The Implications of Stereotypical News Primes on Evaluations of African American Political Candidates
Item Type text; Electronic Dissertation
Authors Kopacz, Maria Aleksandra
Publisher The University of Arizona.
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THE IMPLICATIONS OF STEREOTYPICAL NEWS PRIMES
ON EVALUATIONS OF AFRICAN AMERICAN POLITICAL CANDIDATES
by
Maria Aleksandra Kopacz
______
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF COMMUNICATION
In Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2007
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THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Maria Aleksandra Kopacz entitled
The Implications of Stereotypical News Primes on Evaluations of African American
Political Candidates and recommend that it be accepted as fulfilling the dissertation requirement for the
Degree of Doctor of Philosophy
______Date: 12/15/2006 Dana Mastro
______Date: 12/15/2006 James Harwood
______Date: 12/15/2006 Henry Kenski
______Date: 12/15/2006 Steve Rains
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and
recommend that it be accepted as fulfilling the dissertation requirement.
______Date: 12/15/2006 Dissertation Director: Dana Mastro
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STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the library.
Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed used of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.
SIGNED: Maria Aleksandra Kopacz
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ACKNOWLEDGEMENTS
I would like to offer special words of gratitude to my advisor, Dana Mastro.
Throughout my graduate career, your constant support, sage advice, unwavering enthusiasm about my work, and friendship, have helped me complete this journey.
Without your guidance I would not be the competent scholar and teacher I am today.
Thank you.
I would also like to thank my outstanding committee members, Jake Harwood,
Henry Kenski, and Steve Rains, for all the invaluable insight, moral support, and endless
patience they extended as I worked on my project. The tremendous help you provided will continue to bear fruit as I work on advancing my program of research.
I would never have completed my degree without the support of my dearest husband, Marcin Aleturowicz. You were there for me every step of the way, helping me make it through my bad days, celebrating my victories, and putting your life on hold to ensure my success. I feel honored, fortunate, and thrilled to be sharing my life with you.
I am also blessed with wonderful colleagues and friends who supported me in so many ways throughout my journey, especially Lissa Behm-Morawitz, Carolyn
Donnerstein-Karmikel, Michael Dues, Peggy Flyntz, Ellen Kovac, Nancy Linafelter,
Michelle Ortiz, Priya Raman, Chris Segrin, and Carey Willits.
Special thanks go to Kanan Sawyer and Dennis Klinzing of West Chester
University of Pennsylvania, Randi Kent of the University of Arizona, and Trina Felty of
Pima Community College, for their invaluable help in data collection for this project.
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DEDICATION
To my Mom – my first Teacher
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TABLE OF CONTENTS
ABSTRACT……………………………………………………………………………....9
I. INTRODUCTION…………………………………………………………….10
News Portrayals of African Americans………………………………………..14
Race-Related Implications of Media Priming…………………………………16
Priming ……………………………………………………………………...16
Media Primes and Responses to African American Individuals ...………….18
Media Primes and Political Decision-Making ………………...……………19
Leader Categorization Theory…………………………………………………27
Implicit Leadership Theories ……………………………………………….27
The Process of Leadership Categorization………..………………….……….. 28
Prototypicality Ratings and Leader Emergence……………………...………. 31
Leader Prototypicality and Political Outcomes…………………....…..…….. 36
Race and Leader Prototypicality in Mixed-Race Elections…………………. 38
Priming, Racial Ingroup Identification, and Electoral Decisions……………...45
Toward a Model of Racial Priming in Mixed-Race Elections…………………47
II. STUDY 1………………………………………………………………………52
Method…………………………………………………………………………52
Participants …………………………………………………………………52
Independent Variables ………………………………………………………52
Dependent Variables…………………………….….……………………………… 54
Control Variables……….………….………………………………………………. 56
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TABLE OF CONTENTS - Continued
Procedure………………..……………………………….………………………… 58
Results………………………………………………………………………….59
III. STUDY 2………………………………………………………………………67
Method…………………………………………………………………………67
Participants …………………………………………………………………67
Independent Variables ………………………………………………………68
Dependent Variables ………………………………………………………..69
Control Variables ……………………………………………………………71
Procedure …………………………..………………………………………71
Results ………………………………………………………………………….72
IV. DISCUSSION……………...…………………………………………………..81
Racial crime news, candidate race, reader racial identification, and leadership prototypicality ………..….………………………………….83
Prototypicality, expectations of policy performance, electoral support, and affect…………….……………………………………………………………....88
The mediated model of news effects………………… …………………………… 92
Direct effects of crime story and candidate race on the dependent variables…………………………………………………………………………….. 93
Theoretical and substantive implications of the study …………………….....97
Limitations and Recommendations For further Research……………………101
APPENDIX A: Stage 1 Instrument (Study 1)………………………………….……...107
APPENDIX B: Stage 2 Experimental Materials (Studies 1 & 2)….……………….....114
APPENDIX C: Stage 2 Instrument (Studies 1 & 2).……………….……………...….120
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TABLE OF CONTENTS - Continued
APPENDIX D: Tables and Figures…...……….….………………...……….………..137
REFERENCES…………………………………….....……………………………….207
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ABSTRACT
The present study aimed at advancing our understanding of the effects that racially stereotypical media discourse has on White voters’ responses to African
American candidates in mixed-race elections. In particular, a causal model was proposed where the racial stereotypicality of news messages was predicted to interact with the race of political candidates and White news consumers’ racial identification in affecting
perceptions of candidates’ leadership prototypicality. In turn, the prototypicality ratings were hypothesized to positively predict expectations of policy performance, candidate affect, and electoral support. In particular, it was predicted that White individuals exposed to racially stereotypical crime news would view African American candidates in unrelated stories as less leader-prototypical than White candidates and this effect was expected be stronger than among Whites exposed to non-stereotypical crime news or no crime news at all. This relationship was also predicted to increase as a function of White
participants’ racial ingroup identification.
The findings from two experimental investigations offered limited support for the
mediated model. The independent variables had weak and qualified effects on the
prototypicality ratings. In addition, most of these effects worked in favor of, rather than to the disadvantage of the African American candidate. However, as hypothesized,
prototypicality was a consistent predictor of electoral support, candidate affect, and, less so, policy performance expectations. Overall, these findings suggest that race matters in mass mediated political processes, both as a contextual factor and as a characteristic of electoral contenders.
