CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
USING PERCEPTIONS OF TRUSTWORTHINESS AND INTELLIGENCE TO
PREDICT CANDIDATE SUPPORT
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Arts in Psychology
General Experimental
By
Lee H. Tillman
December 2012
The thesis of Lee H. Tillman is approved:
____________________________
Dr. Gabriela Chavira, Ph.D.
____________
Date
_____________________________
Dr. Debbie Ma, Ph.D.
____________
Date
____________________________
Dr. Que-Lam Huynh, Ph.D., Chair
____________
Date
California State University, Northridge
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ACKNOWLEDGMENTS
I would like to acknowledge the advice and guidance of Dr. Que-Lam Huynh, my
committee chair. I would also like to thank the other members of my graduate committee,
Dr. Debbie Ma and Dr. Gabriela Chavira for their guidance and suggestions.
Lastly, I offer my regards and blessings to all of those who supported me in any
respect during the completion of this project.
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Table of Contents
Signature Page
ii
Acknowledgments
iii
Abstract
v
1. Introduction
1
2. Theories of Contemporary Prejudice
3
Aversive Racism
3
Symbolic Racism
7
Racial Microaggressions
8
3. The Current Study
13
4. Method
14
Participants
14
Measures
14
5. Results
18
Preliminary Analyses
18
Test of Hypothesis 1
19
Test of Hypothesis 2
20
6. Discussion
22
7. References
29
8. Appendix A
35
9. Appendix B
50
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ABSTRACT
USING RATINGS OF TRUSTWORTHINESS AND INTELLIGENCE TO PREDICT
CANDIDATE SUPPORT
By
Lee H. Tillman
Master of Arts in Psychology
General Experimental
The current research sought to examine the relationship between ethnicity and
ratings of trustworthiness and intelligence. It also sought to examine the relationship
between implicit and explicit perceptions of African American political candidates and
willingness to support them. Previous studies have found implicit attitudes about race and
ethnicity play a part in political elections even after taking into consideration other
factors such as party affiliation, sex, and age. The 506 participants in this study were
asked to complete two Implicit Association Tests (IATs) and one survey of candidate
support. It was hypothesized that on average, participants would be significantly faster at
pairing trustworthy and intelligent synonyms with White American faces compared to
African American faces on the IAT. This hypothesis was fully supported. In addition, it
was hypothesized that both implicit and explicit perceptions of trustworthiness and
intelligence would significantly predict candidate support. This hypothesis was only
practically supported. Although explicit ratings of trustworthiness and intelligence
significantly predicted whether participants supported an African American candidate, no
evidence was found that the implicit IAT scores predicted candidate support. It is
believed that this finding was due to the lack of controls in the explicit ratings portion of
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the study. In order to gain a better understanding of the role of implicit stereotypes in
candidate support, future studies will have to control for these extraneous variables and
manipulate the candidates’ ethnicity. Limitations of the current study and implications for
future research are discussed.
vi
Using Ratings of Trustworthiness and Intelligence to Predict Candidate Support
Although most American voters would not list race as a defining factor in his/her
decision to vote for a candidate, it is improbable that race is completely left out of the
equation. This is because race in general, and racism specifically has shaped American
social life, politics, and economics for centuries (Dovidio & Gaertner, 2001). Even
though racial prejudice is no longer as overt as it was in the past, it is still expressed
today but in more subtle ways (Sue et al., 2007).
For instance, in 2007, Joe Biden (as a competing candidate for the Democratic
Party’s presidential nomination) described Barack Obama as “the first mainstream
African-American who is articulate and bright and clean” (Thai & Barret, 2007). Aside
from it being historically inaccurate and offensive to many, the underlying message of the
statement is that African Americans are generally unintelligent, as Biden found it novel
that Obama was both articulate and African American. The theme of President Obama as
an untrustworthy politician has also pervaded his presidential career. Although
trustworthiness is an issue that all politicians must face, it is particularly unusual for an
American president to be continually questioned on the nature of his citizenship and/or
religious affiliation, as Obama has.
These specific attitudes towards and stereotypes applied to President Obama stem
from the general negative attitudes and stereotypes directed towards African Americans
(Greenwald, Smith, Siriam, Bar-Anan, & Nosek, 2009; Knowles, Lowery, &
Schaumberg, 2010; Mas, & Moretti, 2009; Parker, Sawyer, & Towler, 2009; Payne, et al.,
2010). By examining two specific, widespread stereotypes of African Americans,
1
unintelligent and untrustworthy, I aim to get a clearer picture of how these stereotypes
may play a role in electoral politics. The assumption that African Americans are criminal
has been demonstrated in studies in which people exhibited a propensity to “shoot”
African American targets in a video game simulation significantly more often than if the
target were White American (Correll, Park, Judd, & Wittenbrink, 2002; Glaser &
Knowles, 2008), or misidentified benign objects as weapons when primed with African
American faces (Payne, 2001). They also linked African American men with weapons
more so than their White American counterparts (Nosek et al., 2007). Although these
studies do not directly measure utrustworthiness, they do examine the assumption that
African Americans are criminal, a stereotype contributing to the general perception that
African Americans are untrustworthy. The assumption that African Americans are
unintelligent has been noted in studies where African Americans were described by the
majority of participants as such (Devine & Barker, 1991), and in studies where African
Americans where rated as less intelligent than White Americans (Bobo & Krugel, 1997).
Because these characteristics are commonly attributed to African Americans, they are
likely to be applied to African American politicians as well. Indeed, research has shown
that negative attitudes about race and ethnicity play a part in political elections
(Greenwald et al., 2009), and there are many ways these attitudes may be expressed, such
as attributing intelligence to some ethnic groups but not others, and ascribing criminal
traits to some ethnic groups but not others, etc. (Sue et al., 2007). Based on research on
contemporary forms of prejudice, I argue that both implicit associations (concerning
these stereotypes) and explicit ratings of trustworthiness and intelligence will be
predictive of candidate support.
2
Theories of Contemporary Prejudice
Aversive Racism
Because of the legislative advances of the Civil Rights Movement of the 1960s,
overt racism has declined dramatically in the U.S. Separate drinking fountains, public
lynchings, and segregated schools are unfathomable to most Americans today, yet racism
in the U.S. has not been eradicated completely. In the place of the overt racism of the past
is a subtler version of racism (Gaertner and Dovidio, 1977).
One term used to label this contemporary form of racial prejudice is “aversive
racism” (Kovel, 1970). Aversive racism reflects the racial attitudes of mostly liberal
White Americans who endorse egalitarian ideals yet still hold unconscious, negative
feelings toward minorities. Researchers have found that contemporary prejudice tends to
be subtle, unintentional, and unconscious. This explains why the divergence of racial
attitudes often goes unnoticed. The following studies explore the dueling nature of the
racial attitudes of White Americans.
Conditions that foster aversive racism. In a study on emergency interventions,
Gaertner and Dovidio (1977) demonstrated that when a person is provided the
opportunity to attribute his/her behavior to something other than race, he/she is more
likely to display aversive racism. For instance, when participants in the study were lead to
believe that they were the only witness to an emergency, participants helped the victim
regardless of his/her race. In this condition, participants bore the full responsibility for
helping and could not diffuse responsibility to other bystanders. However, when
participants were not alone, they were significantly less likely to help when the victim
was African American than when the victim was White American. In this condition,
3
participants are allowed to blame their inaction on something other than racial bias,
thereby keeping their self-image of being non-prejudiced intact while declining to help
African American victims. Although political candidate support may not directly lead a
person to situations in which he/she would have to engage directly with minorities, this
study is informative and applicable to my research question because it demonstrates that
racial bias is often unintentional and unconscious. It is not the aversive racist’s active
intent to harm that causes damage, but rather his/her passive failure to help.
Another study showed that discrimination occurs when the appropriate behavior is
unclear (Dovidio & Gaertner, 2000). Participants in the study were asked to evaluate
candidates for a counseling position at a school. The applications were in one of three
different categories: strong, moderate, or weak qualifications. The researchers found that
bias was the highest against African American applicants in the moderate condition.
