JOPO 062800crfc
Partisan Conversion in the 1990s: Ideological
Realignment Meets Measurement Theory
David W+ Putz
University of Houston
A well-known debate in electoral behavior has centered on the potential for individual-level partisan change. As the recent work on ideological realignment published in this journal suggests, this
debate is far from settled. Using 1992–1994–1996 NES panel data, I test the conversion assumption of ideological realignment theories using a structural equation model combined with two measurement models to control for measurement error in my indicators of ideology and party identification.
Results suggest that while partisan conversion did occur during the 1994 midterm election, ideological realignment theories must be qualified.
W
hile numerous studies have addressed the stability of partisanship and the
alignment of the contemporary American electorate, few projects to date have
combined realignment and measurement theories.1 The recent work on ideological realignment by Abramowitz and Saunders (1998) shows that stability traditionalists, revisionists, and measurement theorists continue to talk past each
other. In this note, I attempt to bridge the methodological and theoretical distance between these three schools. Using panel study data from the 1992–1994–
1996 National Elections Studies (NES), I introduce statistical corrections for
measurement error to show how the Abramowitz team overestimated the effect
of political ideology on the partisan identification of the 1994 electorate and
underestimated the stability of partisanship. Contrary to the argument of measurement theorists, however, the data indicate that partisan conversion occurs
even after controls for error are modeled.
Ideology and Electoral Realignment
The origin of the partisan attachments of the contemporary American electorate comes close to what Key (1959) identified as a “secular realignment”and
what Carmines and Stimson (1989) more recently described as an “issue evolution.” Instead of a singular “critical” election that provides the catalyst for
more traditional understanding of electoral realignments (Key 1955), issue and
The author thanks Tom Carsey, Robert S. Erikson, and three anonymous reviewers for comments
on earlier drafts of this paper.
1
For example, Abramowitz 1994; Adams 1997; Green and Palmquist 1990, 1994; Miller and
Shanks 1996; Rapoport 1997, but see Carsey and Layman 1999a.
THE JOURNAL OF POLITICS, Vol+ 64, No+ 4, November 2002, Pp+ 1199–1209
© 2002 Southern Political Science Association
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David W. Putz
ideological evolution theories argue that realignments happen gradually. As the
public becomes more aware of its ideological predisposition, it experiences an
“ideological awakening” that allows corrections to partisanship to occur (Erikson and Knight 1993).
Coupled with these theories of gradual partisan change, a growing body of
literature has suggested that the electorate is using the liberal0conservative
continuum with greater frequency in framing the terms of political debates.
The presence of “ideological sophisticates” among the public has increased
steadily since the 1980s (Knight 1985; Erikson and Knight 1993). Abramowitz
and Saunders (1998) conclude that this heightened ideological awareness led to
a partisan realignment in 1994. Similar changes have been noted among partisan leaders and activists (Carsey and Layman 1999b).
With few exceptions, the work on ideological realignment has failed to recognize the consequences of measurement error in indicators of partisanship
and ideology. I avoid this problem by first assessing the reliability of my measures of partisanship and ideology. I then test the ideological realignment process and introduce controls for measurement error to satisfy critics of partisan
instability from the measurement school.
Data and Measurement
The data for this note come from a panel study conducted by the National
Election Studies (NES) during the 1992–1994–1996 electoral cycle. The panel
provides not only the opportunity to observe the capture of the House by Republicans in 1994 and the retention of the White House by Democrats in 1996,
but the multi-wave study can be used to control for measurement error as well.
The consequences of error in single, cross-sectional indicators are well known.