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CHAPTER I
INTRODUCTION
The news media system in the United States has been referred to as a key facilitator of the country’s social and political processes (Graber, 1997; Patterson, 1993,
1998). Yet, researchers point to patterns in news coverage of public life suggesting that news continues to misrepresent the society’s major ethnic groups (Greenberg, Mastro, &
Brand, 2002) in ways that may adversely influence intergroup relations and ultimately
perpetuate social inequalities in the United States (Pan & Kosicki, 1996).
Alongside the fact that news remains the most popular source of political
information (Pew Research Center, 2004), news consumption in general is likely to
expose its users to persistently stereotypical and negative portrayals of the largest ethnic
minorities, such as African Americans (Dixon & Linz, 2000a, 2000b; Dixon & Azocar,
2006; Entman, 1994; Entman & Rojecki, 2000). The negative implications of such
portrayals have been demonstrated by multiple studies of media effects, which consistently demonstrate the ability of media exposure to affect White consumers’ racial cognitions, which subsequently influence the evaluations of racial outgroups (Peffley,
Shields, & Williams, 1996; Power, Murphy, & Coover, 1996), race-related policy
positions (Gilliam, 1999; Pan & Kosicki, 1996; Tan, Fujioka, & Tan, 2000) and even the evaluations of White political actors (Mendelberg, 1997, 2001; Valentino, 1999, 2001).
The theoretical basis for much of this literature has been the priming framework, which submits that people’s judgments are informed by cognitions that are most cognitively accessible at the time of the judgment (Domke, Shah, & Wackman, 1998). In
11 the case of racial priming, these considerations may be racial stereotypes which are made salient through exposure to stereotypical media discourse. These activated stereotypical
perceptions then influence individuals’ evaluations and expectations of the members of stereotyped groups (Valentino, 1999, 2001; Power, Murphy, & Coover, 1996 ). Although
priming has been used extensively to examine the effects of exposure to racial stereotypes in media on Whites’ responses to minority targets in a number of contexts
(Mendelberg, 1997; Peffley, Shields, & Williams, 1996; Tan, Fujioka, & Tan, 2000;
Valentino, 1999), these frameworks have not been applied to political elections with minority candidates. As such, the present study seeks to expand this literature by applying the news priming model to test the effects of crime news on White voters’ responses to African American political candidates.
At present, African Americans, as well as the nation’s other racial minorities, are far from being adequately represented on the political scene (Highton, 2004). Moreover,
African American candidates for elective offices with predominantly White constituencies continue to face formidable obstacles to electoral success (Canon, 1999).
A principal explanation offered for these race-related electoral disparities is the reluctance of White voters to support African American candidates (Bullock, 2000; Jones
& Clemons, 1993; Piliawsky, 1989; Vos & Lublin, 2001). Whereas some empirical support exists for this notion, it remains largely unclear what factors, in particular, contribute to Whites’ continued disinclination to elect minority politicians (Hutchings &
Valentino, 2004).
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Notably, research has paid very little attention to the possible implications of media exposure for perceptions of African American candidates as leaders.
Since elections are formalized processes of leader emergence, a consideration of these priming effects may be particularly informative in conjunction with leadership categorization theory (LCT) (Phillips & Lord, 1981; Lord, 1977; Lord & Alliger, 1985;
Lord, Foti, & De Vader, 1984; Lord, Foti, & Phillips, 1982). This framework implies that leader support is a function of potential leaders’ congruence with context-dependent leader prototypes understood as individuals’ perceptions of an effective leader’s desirable traits and behaviors. The more prototypical a leadership candidate is perceived to be in a given leadership context, the greater is the followers’ support for that candidate.
Surprisingly, few researchers have considered this framework when examining voter responses to political candidates (Maurer, Maher, Ashe, Mitchell, Hein, et al., 1993).
Because political campaign communication today places emphasis on the attributes of individual candidates at the expense of parties and issues (Bennett, 2005), the processes of person perception, such as those pertaining to race and leadership, are likely to have substantial impact on voters’ political reasoning. Within this context, then, stereotypical news coverage of African Americans in general would be expected to prime existing cognitions of this ethnic group, which would subsequently affect the extent to which African American candidates’ traits and behaviors appear congruent with relevant leader prototypes, ultimately predicting affective responses and electoral support.
A better understanding of the implications that exposure to news stereotypes of
African Americans carries for the selection of political leaders is important for both
13 theoretical and practical purposes. From a theoretical standpoint, the present study is meaningful as it combines the assumptions of priming and LCT and tests these integrated frameworks in a new context. As such, this study expands the parameters of their application and provides additional clarity in terms of the mechanisms governing the influence of media messages on race-related political decisions.
In practical terms, this investigation may offer indirect insights into the ways in which our political system can become more aligned with the model of representative democracy at the foundation of our government. Such understanding may be particularly consequential to primary elections, which are among the first major hurdles that new candidates must overcome in order to establish themselves on the political scene. Unlike established politicians who have the capital of name recognition and political record at their disposal, new candidates are often virtually unknown to most voters (Patterson,
1993) and are typically competing alongside a multitude of contenders. Moreover, the media coverage of such elections is typically low (Golebiowska, 2001). Given that, and in light of the abundance of stereotypical depictions of racial minorities in local media, as well as evidence demonstrating the potential for racial news primes to result in discrimination among White citizens, these early races are likely to pose critical obstacles to African Americans aspiring to political leadership. As such, it is important to explore the extent to which racially stereotypical news messages influence Whites’ responses to minority political leaders in such contests (Citrin, Green, & Sears, 1990).
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News Portrayals of African Americans
The tendency of the news media to present a distorted, stereotypical, and highly negative image of African Americans has been widely documented. The stereotype of
African Americans as criminal and devious has been particularly prevalent in television news discourse (Entman & Rojecki, 2001; Jamieson, 1992). Although African Americans and Whites appear with relative parity in news stories unrelated to crime, African
Americans are nearly twice as likely as Whites to be depicted when the topic is crime
(Romer, Jamieson, & DeCoteau, 1998). Among the most notable investigations, a content analytic study of local television news in the Los Angeles and Orange County area conducted by Dixon and Linz (2000a) reveals that African Americans are more likely to be represented as crime perpetrators than Whites and are overrepresented as such relative to their actual crime rates. In addition, African Americans are more likely to
be depicted as crime perpetrators than as law defenders. An additional investigation of local television news in that same geographic area additionally indicates that African
Americans are less likely to appear on television news as crime victims than as crime
perpetrators and are less likely than Whites to be portrayed as victims (Dixon and Linz,
2000b). A similar pattern of findings was recently revealed for African American
juvenile offenders, who were found to be overrepresented on Los Angeles area television news relative to their actual share in juvenile crime (Dixon & Azocar, 2006).