Moderate qualifications were treated as strong when the candidate was White American
but as weak qualifications when the candidate was African American. In other words, the
White applicant is more likely to get the “benefit of the doubt.” Because the moderate
condition was ambiguous, participants were allowed to explain their recommendations on
a factor other than the race of the applicant. The participants were allowed to maintain
their positive self-image while still discriminating against the African American, but not
the White American applicant, when the applicants’ qualifications were ambiguous.
Aversive racism and politics. A major theme in aversive racism theory is the
concept of ambiguity. For instance, a condition that fosters aversive racism is one in
which prejudice can be blamed on something other than race. Recently, Rush Limbaugh’s
description of First Lady Michelle Obama as “uppity” (“Rush Limbaugh: Michelle
4
Obama,” 2011) is a case in which his choice of words could be perceived as a response to
her supposed conceit just as well as a response to her ethnicity. Although the literal
translation of the word has no racial connotation, “uppity” is a relatively outdated word
that typically refers to an African American who “doesn’t know his/her place” (Major,
1994). The strength of this comment lies in its ambiguity. Another condition that fosters
aversive racism is one in which the correct mode of action is unclear. This condition is
more concealed than the previously mentioned condition. Knowles, Lowery, and
Schaumberg (2010) conducted a study in which White American, Asian American, and
Latino/a participants were randomly assigned to read a description of healthcare reform
framed either as being President Obama’s plan or President Bill Clinton’s 1993 plan. In
the Obama condition, there was an association between implicit racial prejudice and
opposition to the healthcare plan; there was no such association in the Clinton condition.
Participants high in implicit racial prejudice were less likely to support the Obama
healthcare plan than the Clinton healthcare plan. Simply put, high-prejudice participants
perceived Clinton’s plan as superior to Obama’s plan even though the two plans were
identical in nature.
Although aversive racism theory places emphasis on ambiguity, it does not focus
a sufficient amount of attention on intergroup perceptions. Gaertner and Dovidio
proposed a strategy they believed would be useful in mitigating aversive racism called
the Common Ingroup Identity Model, which states that aversive racists may not
necessarily dislike outgroup members (i.e., minorities) but instead may be expressing
favorability towards their ingroup. While this may be true, attention should be placed on
the important distinction between being an ingroup member and being perceived as an
5
ingroup member. Devos and Banaji (Study 5, 2005) concluded that at the implicit level,
when compared to African Americans and Asian Americans, White Americans always
perceived themselves to be more representative of what it means to be American. This
implicit perception was utilized throughout the 2008 presidential campaign. Many efforts
were made by Obama’s opponents to depict him as un-American, and by doing so, they
also painted him as untrustworthy and criminal. The most direct expression of this is the
birther movement (a movement that still exists more than three years into his term),
which pronounces that he is literally not American. A recent poll revealed that only 33%
of Republican voters in Tennessee, 40% in Georgia, and 42% in Ohio believe the current
President of the U.S. was born in America (Public Policy Polling, 2012). If the current
president is not a natural born citizen of the U.S., then that makes him a criminal who has
broken one of two requirements to qualify for his position. Obama’s less extreme rivals
have also alluded to the idea that he is untrustworthy. For instance, the price of
ammunition rose in 2008 because of the fear that Obama would introduce more severe
gun laws. Historically, when a Democratic President is elected and/or there is a
Democratic majority in the Senate, the price of ammunition rises. However, the demand
and price for ammunition in 2008, during the first Obama presidential campaign, was
significantly higher than in previous years when Democrats controlled the White House
or the Senate (Hennigan, 2009). Lawrence Keane, senior vice president and general
counsel of the National Shooting Sports Foundation Inc., whose members include some
of the largest ammunition suppliers in America, stated that "nobody has ever seen this
kind of demand before," and because of this higher demand, ammunition prices have
risen (Hennigan, 2009). During the first three years of his term, however, the president
6
did not propose any anti-gun legislation and has rarely mentioned the topic. Regardless,
presidential candidate Mitt Romney conveyed during the recent NRA’s Leadership
Forum that if reelected, the president would use his second term to enforce stricter gun
laws (Landsberg, 2012). For another president, these allegations may be easily refuted.
However, convincing voters that an African American politician is untrustworthy
becomes easier when the perception is that African Americans as a whole are
untrustworthy.
Symbolic Racism
Symbolic racism is another example of contemporary racism. Kinder and Sears
(1981) described it as a combination of anti-Black affect and conservative political
values. In essence, symbolic racism is a resistance to change based on moral grounds that
African Americans violate the core American values of self-reliance, obedience,
discipline, independence, and so on. Symbolic racism is in essence a political belief
system that embodies four specific themes: (1) prejudice and discrimination against
African Americans is a thing of the past, (2) Africans Americans’ failure to progress is
due to their poor work ethic, (3) African Americans are demanding too much at once, and
(4) African Americans have already received more than they deserve (Henry & Sears,
2002). These themes allow political conservatives to practice racism while appearing to
practice equality. For example, if Whites Americans (who are the majority) define
American values in terms of characteristics they see themselves possessing (e.g., selfreliant, independent), as measured by their standards, then they can apply those standards
in a way they consider nonracist and yet maintain racial exclusion (Wood, 1994). It
should be mentioned that anti-Black affect is not necessarily reflected in feelings of
7
dislike and hostility. It can be experienced subjectively as fear, avoidance, or unease
(Sears, 1988).
Although symbolic racism is a viable explanation of contemporary racism, there
are a few issues that need to be addressed. First, symbolic racism theory argues that the
greatest impediment to racial equality is people’s opposition to race-targeted government
programs. This is in spite of the fact that the U.S. government has put into effect several
race-targeted programs, and the result has not been complete racial equality (Dovidio, &
Gaertner, 2004). Secondly, the scores on the symbolic racism scale, the only scale
measuring this construct, are not equally reliable across all ethnic groups. In a survey
study, the Symbolic Racism 2000 scale, score reliability was higher for White Americans
(! = .77) and Asian Americans (! = .74) than for African Americans (! = .55) and
Latinos (! = .42) (Henry & Sears, 2002). Lastly, the scale items assess attitudes towards
African Americans and no other group [e.g., “Blacks should try harder,” “Blacks are
demanding,” “Blacks have gotten less than they deserve” (reverse scored)]. This makes
the Symbolic Racism 2000 Scale difficult to generalize to the diverse American
population. The combination of these issues makes symbolic racism theory and its
findings difficult to generalize. Though symbolic racism may exist, the scope of its
empirical data does not extend past the relationships between White Americans and
African Americans in the realm of government policy.
Racial Microaggressions
Another perspective on contemporary racism is racial microaggressions theory,
which focuses on how racial prejudice is experienced by minority group members
(Pierce, 1978). Racial microaggressions are “brief, everyday exchanges that send
8
denigrating messages to people of color because they belong to a racial minority group”
(Sue et al., 2007, p. 72). Racial microaggressions fall into one of three categories:
microassault, microinsult, or microinvalidation. Microassaults are explicit racial
derogations meant to injure, akin to old-fashioned racism in that they are often conscious
attempts to degrade a person of color. A microinsult, on the other hand, is typically
unconscious. For instance, if an employee of color is asked, “How did you get your job?”
the implication is that the position was given to him/her because a quota needed to filled
and not because of his/her abilities. Thus, hearing statements such as this on a regular
basis could be interpreted by the perceiver as racial aggression. Microinvalidations, like
microinsults, are also often unconscious and are defined as actions or words that nullify
the thoughts, feelings, or reality of a person of color (Sue et al., 2007). For example, a
person of color may relay a story to a White American friend of a time when he/she was
pulled over by a police officer for no reason, only to be told by the friend, “You are being
too sensitive” or “That could have happened to anyone.” These types of responses from
the majority group member nullify the racial experiences of the person of color.