Error in the dependent variable reduces the amount of variance the model can
explain and inflates the error term on the right-hand side of the model. Coefficients remain unbiased, though they are less efficient since their standard errors are inflated due to the amount of error variance in the model. Error in
independent variables is more problematical; estimates no longer remain unbiased. Introducing measurement error in a variable on the right-hand side of the
model has the effect of decreasing the true variance of the variable. Thus, the
size of the regression coefficient is attenuated for variables measured with error because the true and the error effect of the variable are included in the
estimate. If a control variable contains substantial error, the remaining true variance may be insufficient to account for the spurious relationship in the bivariate in its entirety. The result again is an attenuated estimate of the effect of the
control variable and an inflated estimate of the bivariate coefficient.2
2
The problem of measurement error in panel data is exaggerated for several reasons. As Finkel
(1995) notes, error in lagged values of the dependent variable will generate estimates of larger
negative covariance with subsequent values of the dependent variable. Failing to control for error
in the dependent variable results in coefficients that underestimate the true-score effect of lagged
values of the dependent variable. Finally, parameter estimates may be exaggerated between lagged
Partisan Conversion in the 1990s
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Measurement error can be modeled explicitly by obtaining either multiple
indicators of single concepts in cross-sectional data or by obtaining multiple
readings of a single variable at different points in time (Kessler and Greenberg
1981). I utilize the multiple readings approach for controlling random error in
my party identification measure and the multiple indicator approach for the
ideology constructs.
Party Identification
To tap partisanship, I collapse the traditional seven-point party identification
scale to create a three-point Democrat-Independent-Republican item.3 Since
the seven-point scale is the sole measure of party identification available in the
NES, panel data provide a means for correcting the biases associated with measurement error.4 In order to estimate the error variance in the partisanship item,
Kessler and Greenberg (1981) suggest a model that is composed of two
equations—a measurement equation to estimate the reliability of the item and a
structural equation to assess its stability.5
Table 1 provides the reliability estimates for two groups, the national electorate and the ideologically sophisticated.6 As indicated by the figures in Table 1,
reliability estimates are highest for ideological sophisticates, but the differ-
values of the dependent variable and the dependent variable at the expense of attenuating the estimates of other variables in the model.
3
Partisan change can be either interparty or intraparty. Interparty change captures movement
between two parties and indicates a change in partisan identification. Intraparty change captures
movement within a single party and indicates a change in the strength of partisan attachment.
Interparty change is closer to Key’s (1959) conceptualization of realignment as the shift in partisan
attachment from one party to another. Collapsing the seven-point scale captures changes in partisan identification, the focus of this project, and not partisan strength. For this reason, independent
leaners have been included with partisan identifiers, leaving only “true” independents in the middle category. Results remain invariant to the use of either the three- or seven-point measure (see
Green and Palmquist 1994, for similar results).
4
While Campbell et al. (1960) suggested partisanship is multidimensional, they, as I, are concerned with the dimension associated with long-term identification. They argue that partisanship is
a matter of self-definition; it is how voters see themselves and others on the political landscape.
Studies concluding that partisanship is multidimensional (Weisberg 1980) reconceptualize partisanship as affect for one of the two parties. This, however, is not how The American Voter or I conceptualize partisanship.
5
This model is underidentified. In order to estimate the parameters, I impose identifying assumptions recommended by Wiley and Wiley (1970). The data were treated as both ordinal and interval,
with slight increases in reliability and stability assuming ordinal measurement. The decision to
treat the data as ordinal or interval does not change substantive conclusions. For further discussion
of measurement issues, see Appendix.
6
Ideological sophisticates are knowledgeable about the meaning of the terms liberal and conservative. They are identified as correctly placing the Democratic party to the left of the Republican
party on four issues from the 1994 cross-sectional survey: liberal-conservative placement, locus of
responsibility for jobs and living standards, government aid for minority groups, and locus of
responsibility for health insurance. A total of 181 respondents were classified as ideological sophisticates. The total sample size is 386.