Moreover, in these crime stories, African Americans are more often presented as unnamed, menacing, disheveled, and in restraints (Entman, 1990; 1992; 1994; Entman &
Rojecki, 2001; Klite, Bardwell, & Salzman, 1997). For instance, Entman & Rojecki
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(2001), examined the portrayals of African American crime perpetrators in Chicago local television news. The findings revealed that these broadcasts were less likely to contain on-screen names of African American perpetrators or to portray them as individuals than was the case with White perpetrators. The authors suggest that such portrayals deprive depicted individuals of their personal identities while highlighting their race. This, they argue, may lead viewers to think of criminality more as a characteristic defining certain groups in society (especially African Americans) than as an individual trait. Additionally, these findings revealed that African American crime suspects were more likely than
White perpetrators to be shown handcuffed or otherwise restrained by police officers and more likely to be shown in prison or street clothes (rather than in collared suits). Such
portrayals are problematic in that they are likely to increase the perceived societal threat of African Americans as a group, thus intensifying race-based fear among Whites.
National news also appears to disadvantage African Americans in crime coverage.
Dixon, Azocar, and Casas (2003) examined the content of nightly network news, demonstrating that African Americans are more likely to be portrayed as crime
perpetrators than as victims or police officers. They are also underrepresented as crime victims and police officers relative to their actual proportions in society.
As a whole, this body of research suggests that news media content misrepresents
African Americans as devious and criminal, therefore, socially undesirable. Such negative imagery has the potential to prime and perpetuate White consumers’ stereotypes of African Americans as low-status members of the society and in doing so, adversely affect their chances to emerge as political leaders. This is particularly problematic when
16 considering that viewers look to news to provide realistic coverage of the daily events and consider news content to be more faithful in its depictions of reality than fictional media products (Tamborini, Zillman, & Bryant, 1984). Moreover, political campaign news’ tendency to emphasize the race of African American candidates (Terkildsen &
Damore, 1999; Zilber & Niven, 2000) should increase the probability that media users will apply their racial cognitions when responding to African American candidates.
Consequently, the unfavorable news portrayals of African Americans as a group may be
particularly harmful to assessments of African American candidates’ electoral outcomes.
Race-Related Implications of Media Priming
Priming
The effects that exposure to racialized crime coverage may carry for voters’
responses to African American leaders can be best explained in the context of the priming
framework. Priming refers to a process where cognitions, attitudes, or emotions made
salient by prior stimuli, influence the perception and interpretation of new information
(Domke et al., 1998; Fiske & Taylor, 1991). The priming perspective is based on the
assumption that individuals cannot access all the information stored in memory that is
relevant to a given judgment and apply it in its entirety to that judgment (Simon, 1979).
Instead, people tend to rely on the most accessible and convenient mental considerations
(Higgins & King, 1981; Higgins & Bargh, 1987). These are likely to be the constructs
that are either activated frequently or that were activated most recently (Iyengar &
Kinder, 1987; Srull & Wyer, 1978).
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The phenomenon of activation in priming is a function of interconnectedness of constructs in memory, such that exposure to a message brings to mind a host of concepts and models linked to one another in a mental network (Anderson, 1983; Berkowitz &
Rogers, 1986). These constructs then shape responses to subsequent stimuli. For example, watching a television show that portrays of African Americans as criminal and uneducated may raise the salience of racial stereotypes in the mind of a White viewer, whose evaluations of other (real-world and mediated) African American targets may subsequently be adversely affected by the stereotypical cognitions, as was revealed by
Ford’s (1997) experiment.
Extending the general priming model to intergroup relations in the political context, Valentino (1999; 2001) submits that people are likely to respond to race-related cues in political media content in ways that influence their subsequent decisions. In
particular, presenting African Americans in the context of deviance activates negative group stereotypes that guide thinking about African American targets and issues related to them. At a fundamental level, this racial group priming effect is possible because of the highly perceptible physical features that manifest individuals’ racial group membership. These characteristics, including skin color, facial features, names, and accent (Fiske & Taylor, 1991) may help perceivers organize person-related stimuli in terms of racial categories. In this process, attitudes about social groups are likely to
become key factors in framing individuals’ knowledge about various actors in the public life (Valentino 1999; 2001; see also Conover, 1984, 1988).
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Consistent with the priming perspective, media effects literature offers substantive evidence that Whites’ exposure to negative and stereotypical portrayals of African
Americans (particularly in crime news) increases the influence of viewers’ negative racial cognitions and affect on the evaluations of African American targets. Moreover, these
primed group stereotypes have been found to adversely affect Whites’ policy positions on issues related to race, as well as responses to those political actors who may be believed to champion the causes of African Americans in the U.S. government.
Media Primes and Responses to African American Individuals
Among the notable investigations illustrating the racial priming effect on Whites’
reactions to African Americans, Power et al. (1996) measured the effects of news stories
about African Americans on Whites’ perceptions of the extent to which an African
American featured in an unrelated news story (Rodney King or Magic Johnson) was
personally responsible for the predicament he had found himself in (police beating and
contracting HIV, respectively). White participants exposed to stereotypical news
depictions of African Americans made a greater proportion of internal (unfavorable)
attributions of responsibility to external (favorable) attributions, compared to participants
who read positive (counterstereotypical) depiction or participants in the control condition.
Peffley et al. (1996) further demonstrated that race of a criminal perpetrator in a
news story may significantly alter the interpretation of the depicted crime among some
White news users. Participants were exposed to a television story about a violent crime
incident, where the race of the suspect was visually manipulated to be African American
or White. The results revealed that the story with the African American suspect led the
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Whites who had reported higher levels of negative racial stereotyping to evaluate the
perpetrator as more likely to have committed the crime and more deserving of
punishment than when the featured suspect was White. The effects were in the opposite direction for White viewers who held positive initial views of African Americans prior to exposure.
Bodenhausen, Schwarz, Bless, and Wänke (1995) offer additional evidence for the positive impact of favorable racial primes by exploring the effects of exposure to
African American and White media personalities on White consumers’ racial attitudes.
In a series of experiments, participants were exposed to successful, well-liked African
American celebrities (Michael Jordan or Oprah Winfrey), successful but neutrally evaluated African Americans (Jesse Jackson or Spike Lee), or a White celebrity (Julia
Roberts). Findings revealed that exposure to the favorably rated media exemplars was linked with more understanding attitudes about the issue of racial discrimination and more positive attitudes toward African Americans.