The impact of racial microaggressions. One way in which racial
microaggressions can affect people of color is that they can be damaging to the
perceiver’s psychological welfare (Sue et al., 2007). A series of studies conducted by
Cheryan and Monin (2005) shows Asian American participants going out of their way to
demonstrate knowledge of American popular culture to dispel impressions they are not
American (Study 4). Cheryan and Monin (2005, Study 5) also showed that Asian
Americans, after being confronted with a statement alleging that they were not American
and therefore could not participate in the study, reported higher participation in American
9
practices such as playing American sports, listening to American music, and having
American friends. The threatened group also felt more offended, felt angrier, and liked
the experimenter less than those in the control group (Cheryan & Monin, 2005). A more
recent study reported that Asian Americans were three times more likely to report a
typical American food as their favorite after being asked whether they spoke English than
when they had not been asked, even when these food choices were poorer choices
nutritionally (Guendelman, Cheryan, & Monin, 2011). The questioning produced no
effect on White Americans. Huynh, Devos, and Smalarz (2011, Study 3) found that even
after controlling for perceived discrimination in other domains, ethnic minorities who
were more aware of the perpetual foreigner stereotype (the perception that ethnic
minorities are foreigners in a White, Anglo-Saxon dominated society) reported greater
depression (Latinos) and lower life satisfaction (Asian Americans). This is all consistent
with the microaggressions framework, which draws attention to the damaging
psychological effect of microinvalidations and microinsults. Having to change one’s
behavior on a daily basis during interracial interactions may be harmful to ethnic
minorities.
Microaggressions may also be harmful in that they may lead to people of color
being judged in a more negative manner than others. For instance, Yogeeswaran and
Dasgupta (2010, Study 2) tested whether implicit prototypes about nationality predicted
discriminatory practices against Asian Americans. Specifically, the researchers examined
the assumption of Asian Americans as traitors to their country. The study revealed that
the more participants held the implicit national prototype that American = White, the less
qualified they perceived Asian American candidates to be for a forensic investigator job,
10
but only when the job made national loyalty salient and not when the job made national
loyalty irrelevant. In this study, the positive attribute (loyalty) is attributed to White
Americans, whereas the negative attribute (disloyalty) is attributed to Asian Americans.
When national loyalty was salient, Asian Americans were assumed more likely to
commit a crime of national security than a White American was. Simply put, Asian
Americans were victims of an assumption of criminal status, a racial microinsult.
Microaggressions and politics. One of the most detrimental aspects of racial
microaggressions is the belief that they are harmless. The fallacy lies in the belief that if
an offence is unintentional, it is also justifiable and more importantly, innocuous.
Recently, Congresswoman Michele Bachmann criticized President Obama for not
addressing the issues of economy and energy prices and accused his re-election campaign
of “waving a tar baby in the air” (Cohn, 2012). In 2011, Congressman Doug Lamborn
stated that working with the President on the debt ceiling was like “touching a tar baby”
(Doug Lamborn Apologizes, 2011). Both Representatives asserted that their use of the
phrase “tar baby” was not racially motivated. Regardless of how the phrase was intended,
its cavalier use minimizes the experiences of people of color and reinforces the notion
that these experiences are unimportant to their political leaders.
In addition to the psychological impact of racial microaggressions, there are also
potential effects on policy and intergroup perceptions in the political realm.
Microinvalidations (specifically “alien in own land”) have been used repeatedly in an
attempt to present Barack Obama as a foreigner. With the use of this tactic, Obama is put
in the position of defending his nationality, a defense that no other president has had to
make in the past. Furthermore, the aforementioned finding that President Clinton’s
11
healthcare bill was more likely to be accepted than President Obama’s healthcare bill
(Knowles et al., 2010) is comparable to "Obamacare" being rejected by Republicans who
accepted "Romneycare" although it has been revealed that the two plans have substantial
similarities (Johnson, 2011). This preference may be accredited to the idea that Romney
is perceived to be more trustworthy and/or intelligent than Obama. In any event, both
conscious and unconscious microaggressions are shown to influence the perceptions of
political leaders of color and their policies.
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The Current Study
Some of the most important factors in deciding whom to vote are whether one
perceives the candidate to be trustworthy and/or intelligent. Does he really mean what he
says? Will she really follow through on the promises she made during her campaign? Is
he smart enough to handle the difficult problems that come with this position? Although
there are many details that could bias one’s perceptions of any given candidate (e.g.,
political party, age, sex, religion), race and ethnicity have been a focal point in America’s
recent political elections. There are ways in which the discussion of race and ethnicity
has brought about positive discourse in America, such as President Obama being a
positive role model for children of color. However, this discussion often brings about
negative implications as well, such as the idea of a “post-racial” America, a
microinvalidation that minimizes the effects of racism on people of color.
As mentioned above, trustworthiness and intelligence may be important factors
when deciding on a candidate. However, studies have shown that African Americans are
viewed to be on the low end of the spectrum of each of these factors. Therefore, using
two Implicit Association Tests, I expected the participants to have a more difficult time
pairing intelligent synonyms (e.g., clever, bright, brilliant) and trustworthy synonyms
(e.g., honest, truthful, virtuous) with African American faces. Because African
Americans are perceived to be less trustworthy and intelligent, the stereotype of what
African Americans are and the idea of what an elected official should be in direct
opposition of each other. Thus, I also hypothesized that these implicit perceptions of
African Americans in general and the explicit perceptions of specific African American
candidates will predict the participants’ support for African American candidates.
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Method
Participants
Participants were 506 workers (288 men) from Amazon’s Mechanical Turk
Website. All participants self identified as White U.S. citizens who were of voting age.
About a quarter of them (24.3%) were between 26-30 years of age. Nearly 1/3 of
participants reported a household income of $30,000-$49,999 per year, that they belong
to the middle class, have completed some college, and that they reside in America’s
Southern region. Over half of our participants stated that they had no religion (26%),
were Atheist (16%), or Agnostic (14%). The majority of participants considered
themselves to lean more towards liberal ideals concerning economic (55%) and social
(76%) issues. Forty-eight percent of participants identified as Democrats, 22% had no
party affiliation, and 16% identified as Republican. The majority (n = 448) was registered
to vote, but 62% of participants did not know the name of their U.S. Representative (see
Table 1 for complete demographics).
Measures
The participants completed two Implicit Association Tests (IATs), a self-report
measure of their perceptions and willingness to support various candidates, as well as a
self-report demographic questionnaire. The order of the IATs and self-report measures
were randomized.
Implicit Association Test. Participants completed two IATs (counterbalanced) to
measure the direction and strength of associations between: (1) African American versus
White American and trustworthy versus dishonest and (2) African American versus White
American and intelligent versus unintelligent. The IATs were administered using Inquisit
14
3.0 software hosted by Millisecond.com. First, to acquaint participants with the
synonyms used to represent the concepts trustworthy and dishonest, each stimulus
appeared in the middle of the screen for 1,500 ms under the appropriate label.
Participants were instructed to watch the stimuli carefully. For each task, stimuli were
presented at the center of the computer screen. Participants were asked to categorize each
stimulus as quickly as possible by pressing a key that was toward either the left or the
right of the keyboard. Response times were recorded. Categorization labels were
positioned at the top left and right of the screen to indicate the particular pairing that was
requested. The labels used for the ethnic groups are White American and African
American. The attribute dimension was labeled trustworthy and dishonest. If a stimulus
was incorrectly classified a red “X” appeared below the stimulus; participants needed to
provide the correct answer to move on to the next trial. Each block included 12 practice
trials and 30 test trials. Stimuli were selected alternatively from each pair of concepts.
The same number of stimuli was presented for each concept. Each IAT included two
blocks of trials. For example, to measure the relative association between White
American versus African American and trustworthy versus dishonest, participants
completed two blocks of trials. In the compatible block, they paired as quickly as possible
trustworthy synonyms (e.g., honest, truthful, moral) with White American faces while
pairing dishonest synonyms (e.g. corrupt, devious, immoral) with African American
faces. In the incompatible block, the opposite pairing was completed. The intelligence
IAT was the same as the trustworthiness IAT, the only difference being the synonyms for
intelligent (e.g., clever, brilliant, astute) and unintelligent (e.g., ignorant, dense, foolish)
(see Table 2).