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David W. Putz
TABLE 1
Reliability Estimates of Partisan Identification for Two Segments
of the National Electorate Supplemented with
True and Error Score Variances a
National Sample
Sophisticates b
Reliability Estimates
Party ID
1992 (l1 )
1994 (l2 )
1996 (l3 )
.93
.92
.92
.94
.93
.93
Variances
Error
Party
True
Party
Party
Party
N
ID
.13
.11
ID 1992
ID 1994
ID 1996
.77
.81
.82
.81
.85
.83
386
181
Source: 1992–1994–1996 NES Panel
a
Party Identification is measured using the traditional NES seven-point strength of identification
measure. The seven-point scale has been collapsed to create a three-point Democrat-IndependentRepublican scale.
b
Sophisticates placed correctly the Democratic party to the left of the Republican party on four
items: ideology, locus of responsibility for jobs, locus of responsibility for health care, and aid to
minorities.
ences are not substantial.7 The estimates of true and error score variance also
conform to expectations. Sophisticates have the least amount of response error,
but again, differences do not remain all that large across the two groups. True
score estimates increase across the series, indicating that both groups became
more polarized during the mid-1990s. These estimates, however, must be interpreted cautiously because they provide little information about the causal process of partisan change.
Political Ideology
I borrow the operationalization of my ideology constructs from Abramowitz
and Saunders (1998). As there are multiple items available in the NES that can
be used to construct an error-free measure of political ideology, I create a unidimensional factor index for each of the three cross-sections in the panel using
7
The “true” score is a measure of respondent’s latent partisanship.
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TABLE 2
Factor Scores, Political Ideology Construct for 1992–1994–1996
NES Panel Survey a,b,c
Item
1992
1994
1996
.51
.56
.47
.37
.37
.46
.57
.52
.58
.40
.32
.47
.48
.63
.23
.46
.19
.53
.52
.61
.48
.49
.61
.62
.46
.56
.63
.64
.52
.64
3.07
.75
3.15
.76
3.51
.80
Spending Items
Food Stamps
Welfare
AIDS Research
Social Security
Environment
Public Schools
Child Care
.60
.62
.41
.38
.41
.41
.59
Four-point Scales
Abortion
Affirmative Action
.12
.45
Seven-point Scales
Ideology
Locus of Responsibility—Jobs
Locus of Responsibility—Health Care
Aid to Minorities
Eigenvalue
Alpha
a
Factors extracted using principal axis, iterative method.
Analysis conducted on total sample size for each of the three years in the panel.
c
Factor loadings not meeting traditional standards have been retained for purposes of replication.
b
an iterative principal axis extraction method to tap a latent dimension of political ideology.8 Factor loadings are reproduced in Table 2.
Testing the Ideological Realignment Thesis
The ideological realignment argument suggests that the American electorate
became more ideologically polarized during the 1994 midterm election and, as
a result, made a correction to its partisanship. The model I use to test this thesis
is similar to the three-wave, cross-lag panel design discussed in Kessler and
Greenberg (1981) and Finkel (1995) and reproduced in the Appendix. Instead
of combining two single-indicator measurement models with a series of structural
8
The same fourteen items were reproduced in the 1996 survey, enabling me to create factor
scores for all three waves of the panel.
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David W. Putz
equations, I combine a single-indicator, three-wave measurement model to tap
latent partisanship with item-specific factor scores in the structural equation
portion of the model.9 The estimates from these models are produced in Table 3.
OLS estimates reported in Table 3 were generated by replicating the measurement and modeling strategy of Abramowitz and Saunders (1998). I have
extended their model to incorporate data from 1996.10 The OLS estimates
indicate a sizable partisan correction took place during the 1994 midterm
election. As the ideological realignment thesis suggests, the effect of the 1992
ideology construct on the 1994 party identification measure is greater than the
opposite cross-lag. The effect of the public’s 1992 ideology on its 1994 partisanship was four times greater than the corresponding effect of partisanship
on the ideology construct. Among the ideologically sophisticated, the effect
was three times as great. Adding the 1996 data, however, lends qualified support to the Abramowitz argument. Though stability estimates for partisanship
are higher in the later 1994–1996 panel, cross-lag estimates for both ideological sophisticates and the general public indicate continued ideological corrections to partisanship.