Media Primes and Political Decision-Making
Tan et al. (2000) extend this priming research to the political realm by illustrating the effects of negative news primes on race-related policy decisions. They examined the effects of exposure to stereotypical TV portrayals of African Americans on Whites’ affirmative action policy positions. Based on their heuristic model of policy reasoning, the authors found that negative stereotypical television portrayals of African Americans
predicted negative stereotyping in White viewers, who, consequently, were more opposed to affirmative action policies.
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Using a similar design, Mastro and Kopacz’s (2006) survey investigation further refined this empirical model by demonstrating that that the perceived White-ingroup
prototypicality of portrayals of African Americans and Latinos in entertainment media, that is, the similarity of these minorities’ images to White viewers’ ingroup prototype,
predicts real-world perceptions of African Americans and Latinos among White viewers.
In turn, these perceptions affect positions on affirmative action. Specifically, the authors find that the less prototypical the minority media portrayals are perceived to be relative to
Whites’ ingroup, the more likely the White media users are to hold negative stereotypes of African Americans and Latinos, and consequently, the more opposed they are to affirmative action policies. Notably, even though these findings (as well as those of Tan et al., 2000) were obtained using a sample of college students, who are commonly
believed to be more egalitarian in their racial cognitions (Mendelberg, 2001), media
primes do seem to adversely influence race-related political reasoning of this young adult
population.
Substantial literature in the area of race-related media effects also has focused on the impact of news frames (i.e., ways of portraying events and social problems) on stereotype activation and policy reasoning among White citizens. For example, Gilliam
(1999) found that exposure to news messages that frame welfare recipients as African
American, female, lazy, and promiscuous (the so-called “welfare queen” frame), increased the likelihood of negative affect toward African Americans and raised opposition to welfare spending, regardless of the welfare recipient’s race depicted in the experimental stimuli. Gilliam explains this effect by arguing that welfare is a race-coded
21 issue, that is, one implicitly linked with racial minorities through unsympathetic
portrayals of minorities as welfare recipients (see also Gilens, 1999). Therefore, the mere discussion of welfare (especially of welfare abuse), without making race implicitly or explicitly a part of the discourse, may activate White audience members’ racial cognitions.
Furthermore, Iyengar’s (1991) experimental investigations revealed that exposure to news depictions of poverty and crime as African American (rather than a White) issues resulted in White participants’ increased tendency to make individual (instead of more
broad, societal) attributions of responsibility for these problems in society. Additionally, for crime, this effect was especially pronounced among participants exposed to episodic stories, that is, stories depicting individual crime incidents with little attention given to
broad societal context of crime.
Gilliam and Iyengar (2001) revealed consistent framing effects of crime coverage
in local news on viewers’ positions on crime policy. The racially coded crime news frame
portrays criminal acts as generally violent and perpetrated by non-White males. Through
a series of experiments, the authors found that exposure to African American perpetrators
in news stories increased White viewers’ negative evaluations of African Americans and
strengthened their support for tougher regulations on crime.
In sum, this literature suggests that news reports regarding an issue which was
persistently used in the past as a context for unfavorable portrayals of racial minorities,
such as has been the case with crime and welfare, have the potential to prime negative
22 racial cognitions among White media users even if a news story contains no explicit references to the minority in question.
In a comprehensive extension of this framing research, Mendelberg (2001) argues that race-related messages in the media often take the form of implicit racial appeals, that is, messages that prime racial perceptions by making race salient using means other than direct verbal references. Such messages are often used by political candidates who want to mobilize the racially prejudiced voters without violating egalitarian norms of the greater society. This is achieved by discussing social issues, such as crime, welfare, or sexuality, which are stereotypically linked to African Americans, or through visual depictions of African Americans. While on the surface, these implicit appeals sound like racially-neutral conservative messages on broadly understood social problems, they strike the cord of racial resentment deeply held and usually hidden by many Whites.
Mendelberg’s (1997) experimental investigation of the effects of exposure to news coverage of the 1988 assault on a White couple perpetrated by an African American convict, Willie Horton, offers results consistent with this framework. The participants who viewed the Horton story were more likely than those who did not watch the story, to
base their policy judgments on their existing racial prejudice, rather than on the concern about crime. In addition, these individuals were more resistant to affirmative action
policies and reported elevated perceptions of racial conflict in U.S. society.
Mendelberg’s (2001) experimental study further demonstrates the potential of implicit racial appeals to prime negative racial stereotypes and affect. The author exposed a non-student sample to gubernatorial campaign stories, where candidates discussed their
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positions on welfare. The nature of the racial frame was manipulated such that the stories made (a) explicit references to African American welfare recipients, (b) implicit (visual) references to African Americans, or (c) implicit (visual) references to White welfare recipients. Exposure to the implicit welfare stories resulted in greater opposition to welfare policies among prejudiced viewers compared to non-prejudiced ones. The effect of racial prejudice was not as strong in the explicit stereotypical or counterstereotypical
(White) condition.
In another test of implicit racial priming, Valentino (2001) assessed how a media
prime may affect the relationship between racial ingroup identification and race-related
policy reasoning. Participants watched videotaped crime news that visually manipulated the race of the depicted suspect (White photo, African American photo, no photo).
Additionally, a control group was exposed to an unrelated news video. Among White
participants, exposure to an African American crime suspect significantly strengthened the relationship between racial identification and race-related policy reasoning relative to other conditions, such that as White respondents’ racial identification increased, so did their tendency to provide group-based, rather than society-based explanations for the
problem of crime in the U.S. In other words, racial identification in this condition increased participants’ support for the notion that African Americans as a group, rather than more general societal forces, are responsible for proliferation of crime in the U.S.
Several investigations have also examined the impact of implicit racial primes on voters’ responses to White politicians. For instance, Mendelberg (2001) sought to determine if the Willie Horton coverage influenced the 1988 election outcome. To do so,
24 she analyzed the 1988 National Election Studies survey data gathered during the campaign phase when the Horton-related racial appeals were still implicit alongside data gathered after Jesse Jackson’s October 21 accusations of racism began saturating campaign reports (leading news reports to start explicitly discussing Horton’s race).
Mendelberg found that during the period of the implicit racial appeals, negative racial attitudes had a significant effect on the electoral attitudes above and beyond the effect of voters' concern about crime or their party orientation. During this period also, negative racial attitudes intensified the perceived gap between the positions of Dukakis and Bush on government assistance to African Americans, such that Dukakis was perceived to support that assistance to a greater extent than he was in the explicit period. Moreover, as the implicit appeals intensified during the period before October 21, the support for Bush rose and that for Dukakis plummeted. In turn, Bush’s support dropped after news reports
began to explicitly address the issue of Horton’s race (possibly because voters may have found the such explicit appeals unacceptable in the campaign discourse).