15
Candidate Perceptions and Support Survey. Using a Likert-type scale ranging
from 1 (strongly disagree) to 5 (strongly agree), participants were asked to indicate their
agreement to 13 statements regarding each of ten U.S. Representatives (all men) running
for re-election in 2012 (4 White Americans, 3 African Americans, 2 Latinos, and 1 Asian
American). Information about all candidates is presented in Table 3, and their pictures are
presented in Figure 1. The only information provided to participants for each candidate
was his name and picture. In testing my specific hypotheses, only items referring to the
African American candidates (Sanford Bishop, Hank Johnson, and David Scott) were
used (see Figure 1). Candidates were presented in randomized order, and for each
candidate, all items were presented in randomized order (see Figure 2 for a sample
screenshot of how items, names, and photos were presented).
In order to assess the participants’ perception of each candidate, 8 of the 13 items
measured the perceived intelligence (3 items), trustworthiness (3 items), likability (1
item), and patriotism (1 item) of each candidate. In examining my hypotheses, I was only
interested in the trustworthiness and intelligence ratings of the candidates; therefore, the
likability and patriotism ratings were not used in any analyses. The items in the
Intelligence and Trustworthiness subscales were modified items from the Inventory of
Microaggressions Against Black Individuals (Mercer, Zeigler-Hill, Wallace, & Hayes,
2011) and the Racial and Ethnic Microaggressions Scale (Nadal, 2011).
There were 5 additional items assessing participants’ willingness to support each
candidate in various ways, such as openly stating that one is a supporter of the candidate
or voting for that candidate. In all, the Candidate Perceptions and Support Survey had
five sub-sections with a total of 13 items, and participants rated each candidate on these
16
13 items. A full list of items is presented in Table 4.
Demographic Information. A demographic questionnaire assessed participants’
sex, age, ethnicity, race, income, socioeconomic status, voter registration (registered or
not), economic and social views, political party, religion, education, citizenship,
residence, and Congressional Representative from his/her home district.
Design and Procedure
After a participant elected to complete the task on the Mechanical Turk website,
he/she was redirected to Millisecond.com to complete the study. Participants were
randomly assigned to complete either the IAT or the Candidate Perceptions and Support
measure first. For the Candidate Perception and Support measure, there were 10 different
U.S. Representatives to rate with each candidate presented one by one. As displayed in
Figure 1, each candidate’s picture was only accompanied by his name. For each
candidate, the items on the Candidate Perceptions and Support measure were presented in
randomized order at the bottom of the screen separately and in succession. Once the
participant made his/her selection from the 5-point Likert-type scale, he/she was
presented with the next item. Once all the items were completed for the first candidate,
the participant did the same for the remaining candidates. When the participant
successfully completed the Candidate Perceptions and Support section, he/she was
presented with the IAT portion of the study (or vice versa) followed by the demographic
questionnaire and debriefing (see Appendix). In order to ensure that each participant
maintained focused on the task, a completion code was given in two parts during the
study. When the participant was directed back to the Mechanical Turk website, he/she
was required to enter this two-part code in order to submit his/her work to be paid $3.
17
Results
Preliminary Analyses
All 13 items of the Candidate Perceptions and Support questionnaire were factor
analyzed individually for each of the three African American candidates (Sanford Bishop,
Hank Johnson, and David Scott) using maximum likelihood extraction with direct
oblimin (non-orthogonal) rotation. The three separate analyses yielded two factors
explaining 58.68% – 63.14% of the variance for the entire set of variables. Factor 1
contained the Trustworthiness and Intelligence items. This first factor explained 44.24% 50.21% of the variance for each candidate. Factor 2 contained the Support items. The
variance explained by this factor was 12.93% - 14.44% for each candidate. It seems that
the item stems as opposed to the item content created the factor loadings. For example,
all items in Factor 1 contained the phrase “appears to be.” The only exception being an
item that contained the phrase “I could NOT imagine.” Although the items on Factor 1
were designed to survey four different concepts, they all loaded onto the same factor. In
addition, the items in Factor 1 measured the participants’ attitudes towards the candidate.
In contrast, all items in Factor 2 contained the phrase “If I were a voter in his district, I
would...” and measured the participants’ behaviors in this hypothetical setting.
Congressman Sanford Bishop received the highest ratings on all three explicit
measures: Trustworthiness (M = 3.65, SD = .65), Intelligence (M = 3.33, SD = .51), and
Support (M = 3.15, SD = .70). He received significantly higher ratings than Congressmen
David Scott, Silvestre Reyes, Mike Honda, Joe Courtney, and Lynn Westmoreland on all
explicit measures. Congressman David Scott, when compared to Congressman Bishop,
Johnson, and Gonzalez, received significantly lower ratings on all three measures (see
18
Table 5): Trustworthiness (M = 3.22, SD = .75), Intelligence (M = 3.14, SD = .55), and
Support (M = 2.88, SD = .73).
There were small, negative correlations between Support and the demographic
variables of sex, and economic issues. The negative correlations imply that men, and
those who held conservative views on economic were less likely to support the African
American candidates than women, and those who held liberal views on economic issues.
There were small, positive correlation between Support and socioeconomic status. The
positive correlation implies that participants with high socioeconomic status were more
likely to vote for the White American candidates (see Tables 6 through 8). Although the
aforementioned variables were significantly correlated with Support, the magnitude of all
these correlations was very small. Therefore, I did not use any demographic variables as
controls in subsequent analyses to test my hypotheses.
There was a small, negative correlation between explicit rating of trustworthiness
for Sanford Bishop and scores on the Trustworthiness IAT: r(504) = -.098, p < .05. Aside
from this, there were no correlations between any of the explicit ratings and IAT scores.
Test of Hypothesis 1
The first hypothesis that participants would be faster at pairing
Trustworthy/Intelligent words with White faces than Black faces on the IAT was
supported. Overall, it was easier for participants to associate Trustworthy adjectives with
White American faces and Untrustworthy adjectives with African American faces
(compatible block: M = 773.06 ms, SD = 191.76 ms) than to perform the opposite pairing
(incompatible block: M = 924.62 ms, SD = 258.10 ms; MD Score = 0.38, SD = 0.35, t(505)
= -18.56, p < .001). The effect of participants’ party affiliation was not significant. It was
19
also easier for participants to associate Intelligent adjectives with White American faces
and Unintelligent adjectives with African American faces (compatible block: M = 759.37
ms, SD = 183.20 ms) than to perform the opposite pairing (incompatible block: M =
871.69 ms, SD = 235.37 ms; MD Score = 0.29,SD = 0.35, t(505) = -15.94, p < .001). The
effect of participants’ party affiliation was not significant.
Test of Hypothesis 2
The second hypothesis that the IAT scores and explicit
Trustworthiness/Intelligence ratings of African American candidates would predict
people’s willingness to support these candidates was partially supported. Explicit ratings
of Trustworthiness/Intelligence significantly predicted participants’ willingness to
support the African American candidates; however, their implicit ratings did not (Table
9). Explicit Trustworthy/Intelligence ratings significantly predicted Support for David
Scott, bTrust = .37, t(501) = 10.82, p < .001; bIntelligence = .66, t(501) = 13.85, p < .001.
Explicit Trustworthy/Intelligence ratings also explained a significant proportion of
variance in Support for David Scott, R2 = .63, F(4, 501) = 215.26, p < .001. Explicit
Trustworthy/Intelligence ratings significantly predicted Support for Sanford Bishop, bTrust
= .39, t(501) = 9.50, p < .001; bIntelligence = .66, t(501) = 12.79, p < .001. Explicit
Trustworthy/Intelligence ratings also explained a significant proportion of variance in
Support for Sanford Bishop, R2 = .58, F(4, 501) = 169.49, p < .001. Explicit
Trustworthy/Intelligence ratings significantly predicted Support for Hank Johnson, bTrust
= .27, t(501) = 6.59, p < .001; bIntelligence = .81, t(501) = 15.75, p < .001. Explicit
Trustworthy/Intelligence ratings also explained a significant proportion of variance in
Support for Sanford Bishop, R2 = .57, F(4, 501) = 167.23, p < .001. In short, explicit
20
ratings of Trustworthy/Intelligence significantly predicted support for all three African
American candidates, whereas the implicit ratings did not.