Table 3 also presents maximum likelihood, error-corrected estimates. Contrary to the OLS estimates, party identification emerges as more stable than
the ideology construct once error is modeled. There is considerably less movement among the party identification variable under the ML strategy. As for
the causal process, purging error from the ideology and partisan identification
indicators makes it difficult to disentangle with certainty the effects of partisan or ideological conversion, especially for ideological sophisticates. For all
practical purposes, the 1992–1994 cross-lag estimates are identical for these
respondents. More damaging for the ideological realignment argument and
OLS estimates is the indication in Table 3 that ideological, not partisan, change
occurred over the 1994–1996 period. Contrary to the ideological realignment
thesis, political sophisticates and the general electorate alike, perhaps temporarily moved by the partisan rhetoric of 1994, made ideological, not partisan,
changes in 1996.
The maximum likelihood estimates for the national electorate in Table 3 are
more supportive of the ideological realignment thesis. There was a sizable and
clear partisan correction by the 1994 national electorate. The effect of 1992
ideology on 1994 party identification was two and a half times larger than the
opposite effect, even after measurement error is purged from the indicators of
partisanship and ideology. These results suggest that partisanship was subject
to ideological forces at work in the political environment. However, while
partisanship remained stable for the respondents in the later panel wave, ideo-
9
Estimation proceeded in two stages. First, the measurement model for the ideology construct
was estimated. Once results were obtained, factor scores were included in the second estimation.
10
These estimates compare favorably to those generated by the Abramowitz team.
Partisan Conversion in the 1990s
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TABLE 3
Cross Lag Effects: Partisan Identification and Political
Ideology, 1992–1994–1996+a
Sophisticates b
Parameter
Stability Effects
1992–1994
Partisanship c
Ideology
d
1994–1996
Partisanship
Ideology
Cross-Lag Effects
1992–1994
PID to Ideology
Ideology to PID
1994–1996
PID to Ideology
Ideology to PID
N
x2
df
CFI
National Sample
OLS
MLE
OLS
MLE
.64
(.06)
.73
(.06)
.85
(.05)
.75
(.04)
.65
(.04)
.78
(.04)
.84
(.04)
.79
(.03)
.79
(.05)
.85
(.04)
.96
(.06)
.82
(.04)
.78
(.04)
.78
(.03)
.93
(.05)
.79
(.02)
.16
(.40)
.25
(.04)
.28
(.05)
.27
(.04)
.06
(.23)
.23
(.01)
.11
(.03)
.27
(.03)
.09
(.33)
.13
(.01)
.15
(.04)
.04
(.05)
.11
(.22)
.07
(.01)
.16
(.03)
.02
(.04)
181
181
117.2
6
.90
386
386
163.3
6
.90
Source: 1992–1994–1996 NES Panel
a
Standardized coefficients. Standard errors in parentheses.
b
Sophisticates placed correctly the Democratic party to the left of the Republican party on four
items: ideology, locus of responsibility for jobs, locus of responsibility for health care, and aid to
minorities.
c
Party Identification is measured using the traditional NES seven-point strength of identification
measure. The seven-point scale has been collapsed to create a three-point Democrat-IndependentRepublican scale.
d
The measure of ideology is a factor of four seven-point items—respondent’s ideology, guaranteed jobs, government provided health insurance, and aid to minorities; two four-point scales—
affirmative action and abortion; and seven spending items—environment, health care, public schools,
child care, AIDS research, social security, and welfare.
logical change continued to occur until 1996. Not only is this contrary to the
OLS models, it is contrary to the ideological realignment thesis. For the general electorate, partisanship appears to be subject to short-term electoral forces
but returns to a state of stability fairly quickly.
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David W. Putz
The Consequences of Measurement Error for Theories
of Ideological Realignment
One of the more persuasive arguments offered over the past decade to explain the source of the partisan attachments of the contemporary American electorate is that the electorate made an ideological correction to its partisan
preferences. Ideological conservatives are more likely today than 20 years ago
to identify with the Republican party, and the Democratic party increasingly
attracts those who adopt what has been identified as liberal positions on a latent measure of political ideology. The partisan rhetoric of the early 1980s has
been credited with starting this realignment, the 1994 midterm election with its
completion.