Valentino (1999) offered additional evidence that implicit racial appeals can adversely affect White political candidates who are perceived to support the cause of
African Americans. He experimentally tested the effects of exposure to crime news that manipulated the race of the perpetrators, on the evaluations of 1996 presidential candidates and issues. He found that the evaluations of the Democratic presidential candidate (Bill Clinton) on issues of crime and welfare were especially low when the
perpetrators in the news stories were minorities (African American, Latino, or Asian).
Under the same conditions, the ratings of the Republican candidate (Bob Dole) were
25
particularly high, above and beyond the impact of the participants’ party affiliation, ideology, racial policy positions, education, race, and gender. Valentino concluded that such news stories may activate voters’ perceptions of Democrats as “soft” on crime and supportive of minorities, which results in negative assessments. That effect is particularly telling because the news stories used in the study made no direct references to the candidates themselves. An additional manifestation of the strength of these racial primes is the change in welfare-related evaluations of candidates: Because the experimental induction made no reference to this social issue, the primed perception of criminals as typically African American most likely activated the other stereotype of African
Americans as welfare recipients.
In a similar design (Valentino, 2001) examined the effect of perpetrator race in crime news on Whites’ performance evaluations of incumbent Bill Clinton. The findings revealed that exposure to an African American perpetrator (vs. White or racially unidentified one) in crime news increased the effect of Clinton’s performance on the issues of crime and welfare and his concern for White citizens on his overall approval ratings but had no effect on the importance of his economic performance for the overall approval. Valentino’s studies thus demonstrate that race functions as an important node that links a number of constructs in memory and is likely to be primed by subtly racialized media coverage, consequently activating related constructs (here: welfare) even if they are not present in the prime.
Taken together, these studies constitute substantive evidence that racial priming through news exposure is frequent and consequential for race-related political judgments.
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Importantly, empirical evidence demonstrates that priming of negative racial perceptions may occur regardless of individuals’ outward endorsement of racial stereotyping (Devine,
1989). As such, news has the potential to impact race-related judgments in many areas.
Surprisingly, however, literature thus far has not addressed the potential consequences that this stereotype-ridden media discourse may carry for White voters’ responses to
African American candidates. Yet, given the widely documented adverse effects of racial
priming in the general political context, the implications for minority candidates’ electoral outcomes should not be ignored.
A useful framework for examining the nature of racial priming effects in primary elections with minority candidates is leader categorization theory (Phillips & Lord, 1981;
Lord, 1977; Lord & Alliger, 1985; Lord, Foti, & Phillips, 1982; Lord et al., 1984), as this theory explains the process of leader emergence as a function of perceivers’ dynamic appraisal of potential leaders’ individual attributes and behaviors. Originating in organizational psychology, LCT has since been applied across a variety of contexts, including political applications (Maurer, Maher, Ashe, Mitchell, Hein, & Van Hein,
1993) as elections can be conceptualized as structured processes of selecting group leaders. Given the low amount of electoral media coverage and political interest in
Congressional primaries (Golebiowska, 2001), voters may be more likely to rely on candidates’ personal attributes rather than policy issues and party labels in determining candidate support, which may also increase the relevance of candidates’ racial
background for electoral judgments. As such, LCT may help explain how this candidate- relevant knowledge is organized and used in subsequent voting decisions.
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Leader Categorization Theory
Early theories developed to explain leader emergence viewed it as a function of
potential leaders’ personality traits (Kenny & Zaccaro, 1983; Mullen, Salas, & Driskell,
1989), their behaviors (Kirkpatrick & Locke, 1991) or situational considerations (Sherif,
1966). LCT builds on the assumptions of these frameworks, and expands their focus to include contextually grounded cognitions held by perceivers with regard to potential leaders (Lord, Phillips, & Rush, 1980). Specifically, Lord and colleagues (Phillips &
Lord, 1981; Lord, 1977; Lord & Alliger, 1985; Lord, Foti, & Phillips, 1982; Lord et al.,
1984) explain leadership emergence as a direct outcome of leader-related perceptions formed by the members of a leader’s social environment. These leadership perceptions are defined in terms of “an assessment made by observers, or by potential leaders themselves that the target individual has both the qualities typical of leaders and the
potential to exhibit leadership in specific situations” (Lord & Hall, 2003, p. 48). As such, leader perceptions are a function of a dynamic and social process, incorporating the interaction of leaders, followers, group tasks, and contextual factors.
Implicit Leadership Theories
The bases for leadership perceptions are individuals’ implicit theories of leadership, that is, preconceptions about the traits and behaviors appropriate and desirable in effective leaders in different contexts (Calder, 1977; Eden & Leviatan, 1975;
Offerman, Kennedy, & Wirtz, 1994). These preconceptions are typically derived from individuals’ past experiences with relevant leaders, as well as from the immediate informational context (the information that is salient to the perceiver at a given point in
28 time) and are prescriptive of leaders’ personal characteristics, skills, and behaviors. Some of the leader characteristics isolated by Lord et al. (1984) include dedication, intelligence, administration skills, and honesty. Lord’s studies suggest that although implicit leadership theories may be inherently subjective, many perceptions of characteristics considered essential in effective leaders are communicated and shared among the followers in a given context (Hall & Lord, 1995; Hollander & Julian, 1969; Lord et al.,
1984). This consensual nature of key leadership features may be particularly apparent in
political elections, where political leaders’ attributes are frequently the objects of discussion within mass media and among members of social networks (Bennett, 2005).
The Process of Leadership Categorization
Building on the assumptions of Rosch’s (1978) more general theory of cognitive categorization, Lord et al., (1984) further propose that individuals’ implicit leadership theories are organized in terms of abstract and relatively context-dependent cognitive categories that differentiate between leaders and non-leaders and distinguish leaders in some contexts from leaders in others (e.g. managers, senators, presidents). The membership within a leadership category (and thus, leader emergence) is a function of the extent to which an individual is perceived to display a set of personal traits, skills, and
behaviors considered most representative of the category (Foti et al., 1982). Such sets of characteristics are termed category prototypes (Phillips & Lord, 1982) and their content and scope may vary across individual perceivers (Lord et al., 2001), cultures (Gerstner &
Day, 1994), and levels of leader categories (Lord et al., 1984), such that the complexity
29 and richness of leader prototypes increases as the categories narrow in scope (Lord et al.,
2001; Rosch, 1978).