21
Discussion
The current research sought to examine the relationship between a person’s
ethnicity and how that person is rated on measures of trustworthiness and intelligence. It
also sought to examine the relationship between implicit stereotypes and explicit
perceptions of specific political candidates and the willingness to support these
candidates.
I hypothesized that on average, participants would be significantly faster at
pairing White American faces with synonyms for intelligence and trustworthiness than
pairing African American faces with these characteristics. This hypothesis was supported
and was consistent with previous research using the IAT (Cheryan & Monin, 2005;
Devos & Banaji, 2005; Gaertner & Dovidio, 2005; Greenwald et al., 2009; Knowles et
al., 2010; Payne et al., 2010; Yogeeswaran & Dasgupta, 2010). Taking into consideration
the study’s sample of White Americans, the majority of whom were liberal Democrats,
these findings are most supportive of aversive racism theory. The majority of participants
held liberal ideals concerning economic and social issues, yet they still held unconscious,
automatic negative stereotypes toward African Americans. The findings strengthen the
argument that African Americans are still perceived as unintelligent and untrustworthy in
modern day America. It stands to reason that this negative perception could lead to the
perception of African American politicians as less qualified than their White American
counterparts.
In spite of research to the contrary, such as the findings in support of my first
hypothesis that African Americans are automatically linked to untrustworthy and
unintelligent words, some Americans deny the existence of negative feelings being
22
directed towards people of color in modern American society. Some individuals believe
that after the election of Barack Obama, Americans now live in a “post-racial” America
where everyone is judged solely on his/her merit, rather than by ethnicity (Parker, 2009).
The rationalization that an African American president will offset America’s racist past is
defined by Sue and colleagues (2007) as “denial of individual racism”, a
microinvalidation. This is the belief that one is immune to racism because of his/her
personal relationship with a person of color, in the same way one may claim that America
has been purged of racism because Americans elected an African American president
(Sue et al., 2007). In many ways, the denial of individual racism may in fact intensify
issues of racial prejudice in the larger society. For example, Sarah Palin’s use of the
phrase “shucking and jiving” when referring to President Obama’s foreign policy in
Libya was offensive to many (Huffington Post, 2012a). In defense of her comments,
Palin stated that there was “nothing remotely racist” about her remarks and made no
formal apology (Huffington Post, 2012b). The importance of examining the phrase
“shucking and jiving” is that, unlike the terms “tar-baby” or “uppity,” there is no alternate
definition that does not involve a racial connotation (Major, 1994). Her dismissal of the
phrase, and by extension herself, as racist can be explained best by her belief that racism
no longer exists and/or is harmless. This view, reflected by Palin and others, limits our
discussion of race and ethnicity as a source of contention in America and creates
difficulties for racial and ethnic minority group members when trying to overcome these
issues (Sue et al., 2007). The finding in the current study that participants, the majority of
whom held liberal beliefs, found it more difficult to link African Americans with the
23
characteristics of intelligence and trustworthiness than White Americans provides further
evidence that racism continues to exist in American society.
An interesting trend found in the current research is of the skin tone of the African
American candidates (see Figure 1). In all three categories (Intelligence, Trustworthiness,
Support), the ranking of the candidates was always the same. Sanford Bishop, who has
lighter skin-tone than the other two African American candidates, always received higher
ratings, whereas David Scott, who has darker skin-tone than the other two African
American candidates, always received lower ratings (see Table 5). Research has shown
that minority group members with darker skin tones are subject to more negative
stereotyping than their lighter-skinned counterparts (Maddox & Gray, 2002; Uhlmann,
Dasgupta, Elgueta, Greenwald, & Swanson, 2002). Because the candidates in the current
study varied on multiple dimensions, further research is needed in order to gain a better
understanding of how skin tone, among other factors, plays a part in candidate support.
I also hypothesized that implicit perceptions of African Americans and explicit
perceptions of specific African American candidates would predict the participant’s
willingness to support these candidates. This hypothesis was only partially supported.
Although explicit ratings of trustworthiness and intelligence significantly predicted
whether participants supported an African American candidate, I found no evidence that
the implicit IAT scores predicted candidate support. I also found that there were no
correlations between any of the explicit ratings and IAT scores. This is most likely due to
the nature of the measures. The IAT presented participants with controlled stimuli that
varied on very few traits, therefore giving a more direct representation of the implicit
relationship between race and Trustworthiness or Intelligence. The Candidate Perceptions
24
and Support questionnaire was less controlled. The stimuli (i.e., the candidates) in this
measure varied on traits such as age, race, facial features, and so on. With a more
controlled explicit measure, it is likely that there would be a stronger correlation between
explicit and implicit ratings of Trustworthiness or Intelligence.
The finding that implicit IAT scores did not predict candidate support is not
consistent with previous research. For instance, a study conducted by Knowles, Lowery,
and Schaumberg (2010) revealed that participants high in implicit racial prejudice were
less likely to support a healthcare plan proposed by Barack Obama than an identical
healthcare plan proposed by Bill Clinton. Likewise, Yogeeswaran and Dasgupta (2010,
Study 2) revealed that the more participants held the implicit belief that American =
White, the less qualified they perceived Asian American candidates to be for a job
involving national security. Also, Devos and Ma (2012) found that the degree to which
the American identity is implicitly linked to a political candidate could predict a
participants’ willingness to support the candidate. These examples suggest that negative
implicit attitudes, such as racial prejudice, should predict support for a political
candidate. However, an important distinction between the aforementioned studies and the
current research must be made. The current research, unlike the studies recently
mentioned, is a correlational study with no manipulation of the stimuli. The participants
were asked to evaluate real political candidates who varied on multiple dimensions,
including race, facial features, clothing, attractiveness, etc. Because of this, we were
unable to compare the candidates on the basis of race, ethnicity, or any other factor. Also,
we could not control for the preexisting knowledge the participants have of each
candidate or his/her explanations for his/her ratings each. For example, a clear
25
explanation cannot be given as to why Stanford Bishop consistently received the highest
ratings as compared to the other nine candidates. One potential explanation is that the
participants based his/her ratings on the assumption that Rep. Sanford’s opponent was a
White American male and support for Rep. Sanford would also assert his/her support for
liberal causes. However, the current study is unable to address this question. In order to
gain a better understanding of the role of implicit stereotypes with candidate support,
future studies will have to control for these extraneous variables and manipulate the
variable of interest, the candidates’ race.
The role of implicit stereotypes on candidate support could have implications on
the types of candidates and political policies that Americans endorse. Although we may
think of racism as an active response, such as using racial slurs, racism can also be
expressed in more passive ways, such as an unwillingness to support African American
representation in the political system or diversity initiatives. For example, most
Americans are not actively preventing ethnic minorities from attending college; however,
in not supporting diversity initiatives such as affirmative action laws for college
admissions, voters are passively contributing to the racial disparities visible on college
campuses.
Specifically, when examining ethnic minority enrollment in higher education
institutions, the American Community Survey found that from 2009-2011 an estimated
31% of White Americans had at least a bachelor’s degree, whereas only 18% of African
Americans and 13% of Latinos held the same degree (U.S. Census Bureau, 2012).