The validity of the ideological realignment thesis rests on the assumption of
partisan instability. Measurement theorists have challenged this assumption, arguing that error in the party identification scale attenuates the effect of partisanship at the expense of inflating other variables in the model. Contrary to the
stability assumption of the measurement school, partisan conversion occurred
in 1994, though the general picture remains one of partisan stability. The data
analyzed here suggest that this process operates for both ideological sophisticates and the national electorate. Though the findings here are contrary to the
stability traditionalists, they reinforce the lessons of the measurement school:
incorrect assumptions about the measurement of variables leads to erroneous
interpretations of the data.
At this point, the ideological realignment theory must be qualified. Using a
latent, unidimensional construct of ideology, the 1994 midterm election temporarily heightened ideological differences in the electorate. One reason the process behind the current alignment of the American electorate appears to be
much more complicated than earlier work has suggested may be that the 1992–
1994–1996 panel does not provide a complete picture of the electoral realignment process. After all, a secular realignment may take many electoral cycles
to occur; I have examined only one. We may be looking at the end or the middle of a secular realignment process. If political candidates continue to frame
their debates in language similar to the rhetoric of the early 1980s and mid1990s, we may see voters relying less on the psychological attachment to their
partisanship and beginning to conform their vote and partisan choices to their
ideological preferences.
Appendix
Figure A depicts a three-wave, cross-lag measurement model. The measurement portion of the model controls for error in single-item, cross-sectional indicators, such as the NES party identification measure. The measurement model
for partisanship divides the indicators of partisanship, y, into two parts: latent
or “true” measures (h1 , h2 , and h3 ) and error (Ei’s). The li’s indicate the causal
Partisan Conversion in the 1990s
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FIGURE A
Three-Wave Cross-Lag Effects, Single Indicator Measurement Model
with Factor Scores
coefficients between the latent and observed measures of partisanship. Mathematically, the partisanship measurement model is represented as yi 5 lihi 1
Ei . Because there are multiple cross-sectional indicators in the NES to construct a latent measure of political ideology, factor scores are created. Latent
measures of ideology are indicated by h4 , h5 , and h6 . The measurement model
for this construct is not reproduced. Structural equations in the model estimate
item stability. Stability coefficients between the three panel waves are indicated
by b21 and b32 for partisanship and b54 and b65 for ideology. The stability
equations hi 5 bijhj 1 zi describe the causal linkages between the latent variables. Finally, cross-lag effects between the endogenous, latent, measures of
partisanship and ideology can be estimated. Cross-lag effects of partisanship
on ideology are modeled by the following: h5 5 b54h4 1 b51h1 1 z5 .
Figure A also shows that the measurement model for partisanship is underidentified. There are six known pieces of information, the variance and covari-
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David W. Putz
ance of the three indicators (observed scores yi ), and eleven parameter estimates:
the three error variances (E), the three reliability coefficients (l), two stability
coefficients ( b ), and three disturbance terms (z). Wiley and Wiley (1970) assumptions are used to identify the model. These restrictions assume constant
error variance across all three waves of the model. Two additional assumptions
are necessary for identification. First, the initial value of the indicator for each
latent value is set to one to solve the problem of scale indeterminacy and to
provide a “start value” for the estimate. Second, the variance in the true score
of the first wave latent value, h1 , is set to equal the variance in its disturbance
term, z1 . Estimation proceeds with a covariance matrix since the variance of h2
and h3 are not equal to 1. Heise (1969) offers an alternative to Wiley and Wiley
assumptions, suggesting a standardized solution to identify the model. Reliability indicators (lt ) are constrained to be equal, and, by standardizing the model,
the variance of ht is 1. This leaves two parameters to identify, the stability
estimates b21 and b32 . The Heise model increases the stability and reliability of
the partisanship indicators, but it does not substantively affect the overall patterns of change.
Manuscript submitted 28 June 2000
Final manuscript received 11 March 2002
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David W. Putz is a Ph.D. candidate in political science, University of Houston, Houston, TX 77204-3011.