In particular, Rosch (1978) has suggested that three levels of categories exist. At the most inclusive and abstract level are superordinate categories. In the domain of leadership for instance, the category ‘leader’ would be deemed superordinate. Here, the categorization is so inclusive as to incorporate all ranges of leaders from politics, through
business, to sports (Lord et al., 1984). At the next level are basic categories. These
provide greater differentiation ability than the superordinate category by including contextual information into the mental representations – thus, at minimum, allowing individuals to discriminate between leader characteristics in different contexts. More specifically, examples of basic leadership categories would include ‘business leader’,
‘education leader’, ‘political leader’. As such, the category ‘political leader’ is inclusive of political leaders like presidents, party activists, or state representatives. Finally, at the most sophisticated level of specificity are the subordinate categories. The more elaborated nature of these mental representations provides individuals with greater ability to distinguish between context-based leader prototypes such as ‘Republican political leader’ vs. ‘Democratic political leader’ or ‘senator’ vs. ‘mayor’. This most refined, contextualized, and hence, informative, level of leader categories is of interest in the
present study.
Notably, Foti et al. (1982) demonstrated the varied levels of specificity of
prototype conceptualizations in finding that the categories of: “leader”, “political leader”, and “effective political leader” differ from one another in terms of the characteristics
30 considered prototypical of each. On the other hand, a prototype of a given leader category may share some of its characteristics with prototypes of other categories. For example, honesty may be perceived as an important characteristic of both a general category
“political leader” and the much more specific category “senator”. This reflects the fuzzy nature of leadership categories and other social categories, where one cannot isolate a finite set of characteristics that will perfectly differentiate between all category members and all non-members (Cantor & Mischel, 1979; Lord et al., 1982).
Nye and Forsyth (1991) further illustrated the dynamic nature of leader prototypes
by showing that the specific components of leader prototypes, that is, characteristics deemed desirable in effective leaders, may vary from person to person. As such, a follower’s evaluations of a leader may, to an extent, depend on the degree to which the leader conforms to the follower’s individualized prototype.
Nonetheless, although is impossible to identify a single feature that fully defines a given leadership category, and in spite of the individual perceptual factors that shape leadership prototypes for each perceiver, some central attributes do exist allowing individuals to make leadership determinations (Foti et al., 1982). This process of categorization serves the fundamental purpose of reducing the amount of cognitive effort needed to respond to leader-related stimuli. Categorization also helps individuals simplify and organize knowledge about leaders’ traits and behaviors (Nye & Simonetta, 1996).
Further, by providing a set of names and labels shared with others, categorizing also facilitates communication and symbolic representation of leadership phenomena (Cantor
31
& Mischel, 1979). Ultimately, it supports the creation and maintenance of a shared
(consensual) system of beliefs about leaders in different societal contexts.
Prototypicality Ratings and Leader Emergence
Lord and colleagues (Lord et al., 1984; Lord & Maher, 1991) argue that a leader’s
evaluations, as well as followers’ consequent willingness to grant power to the leader,
depend on the extent to which the leader’s attributes and behaviors are perceived to be
consistent with the contextually relevant leader prototype. In other words, through the
process of leader perceptions, perceivers draw on their implicit leadership theories to
determine if a target person fits their image of an effective leader in a given context
(Fraser & Lord, 1988). More specifically, leader prototypicality can be determined on the
basis of high perceived match between individuals’ observed behavior and characteristics
and the prototypical attributes of a relevant leader category. This process begins with
exposure to the potential leader’s actions and attributes, which initiates a limited search
for the matching prototype. The prototype search is limited in that once the match on a
few key observed characteristics is established, a person is deemed to fit a given leader
prototype (Hall & Lord, 1995; Lord et al., 1984, 2001). Such limited prototype search is,
in part, mandated by the dynamic nature of leader prototypes, which renders it difficult to
define clear-cut category boundaries that would be associated with a finite number of
category attributes. Therefore, perceivers in a given situation save cognitive effort by
foregoing what would be a futile attempt at exhausting all the attributes that a target must
have in order to be classified as a leader.
32
High perceived prototypicality of a potential leader has important positive implications for such an individual’s ability to exert control over followers. First, it results in favorable assessments of the evaluated individual’s leadership capability and
performance (Lord & Maher, 1991). Consequently, followers are more likely to entrust the prototypical leader with power in the group. Second, categorization enables low- effort schematic processing of a person’s behavior, such that once categorized as a leader, the individual is subsequently perceived in terms of the relevant implicit leader theory
(schema) and not his/her actual behavior. This mechanism allows perceivers to predict the behavior of categorized individuals consistent with the category prototype. This means that followers form favorable (prototype-consistent) behavioral expectations with regard to individuals rated as leader-prototypical. Ultimately, positive evaluations of
potential leaders are likely to result in such individuals’ actual attainment and/or maintenance of power within groups (Lord & Hall, 2003).
LCT’s predictions about followers’ responses to prototypical leaders have received considerable empirical support. For instance, Lord et al. (1984) sought to examine the effect of individuals’ behavior on leadership perceptions and behavioral expectations by exposing participants to vignettes about a district manager who displayed
prototypical, neutral, or non-prototypical behaviors (the manager’s qualifications and organizational position were kept constant across conditions). Exposure to the vignettes resulted in differential ratings of the manager’s effectiveness, his influence on a new
product’s success, the leadership he exhibited, and his desirability as a district manager.
Specifically, the behaviorally non-prototypical manager received lower ratings across
33 these measures than did the neutral or the prototypical manager. In addition, leader-
prototypicality also affected behavioral expectations, such that the more leader-
prototypical the manager was thought to be, the more prototype-consistent behavioral expectations were associated with him.
Cronshaw and Lord (1987) demonstrated that leadership perceptions can be based on actual behavioral observations by exposing participants to short videos of group interactions that varied the prototypicality of leader information. The authors reveal that
participants formed more favorable general leadership impressions of the leaders who displayed the prototypical behaviors. These leadership impressions were measured as
perceptions of leadership prototypicality, the amount of leader-like behavior exhibited by the target, and participants’ willingness to choose the target as a formal leader. Maurer and Lord (1988) used a similar design, finding a consistent relationship between
prototypicality and leadership impressions. In addition, the authors found that the frequency of prototypical behaviors displayed by targets also contributed to the
perceptions of leadership performance.
More extensive support for LCT’s predictions was offered by Fraser and Lord’s
(1988) investigation, where the manipulation of a business leader’s behavioral
prototypicality resulted in differential ratings of his leadership performance, as well as differential behavioral expectations, such that the more prototypical leader received more favorable assessments and behavioral expectations. The authors also found that the relationship between leader categorization and behavioral evaluations and expectations is
34 mediated by the general impressions formed by perceivers about the target’s leadership effectiveness.