Affirmative action legislation is one way in which colleges and universities can correct
the imbalance in academic enrollment. The Michigan law school case, a landmark case in
26
affirmative action admission policies, affirmed that race can be used in granting
admission but only if it includes a “holistic view” of each applicant, and if applicants of
all races and ethnicities are given the same kind of evaluation (Grutter v. Bollinger,
2003). In addition, no racial stereotypes, quotas, and/or separate admission groupings can
be used. The university was also asked to review these policies periodically to assure that
the program was still necessary in the coming years. In light of this, affirmative action is
often framed as a violation of the core American values of self-reliance, hard work, and
independence. As the literature on symbolic racism argues, this view allows political
conservatives to practice racism while appearing to practice equality. However, it
prevents progress and maintains the status quo of racial disparity. In this sense, racism is
being expressed not by the active means of Jim Crowism, but in a more passive sense of
allowing the continual existence of racial inequalities. The current research provides
evidence that implicit negative stereotypes of African American’s continue to exist and
that perceptions of intelligence and trustworthiness have the ability to predict willingness
to support an African American candidate. The current study did not, however, examine
implicit attitudes but implicit stereotypes towards African Americans. In future studies, it
would be useful to examine these attitudes using an IAT that not only examines
stereotypes but attitudes as well. Future research also should examine if such implicit
attitudes are related to implicit stereotypes and candidate support. This is an important
issue to address because if a voter holds negative attitudes and stereotypes towards
African Americans, it could be linked to a more passive form of racism, such as
unwillingness to support African American politicians or policies that endorse racial
diversity.
27
In sum, the current research strengthens the argument that African Americans are
still perceived negatively in modern society, and that intelligence and trustworthiness are
important concepts when predicting support for a political candidate. By examining two
specific, widespread stereotypes of African Americans, unintelligence and
untrustworthiness, this study adds to the literature a more specific definition of what
predicts candidate support or the lack thereof. These findings will allow a basis for
further research on the topic of electoral politics.
28
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34
Appendix A
Table 1
Participant Demographics
Variable
N
Sex (%)
Female
Male
Age (%)
18-21
22-25
26-30
31-40
41-50
51-60
61-over
Household Income (%)
Less than $10,000
$10,000-19,999
$20,000-29,999
$30,000-39,999
$40,000-49,999
$50,000-59,999
$60,000-69,999
$70,000-79,999
$80,000-89,999
$90,000-99,999
$100,000-149,999
More than $150,000
Socioeconomic Status (%)
Working poor
Working class
Lower-middle class
Middle class
Upper-middle class
Upper class
Registered Voter (%)
Yes
No
Not Sure
506
43.1
56.9
16
18.8
24.3
21.3
9.9
6.5
3.2
5.7
8.7
15.4
14.2
12.1
12.3
9.1
5.5
3.2
2.8
8.5
2.6
9.9
17
27.7
35.4
9.3
0.8
88.5
9.3
2.2
35
Religion (N)
Agnostic
Assemblies of God
Atheist
Baptist
Buddhist
Christian
Church of Jesus Christ of Latter-day Saints
Episcopal Church
Evangelical
Jehovah's Witnesses
Jewish
Lutheran Church-Missouri Synod
Methodist
None
Other
Roman Catholic Church
Seventh-day Adventist Church
United Church of Christ
Education Level (N)
Elementary - Some High School
High School Graduate
Some College
Associate’s Degree
Bachelor's Degree
Some Graduate School
Master’s Degree
Other Advanced Degree
Residence (%)
South
West
Northeast
Midwest
Economic Issues (%)
Strongly Liberal
Moderately Liberal
Slightly Liberal
Neutral
Slightly Conservative
Moderately Conservative
Strongly Conservative
Social Issues (%)
Strongly Liberal
36
73
6
81
28
7
31
3
5
4
1
11
6
16
132
56
40
3
3
9
66
175
45
137
21
31
22
31
22
22
25
10.1
22.1
22.5
8.3
17.2
13.8
5.9
32
Moderately Liberal
Slightly Liberal
Neutral
Slightly Conservative
Moderately Conservative
Strongly Conservative
Political Party (%)
Republican Party
Democratic Party
Libertarian Party
Socialist Party USA
Green Party
Constitution Party
Reform Party
Third Party
None
22.1
21.7
6.1
9.7
5.9
2.4
16.6
47.4
9.7
1
1.8
0.2
0.2
1
22.1
37
Table 2
Attributes Used for Implicit Association Tests
Intelligent
Trustworthy
Synonyms Antonyms
Synonyms Antonyms
Honest
Corrupt
Clever
Stupid
Truthful
Cheating
Bright
Ignorant
Virtuous
Devious
Brilliant
Dense
Loyal
Unethical
Smart
Mindless
Reliable
Immoral
Insightful Slow
Dependable Criminal
Perceptive Foolish
Upright
Deceiving
Sharp
Witless
Steadfast
Lying
Educated Dumb
38
Table 3
List of Congressional Candidates
State/District Name
Texas 16
Silvestre Reyes
Texas 20
Charles Gonzalez
California 15 Mike Honda
Georgia 13
David Scott
Georgia 2
Sanford Bishop
Georgia 4
Hank Johnson
Connecticut 2 Joe Courtney
Colorado 6
Mike Coffman
Mississippi 3 Greg Harper
Georgia 3
Lynn Westmoreland
Race/Ethnicity
Latino
Latino
Asian American
African American
African American
African American
White American
White American
White American
White American
39
Table 4
Candidate Perceptions and Support Survey
Willingness to support
If I were a voter in this district, I would vote for Candidate A.
If I were a voter in his district, I would be willing to put a Candidate A bumper sticker on my car."
If I were a voter in his district, I would be willing to join a group to campaign for Candidate A.
If I were a voter in his district, I would try to convince my friends and family members to vote for Candidate A.
If I were a voter in his district, I would openly state that I am a supporter of Candidate A.
Intelligence/Superiority
Candidate A appears to be intelligent.
Candidate A appears to have grown up poor.
Candidate A appears to be educated.
Trustworthiness
Candidate A appears to be “up to no good”. (Reverse coded)
I COULD NOT imagine Candidate A committing a criminal act.
Candidate A appears to be trustworthy.
Patriotism
Candidate A appears to be patriotic.
Likability
Candidate A appears to be likeable.
Note. Participants were asked to rate these item on a Likert-type scale from 1 to 5; 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 =
agree, 5 = strongly agree.
40
Table 5
Mean Differences On Explicit Ratings of Trustworthiness, Intelligence, and Support for All Candidates
Mean
Mean
Mean
Mean
Mean
Mean
Mean
SD
Diff.
Diff.
Diff.
Diff.
Diff.
Diff.
(1)
(2)
(3)
(4)
(5)
(6)
Explicit Ratings
D. Scott (1)
3.22
0.75
Trust
3.14
0.55
Intelligence
2.88
0.73
Support
S. Bishop (2)
Trust
Intelligence
Support
H. Johnson (3)
Trust
Intelligence
Support
C. Gonzales (4)
Trust
Intel.
Support
S. Reyes (5)
Trust
Intel.
Support
M. Honda (6)
Trust
Intel.
Support
J. Courtney (7)
3.65
3.33
3.15
0.65
0.51
0.70
0.44*
0.20*
0.28*
3.56
3.27
3.06
0.64
0.51
0.71
0.34*
0.14*
0.18*
-0.08
-0.06
-0.09
3.48
3.27
3.04
0.65
0.50
0.71
0.25*
0.14*
0.16*
-0.17
-0.06
-0.11
-0.09
0.00
-0.02
3.46
3.18
2.98
0.62
0.50
0.69
0.23*
0.05
0.10*
-0.19
-0.15
-0.18
-0.11
-0.09
-0.08
-0.02
-0.09
-0.06
3.44
3.12
2.97
0.70
0.51
0.70
0.22*
-0.02
0.09
-0.21
-0.22
-0.19
-0.13
-0.16
-0.09
-0.04
-0.16
-0.07
41
-0.02
-0.07
-0.01
Mean
Diff.
(7)
Mean
Diff.
(8)
Mean
Diff.
(9)
Trust
Intel.
Support
M. Coffman (8)
Trust
Intel.
Support
G. Harper (9)
Trust
Intel.
Support
L. Westmoreland (10)
Trust
Intel.
