Gregory R., David W. Bell and Victoria M. Esses. 1996. “Ambivalence and
Persuasion: The Processing of Messages about Immigrant Groups.” Journal of
Experimental Social Psychology 32: 513-536.
Marcus, George E., and Michael B. Mackuen. 1993. "Anxiety, Enthusiasm, and the
Vote: The Emotional Underpinnings of Learning and Involvement During
Presidential Campaigns." The American Political Science Review 87:672-685.
Markus, Gregory B. 1988. “The Impact of Personal and National Economic
Conditions on the Presidential Vote: A Pooled Cross-Sectional Analysis.”
American Journal of Political Science 32: 137-154.
268
McGraw, Kathleen M., and Brandon Bartels. 2005. “Ambivalence Toward Political
Instititusions.” Ambivalence and the Structure of Political Opinion. Eds.
Stephen C. Craig and Michael Martinez. New York: Palgrave Macmillan.
McGraw, Kathleen M., Edward Hasecke, and Kimberly Conger. 2003.
“Ambivalence, Uncertainty, and the Processes of Candidate Evaluation.”
Political Psychology 24: 421-448.
Meffert, Michael F., Michael Guge, and Milton Lodge. 2004. “Good, Bad, and
Ambivalent: The Consequences of Multidimensional Political Attitudes.”
Studies in Public Opinion: Attitudes, Nonattitudes, Measurement Error, and
Change. Eds. Willem E. Saris and Paul Sniderman. Princeton, NJ: Princeton
University Press.
Nelson, Thomas E., and Zoe M. Oxley. 1999. "Issue Framing Effects On Belief
Importance and Opinion." Journal Of Politics 61:1040-1067.
Nelson, Thomas E., Rosalee A. Clawson, Zoe M. Oxley. 1997. "Media Framing Of A
Civil Liberties Conflict and Its Effect On Tolerance." American Political
Science Review 91:567-583.
Newby-Clark, Ian R., Ian McGregor and Mark P. Zanna. 2002. “Thinking and Caring
About Cognitive Inconsistency: When and for Whom Does Attitudinal
Ambivalence Feel Uncomfortable?” Journal of Personality and Social
Psychology 82:157-166.
Page, Benjamin. 1978. Choices and Echoes in Presidential Elections: Rational Man
and Electoral Democracy. Chicago, IL: University of Chicago Press.
Priester, Joseph R. and Richard E. Petty. 1996. “The Gradual Threshold Model of
Ambivalence: Relating the Positive and Negative Bases of Attitudes to
Subjective Ambivalence.” Journal of Personality and Social Psychology.
71:431-449.
Rosenstone, Steven J., and John Mark Hansen. 1993. Mobilization, Participation, and
Democracy in America. New York, NY: Macmillan.
Schuman, Howard, and Stanley Presser. 1981. Questions and Answers in Attitude
Surveys: Experiments on Question Form, Wording, and Context. New York,
NY: Academic Press.
Scott, William A. 1966. “Brief Report: Measures of Cognitive Structure.”
Multivariate Behavior Research 1:391-395.
269
Shepsle, Kenneth A. 1972. "The Strategy of Ambiguity: Uncertainty and Electoral
Competition." The American Political Science Review 66:555-568.
Steenbergen, Marco and Christopher Ellis. 2003. “Affective Ambivalent in Electoral
Behavior” Paper Presented at Shambaugh Conference on Affect and
Cognition in Political Action. Univeristy of Iowa, March 6-9.
Timpone, Richard J. 1998. "Structure, Behavior, and Voter Turnout in the United
States." The American Political Science Review 92:145-158.
Tetlock, Philip E. 1986. "A Value Pluralism Model Of Ideological Reasoning."
Journal Of Personality and Social Psychology 504: 819-827.
-----. 2000. “Coping with Trade-Offs: Psychological Constraints and Political
Implications.” Elements of Reason: Cognition, Choice, and the Bounds of
Rationality. Eds. Arthur Lupia, Mathew D. McCubbins, Samuel L. Popkin.
New York: Cambridge University Press.
Thompson, Megan M., Mark P. Zanna and Dale W. Griffin. 1995. “Let’s Not be
Indifferent About (Attitudinal) Ambivalence.” In Attitude Strength:
Antecedents and Consquences Eds. Richard E. Petty and Jon A. Krosnick.
Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
Thurstone Leon L. 1928. “Attitudes Can Be Measured.” American Journal of
Sociology 33:529-554
Wolfinger, Raymond E., and Steven J. Rosenstone. 1980. Who Votes? New Haven, CT:
Yale University Press.
Zaller, John R. 1992. The Nature and Origins of Mass Opinion. New York, NY:
Cambridge University Press.
Zaller, John, and Stanley Feldman. 1992. “A Simple Theory of the Survey Response:
Answering Questions versus Revealing Preferences.” American Journal of
Political Science 36:579-616.
270