Further support for the outcomes of leader prototypicality is garnered from Hogg and colleagues’ research program, which, among other things, tested the assumptions of
LCT in the context of small-group interactions (Fielding & Hogg; 1997; Hains, Hogg, &
Duck, 1997; Hogg, 2001). This research comprised both lab and field experiments in which group members evaluated real (field) or hypothetical (lab) group leaders.
Leadership prototypicality was measured as the perceived similarity of the evaluated leaders with participants’ subjective leadership prototypes. Results consistently revealed that the stronger the fit with the group members’ leader prototype, the more favorable the evaluation of the target’s leadership ability. Thus, although no single individual would be expected to exactly reflect the salient leader prototype, the closer the individual is to this conceptualization, the more likely he/she will be perceived to be effective. The implication here is that despite fluidity in leader categories, central features of leader-
based categories can still be integrated into contextually appropriate prototypes that reflect mutually shared conceptualizations; integrating information from intrapersonal, interpersonal, and intergroup sources (Lord, Brown, & Harvey, 2001; Lord et al., 2001).
An additional, thus far largely unexplored outcome of leadership typicality may
be the followers’ affect toward the leader. While Lord and Brown (2004) argue that the extent to which a potential leader matches a relevant leader prototype is likely to predict followers’ attraction toward the leader, this expectation has received little empirical attention. Some indirect support for this notion can be garnered from Engle and Lord’s
35
(1997) survey study, which revealed that the extent to which organizational supervisors and subordinates agree on leadership prototypes predicts liking of subordinates by the supervisors.
Kilburn (2005) provides further indirect support by demonstrating that voters’
perceptions of political candidates’ traits predict candidate-related affect. However, the author also reveals that the reciprocal influence of affect on trait evaluations is substantially greater, which suggests that individuals may use cognitive assessments to rationalize their prior affective responses to leaders. Nonetheless, given that emotional responses to political candidates (especially liking) are significant predictors of political support (Brader, 2005; Glaser & Salovey, 1998; Granberg & Brown, 1989), it is of interest whether and to what extent leader prototypicality predicts leader attraction.
Overall, the organizational and small group research findings demonstrate that the extent to which an individual conforms with the set of characteristics viewed as
prototypical of an effective leader in a given context predicts the followers’ impressions of that individual, influences their expectations of the potential leader’s future
performance, might be related to leader affect, and predicts the likelihood that such
person will attain and/or maintain the leadership of a group. In addition, while leader
prototypes are perceptual and, as such, subjective, many of their components may be shared by the members of a given grouping or a social system. Given these findings,
prototypes and prototypicality judgments are likely to serve as useful and important
predictors of leader emergence in diverse contexts.
36
Leader Prototypicality and Political Outcomes
Although LCT has been applied mostly to organizational contexts (Lord &
Maher, 1991), it would be expected that its assumptions can be extended to explain electoral outcomes since abundant evidence illustrates the role of candidates’ perceived attributes and behaviors as predictors of electoral outcomes (Funk, 1996; Kilburn, 2005;
Kinder, 1986; Sullivan, Aldrich, Borgida, & Rahn, 1990). Preliminary support for this assertion has been yielded from Maurer et al. (1993) in their examination of the relationship between leader prototypes and evaluations of presidential candidates. This survey investigation revealed that perceived prototypicality of the 1988 presidential candidates predicted vote support, such that the more leader-prototypical a candidate was
perceived to be, the more willing the respondents were to support that candidate.
Although issue positions predicted vote support among politically involved respondents,
prototypicality was a significant vote predictor among all respondents, regardless of the level of political knowledge and interest. Moreover, prototypicality predicted candidate support above and beyond the effect of the favorability of the evaluated traits. In other words, the distance of candidates’ characteristics relative to voters’ prototype, rather than the mere positive or negative nature of these characteristics, most accurately predicted
political support.
Foti et al. (1982) additionally demonstrated a link between prototypicality and evaluations of political figures. In their study, undergraduate raters isolated a list of traits
prototypical of the category “effective political leader”. These ratings were subsequently compared with longitudinal Gallup Poll data to determine whether the evaluations along
37 the prototypical dimensions predicted the overall leadership rating of President Jimmy
Carter. Their findings demonstrated that Carter’s approval ratings were indeed strongly
predicted by his evaluations on the prototypical dimensions of the “effective political leader” category. Similar relationships were revealed for evaluations of Senator Edward
Kennedy. These results are particularly telling given that the prototypicality ratings were offered by a different sample of respondents than were the evaluations of the two leaders; thus highlighting both the shared nature of prototypes as well as the potential of
prototypicality within the political context to predict leadership ratings above and beyond the mere favorability of traits.
This research provides preliminary support for the idea that leader categorization is a common person perception process associated with the emergence of political leaders. Being fairly automatic and effortless (Alba, Chromiak, Hasher, & Attig, 1980;
Maurer & Lord, 1988; Schiffrin & Schneider, 1977), prototypicality ratings (unlike issue
positions) are likely to serve as useful vote predictors at all levels of voters’ political information. This should render prototypicality judgments important as criteria in lower- level elections, where the average citizen learns relatively little about political contenders’ policy positions (McDermott, 1997) and where the general interest in elections relative to other events is low. Prototypicality ratings should then be useful to
both informed and uninformed voters in determining whom to support for elected offices.
This reasoning reasonable to expect that prototypicality perceptions are the
process through which racialized media discourse can affect White voters’ willingness to
support African American political contenders. Considering that both racial
38 categorization and leader prototypicality are processes within the realm of person
perception and given that race has been widely argued to predict intergroup responses in the political process, it would be expected that race-based judgments primed by racially stereotypical news messages may play a role in evaluations of African American candidates’ leadership prototypicality. Initial support for the role of race in political elections can be garnered from literature on voters’ responses to African Americans running in electoral contests.
Race and Leader Prototypicality in Mixed-Race Elections
Although African Americans constitute over 13% of the U.S. population (U.S.
Census Bureau, 2006), recent studies indicate that they are vastly underrepresented on the
political arena. To illustrate, Canon (1999) revealed that between 1966 and 1996, African
American candidates won a mere .52% elections held in White majority districts. Walton and Smith (2000) further found that up to the year 2000, only four African Americans had been elected to the U.S. Senate. Overall, according to Bositis (2002), African
Americans constituted a mere 1.8% of all elected officials in the year 2001 and this
percentage had remained fairly stable over time. Alarmingly, this underrepresentation is evident at all levels of government (Highton, 2004) and increases as one ascends the hierarchy of political offices.