Support
Implicit Ratings
3.23
2.97
2.83
0.71
0.54
0.72
0.00
-0.16
-0.05
-0.42
-0.36
-0.32
-0.34
-0.30
-0.23
-0.25
-0.30
-0.21
-0.23
-0.21
-0.15
-0.21
-0.14
-0.13
3.51
3.13
2.98
0.64
0.46
0.69
0.29*
-0.01
0.18*
-0.14
-0.20
-0.10
-0.05
-0.14
0.00
0.03
-0.14
0.02
0.05
-0.05
0.08
0.07
0.01
0.09
0.28*
0.16*
0.22*
3.55
3.12
3.04
0.65
0.50
0.69
0.33*
-0.02
0.16*
-0.10
-0.21
-0.11
-0.02
-0.15
-0.02
0.07
-0.15
0.00
0.09
-0.06
0.06
0.11
0.00
0.07
0.32*
0.15*
0.21*
0.04
-0.01
-0.02
2.95
2.91
2.66
0.74
0.54
0.75
-0.28
-0.23
-0.21
-0.70
-0.42
-0.49
-0.62
-0.37
-0.39
-0.53
-0.37
-0.38
-0.51
-0.28
-0.31
-0.49
-0.21
-0.30
-0.28
-0.07
-0.17
-0.56
-0.22
-0.39
0.29
Intelligence IAT
0.35
3.22
Trust IAT
0.35
Note. *p < .001. Names in bold are African American candidates.
42
-0.60
-0.21
-0.38
Table 6
Correlations of Variables for African American Candidates
1
2
3
4
5
6
7
8
9
10
11
12
13
Sex (1)
1
Age (2)
-0.197
1
Income (3)
0.037 0.048
1
Socioeconomic (4)
0.044 -0.004 0.653
1
Voter (5)
-0.042 -0.070 -0.114 -0.045
1
Education (6)
0.003 0.220 0.211 0.299 -0.086
1
Economic Issues (7)
0.142 0.057 0.045 0.136 0.038 0.073
1
Social Issues (8)
0.053 0.115 -0.014 0.037 0.059 -0.041 0.578 0.903
Scott (Support) (9)
-0.113 -0.001 0.010 0.040 -0.071 0.071 -0.109 -0.024 0.903
Bishop (Support) (10)
-0.107 -0.058 0.054 0.036 -0.120 0.032 -0.094 -0.048 0.651 0.903
Johnson (Support) (11)
-0.126 0.009 0.022 0.027 -0.083 0.061 -0.113 -0.018 0.633 0.766 0.903
Scott (Intelligence) (12)
-0.060 0.026 -0.019 -0.003 -0.117 0.049 -0.036 -0.017 0.739 0.474 0.484 0.903
Bishop (Intelligence) (13) -0.048 -0.010 -0.015 -0.005 -0.102 0.054 -0.012 -0.058 0.437 0.706 0.547 0.469 0.903
Johnson (Intelligence) (14) -0.044 -0.017 -0.034 -0.017 -0.127 -0.004 -0.112 -0.040 0.482 0.566 0.731 0.579 0.602
Scott (Trust) (15)
-0.139 0.024 -0.074 -0.010 -0.032 0.055 -0.053 -0.047 0.700 0.368 0.358 0.644 0.279
Bishop (Trust) (16)
-0.114 -0.074 -0.024 0.002 -0.054 0.025 -0.004 -0.055 0.375 0.660 0.478 0.347 0.631
Johnson (Trust) (17)
-0.178 0.011 -0.068 -0.055 -0.095 0.005 -0.068 -0.084 0.329 0.449 0.600 0.369 0.405
Trust IAT (18)
0.097 -0.015 0.045 0.065 0.005 -0.022 0.029 0.022 -0.036 -0.048 -0.058 -0.018 -0.043
Intelligence IAT (19)
0.122 -0.056 0.010 0.021 -0.011 -0.047 0.048 0.043 -0.039 -0.015 -0.042 -0.038 0.000
Note. p < .01 Bolded; p < .05 Underlined. Cronbach's alpha is displayed for centered variables of Support, Intelligence and Trust.
43
14
15
0.903
0.326
0.422
0.610
-0.052
-0.027
0.903
0.423
0.455
-0.085
-0.079
16
17
18
0.903
0.590 0.903
-0.098 -0.070
1
-0.039 -0.072 0.388
19
1
Table 7
Correlations of Variables for Asian American and Latino Candidates
1
2
3
4
5
6
7
8
9
10
11
12
13
Sex (1)
1
Age (2)
-0.197
1
Income (3)
0.037 0.048
1
Socioeconomic (4)
0.044 -0.004 0.653
1
Registered voter (5)
-0.042 -0.070 -0.114 -0.045
1
Education Level (6)
0.003 0.220 0.211 0.299 -0.086
1
Economic Issues (7)
0.142 0.057 0.045 0.136 0.038 0.073
1
Social Issues (8)
0.053 0.115 -0.014 0.037 0.059 -0.041 0.578
1
Honda (Support) (9)
-0.022 0.042 -0.004 0.094 -0.073 0.101 -0.060 0.021 0.880
Gonzalez (Support) (10)
-0.131 -0.031 0.048 0.090 -0.123 0.087 -0.135 -0.067 0.563 0.880
Reyes (Support) (11)
-0.095 0.005 0.048 0.099 -0.075 0.047 -0.055 0.012 0.574 0.654 0.880
Honda (Intelligent) (12)
0.023 0.011 -0.085 0.026 -0.054 0.054 -0.016 0.003 0.714 0.317 0.332 0.880
Gonzalez (Intelligent) (13) -0.047 -0.052 0.009 0.044 -0.147 0.027 -0.116 -0.085 0.379 0.695 0.460 0.357 0.880
Reyes (Intelligent) (14)
-0.045 -0.012 0.058 0.099 -0.108 0.038 0.006 -0.007 0.412 0.454 0.681 0.406 0.519
Honda (Trust) (15)
-0.009 0.005 -0.065 0.088 -0.037 0.063 -0.042 0.013 0.636 0.266 0.295 0.658 0.260
Gonzalez (Trust) (16)
-0.137 -0.053 -0.072 0.008 -0.087 0.005 -0.087 -0.072 0.317 0.631 0.417 0.278 0.573
Reyes (Trust) (17)
-0.107 0.002 -0.001 0.062 -0.078 0.060 -0.029 -0.022 0.335 0.387 0.615 0.262 0.339
Trust IAT (18)
0.097 -0.015 0.045 0.065 0.005 -0.022 0.029 0.022 0.002 0.022 -0.010 -0.029 0.003
Intelligence IAT (19)
0.122 -0.056 0.010 0.021 -0.011 -0.047 0.048 0.043 0.038 -0.031 -0.015 0.001 -0.026
Note. p < .01 Bolded; p < .05 Underlined. Cronbach's alpha is displayed for centered variables of Support, Intelligence and Trust.
44
14
15
0.880
0.301
0.356
0.579
-0.015
-0.048
0.880
0.410
0.450
0.025
-0.006
16
17
18
0.880
0.521 0.880
0.033 -0.019
1
-0.030 -0.040 0.388
19
1
Table 8
Correlations of Variables for White American Candidates
1
2
3
4
Sex (1)
1
Age (2)
-0.197
1
Income (3)
0.037 0.048
1
Socioeconomic (4)
0.044 -0.004 0.653
1
Registered voter (5)
-0.042 -0.070 -0.114 -0.045
Education Level (6)
0.003 0.220 0.211 0.299
Economic Issues (7)
0.142 0.057 0.045 0.136
Social Issues (8)
0.053 0.115 -0.014 0.037
Harper (Support) (9)
-0.032 -0.011 0.039 0.135
Courtney (Support) (10)
-0.048 0.020 0.025 0.127
Coffman (Support) (11)
-0.108 0.018 0.117 0.207
Westmorland (Support) (12)
-0.021 -0.031 0.048 0.106
Harper (Intelligence) (13)
0.001 0.010 -0.004 0.064
Courtney (Intelligence) (14)
-0.061 0.064 -0.037 0.077
Coffman (Intelligence) (15)
-0.124 0.067 0.098 0.139
Westmoreland (Intellence) (16) -0.034 0.037 -0.001 0.059
Harper (Trust) (17)
-0.036 -0.015 0.007 0.109
Courtney (Trust) (18)
-0.038 0.005 -0.012 0.070
Coffman (Trust) (19)
-0.142 0.018 0.017 0.134
Westmoreland (Trust) (20)
-0.051 0.009 -0.027 0.071
Trust IAT (21)
0.097 -0.015 0.045 0.065
Inteligence IAT (22)
0.122 -0.056 0.010 0.021
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1
-0.086
0.038
0.059
-0.087
-0.062
-0.071
-0.049
-0.067
-0.022
-0.102
-0.036
-0.094
-0.062
-0.016
-0.030
0.005
-0.011
1
0.073
-0.041
0.028
0.089
0.048
0.012
-0.007
0.053
-0.044
-0.018
-0.002
0.045
0.050
-0.046
-0.022
-0.047
1
0.578
0.015
0.033
0.051
0.112
0.070
0.099
0.069
0.103
0.041
0.094
0.064
0.126
0.029
0.048
0.092
0.085
0.111
0.168
0.186
0.119
0.120
0.152
0.195
0.071
0.124
0.118
0.188
0.022
0.043
0.092
0.638
0.685
0.576
0.704
0.473
0.462
0.431
0.623
0.379
0.356
0.317
0.071
0.052
0.092
0.611
0.573
0.430
0.738
0.426
0.402
0.340
0.647
0.281
0.297
0.027
0.034
0.092
0.593
0.457
0.464
0.710
0.433
0.426
0.368
0.612
0.346
0.036
0.027
0.092
0.394
0.369
0.385
0.721
0.291
0.303
0.275
0.698
0.034
-0.025
0.092
0.492
0.437
0.461
0.566
0.316
0.283
0.252
-0.007
0.023
0.092
0.469
0.399
0.311
0.641
0.294
0.207
-0.026
-0.016
0.092
0.413
0.328
0.338
0.569
0.248
0.021
-0.014
0.092
0.282
0.284
0.252
0.620
-0.020
-0.036
0.092
0.436
0.522
0.355
0.097
0.029
0.092
0.453
0.355
0.046
0.006
45
19
20
21
0.092
0.377 0.092
0.059 0.009
1
0.020 -0.052 0.388
22
1
Table 9
Multiple Regression Analyses Predicting Candidate Support with Intelligence and
Trust IAT Scores and Explicit Intelligence and Trust Ratings
R2
b
Std. Error
!
David Scott
0.632
(Constant)
2.876**
0.020
Intelligence IAT
0.019
0.061
0.009
Trust IAT
0.006
0.061
0.003
Explicit Intelligence Rating
0.655**
0.047
0.491**
Explicit Trust Rating
0.374**
0.035
0.385**
Sanford Bishop
0.575
(Constant)
3.152**
0.020
Intelligence IAT
-0.010
0.063
-0.005
Trust IAT
0.020
0.063
0.010
Explicit Intelligence Rating
0.389**
0.041
0.358**
Explicit Trust Rating
0.663**
0.052
0.480**
Hank Johnson
0.572
(Constant)
3.057**
0.021
Intelligence IAT
-0.013
0.065
-0.006
Trust IAT
-0.016
0.065
-0.008
Explicit Intelligence Rating
0.272**
0.041
0.244**
Explicit Trust Rating
0.811**
0.052
0.581**
Mike Honda
0.559
(Constant)
2.966**
0.021
Intelligence IAT
0.083
0.065
0.041
Trust IAT
-0.011
0.065
-0.006
Explicit Intelligence Rating
0.296**
0.040
0.294**
Explicit Trust Rating
0.714**
0.054
0.520**
Charles Gonzales
0.564
(Constant)
3.039**
0.021
Intelligence IAT
-0.026
0.065
-0.013
Trust IAT
0.027
0.065
0.014
Explicit Intelligence Rating
0.379**
0.039
0.346**
Explicit Trust Rating
0.703**
0.051
0.496**
Silvestre Reyes
0.537
(Constant)
2.977**
0.021
Intelligence IAT
0.047
0.065
0.024
Trust IAT
-0.011
0.065
-0.006
Explicit Intelligence Rating
0.372**
0.042
0.332**
Explicit Trust Rating
0.678**
0.052
0.49**
Joe Courtney
0.597
(Constant)
2.831**
0.020
Intelligence IAT
0.073
0.063
0.036
46
Intelligence IAT
0.073
0.063
0.036
Trust IAT
0.028
0.063
0.014
Explicit Intelligence Rating
0.297**
0.038
0.293**
Explicit Trust Rating
0.738**
0.050
0.551**
Mike Coffman
0.569
(Constant)
3.055**
0.020
Intelligence IAT
0.058
0.061
0.030
Trust IAT
-0.010
0.061
-0.005
Explicit Intelligence Rating
0.323**
0.038
0.307**
Explicit Trust Rating
0.779**
0.052
0.536**
Lynn Westmoreland
0.624
(Constant)
2.664**
0.021
Intelligence IAT
-0.006
0.064
-0.003
Trust IAT
0.088
0.064
0.041
Explicit Intelligence Rating
0.413**
0.036
0.406**
Explicit Trust Rating
0.649**
0.048
0.470**
Greg Harper
0.572
(Constant)
3.038**
0.020
Intelligence IAT
0.033
0.063
0.017
Trust IAT
0.071
0.063
0.036
Explicit Intelligence Rating
0.347**
0.038
0.325**
Explicit Trust Rating
0.725**
0.050
0.520**
Note. **p < .001. Centered means of explicit and implicit variables were used. Bold
names are African American candidates.
47
Figure 1
Candidates Used in Current Study
48
Figure 2
Stimulus for Candidate Perceptions and Support Survey
Sanford Bishop
If I were a voter in this district, I would vote for Sanford Bishop ?
!!!!!!!!!"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!$!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!%!!!!!!!!!!!!!!!!!!!!!!!!!!!!!&!
'()*+,-.!/012,)33!!!!!!!!!!!4012,)33!!!!!!!!!!!!!!!!!!536()2-!!!!!!!!!!!!!!!!!!!!!!!!!!!!7,)33!!!!!!!!!!!!!'()*+,-.!7,)33!
49
Appendix B
Study Debriefing
The purpose of this research study is to examine the relationship between negative
perceptions of African Americans and candidate support. Specifically, we examined the
perception of African Americans as unintelligent and/or untrustworthy. Previous studies
have found implicit attitudes about race and ethnicity play a part in political elections
even after taking into consideration other factors such as party affiliation, sex and age.
How was this tested?
In this study, you were asked to complete two Implicit Association Tests (IATs) and one
survey of your candidate support. The purpose of an IAT is to detect associations that
may not be consciously acknowledged. For example, most people may argue that women
are just as likely to be scientist as men are, however, studies show that most people
associate women more strongly with liberal arts and men more strongly with science. All
participants completed both IATs and the candidate support survey. Some participants
were assigned the IAT first, followed by the survey, whereas others were assigned the
candidate survey first followed by the IATs.
Hypotheses and main questions:
We hypothesize that on average, participants will be significantly faster at pairing
trustworthy and intelligent synonyms with White American faces compared to African
American faces on the IATs.
In addition, we hypothesize that after controlling for political party both implicit and
explicit perceptions of trustworthiness and intelligence will significantly predict
candidate support.
Why is this important to study?
Research has shown that implicit attitudes about race and ethnicity play a part in political
elections, and there are many ways these attitudes may be expressed. By examining two
specific, widespread stereotypes of African Americans, unintelligence and
untrustworthiness, we may be able to get a clearer picture of exactly how these
stereotypes may play a role in the election process. These characteristics are commonly
(even if not overtly) attributed to African Americans, and they are likely to be applied to
African American politicians as well. Although voters may not decline to vote for a
candidate solely based on his/her race, because of the often-unconscious nature of
prejudice, it is possible that they would be influenced by stereotypes associated with
African Americans in their assessment of candidates for political office.
What if I want to know more?
If you would like to receive a summary of the findings report when it is completed,
please contact Lee H. Tillman at [email protected].
50
If you have concerns about your rights as a participant in this experiment, please contact
the CSUN Research & Sponsored Projects Department at (818) 677-2901 x. 2901.
Thank you again for your participation.
51