On the one hand, some studies have failed to obtain support for candidate race as a direct predictor of electoral choice. For instance, although Citrin et al.’s (1990) analysis of 1982 California gubernatorial and school superintendent election revealed that anti-
Black sentiment increased White voters’ preference for White candidates, no effect of
39 candidate race on the electoral choice appeared to emerge (notably, however, the African
American candidates lost both elections under analysis).
Similarly, Highton (2004), assessed exit poll data for House elections in 1996 and
1998, finding no significant direct effect of candidate race on vote choice after controlling for variables such as ideology, party identification, incumbency, voter demographics, etc. However, this relationship was significant prior to the introduction of these, numerous control variables. Moreover, most African American candidates in the elections under analysis were incumbents (that is, well-known politicians with established records) in districts with African American majorities, all of which may have substantially increased these candidates’ electoral outcomes. In addition, no variables measuring candidate-relevant perceptions such as evaluations of candidate traits were considered.
On the other hand, substantial evidence illustrates a significant effect of candidate race on electoral outcomes. For instance, Terkildsen’s (1993) experimental study employing a random sample of registered White voters in Jefferson County, Kentucky, finds support for the notion that race adversely affects African American candidates. In this study, White participants were exposed to newspaper articles and photos presenting
White, light-skinned African American or dark skinned African American gubernatorial candidates, and subsequently reported the level of affect toward the candidates (measured with a thermometer scale) and the likelihood of voting for the candidates. Findings revealed a significant preference for the White candidate over either the dark-skinned or the light-skinned African American candidate, both in terms of affect and vote choice.
40
Consistent with the racial priming literature discussed above, this preference was the highest among voters with high levels of racial prejudice. In addition (controlling for self- monitoring) the dark-skinned African American candidate received more negative responses than the light-skinned one. Of note, the significant results for affect in this study underscore the previously discussed notion that affective responses may significantly predict leader emergence.
Moskowitz and Stroh (1994) further document the impact of candidates’ race on the perceptions of candidates’ suitability for political offices as a function of perceived traits and issue positions. The authors exposed White participants to information about equally qualified African American and White candidates, revealing that White voters holding negative racial attitudes attributed more negative traits to the African American candidate than to the White candidate and were more likely to attribute policy positions they disliked to the African American candidate than to the White candidate. These effects were significant above and beyond the impact of party affiliation. This study suggests, then, that race and racial attitudes may indirectly affect electoral choice by informing the perceptions of character traits and policy stances of African American
politicians.
In indirect support of LCT predictions, Williams’ (1990) findings demonstrate a
significant effect of race on trait evaluations and behavioral expectations toward political
leaders. In this study, White survey respondents rated African American politicians as
less likely than White politicians to achieve such pressing political goals as increasing the
economic growth, decreasing budget deficit, or reaching an arms agreement with a
41 foreign nation. The only area where an African American politician was expected to out-
perform a White politician was in helping the poor and needy. However, respondents ranked this goal as one of the least important. White candidates were also rated more favorably on such dimensions of leadership traits as knowledge, trustworthiness, fairness,
judgment in a crisis, clarity of issue positions, experience, excitement, getting things done, and hard work. African Americans were ranked higher than Whites only on the dimensions of religiousness and liberalism. On the whole, African American candidates were expected to do better on issues stereotypically linked with their racial group, such as drug abuse, teenage pregnancy, and poverty, than on pressing issues of national importance, such as national security or economy. In sum, White candidates are more likely than African American contenders to be rated higher on the dimensions commonly
perceived as instrumental to effective political leadership.
Sigelman et al. (1995) revealed similar links between race, perceptions of
candidate traits, and the perceived potential for goals achievement by gubernatorial
candidates. The authors exposed White respondents to messages of political candidates
whose race was manipulated to be White, African American, or Latino. They found that
moderate and conservative minority (African American and Latino) candidates were
considered to be more compassionate (i.e. able to help the poor, end discrimination, and
be fair to all Americans) than White candidates. Still, Whites received higher scores on
the dimension of competence, which comprised such characteristics as the ability to
maintain the military, control spending, stimulate business, encourage democracy, and
eliminate waste. Moreover, the authors found that while both competence and
42 compassion predicted voting preferences, candidates’ competence ratings predicted vote support more strongly than did their perceived compassion; an outcome which significantly disadvantaged minority candidates’ electoral prospects.
These studies demonstrate that trait evaluations are important predictors of leadership support. In light of this, prototypicality ratings, which are more subtle
predictors, may exert an even stronger impact on political leader emergence. Moreover, this research suggests that race affects the prototypicality of candidates on various traits differently, such that compassion-related characteristics are the domain of African
Americans, whereas competence-related traits are believed to characterize Whites.
Importantly, not all traits are created equal in the political field: The competence traits have been found to matter more in predicting electoral support than have compassion characteristics (Miller, Wattenberg, & Malanchuk, 1986).
Additional insight into this process can be garnered from theoretical literature in the area of group status and leader emergence (Berger, Fisek, Norman, & Zelditch, 1977;
Ridgeway 2001a; 2001b; 2003). These scholars submit that social categories like race are indicative of a group’s perceived social worth and competence . Unlike characteristics such as computer or financial expertise, which are linked with competence only on a specific sets of tasks, race functions as a diffuse status characteristic (Ridgeway, 2001a;
2001b), such that membership in a low status racial group (African Americans) results in
perceptions of members of this group as lower in competence on a variety of tasks, from math abilities, through professional skills, to the ability to lead and manage groups of
people. Cognitions of this nature arise as a function of misattributing the unequal
43 distribution of social standing, material wealth, and other resources among racial groups to differential levels of competence and social worthiness of members of these groupings.
These misattributions create unequal performance expectations toward White and African
American individuals, especially in mixed-race, goal-oriented contexts, where members of different social groups must interact to achieve common objectives (Ridgeway, &
Walker, 1995). More specifically, the members of the status-advantaged group (Whites) are expected to perform better, be more assertive and dominating, and exert greater social influence in the interactions than are the disadvantaged actors (African Americans and other minorities). Such differential expectations frequently function as self-fulfilling
prophecies, such that both the advantaged and the disadvantaged members act
consistently with their expected behavioral scripts (Ridgeway, 1991; Webster & Hysom,
1998). This process of social construction of status informs racial perceptions at the
group level and is reinforced through both interpersonal and mediated contact.
Consequently, status characteristics become components of widely shared racial
stereotypes.
The possible implications for the status construction process related to African
American leaders are twofold. First, the influence of these outcomes on perceptions of
African Americans’ competence is meaningful as it is likely to inform leadership
impressions and expectations of leader performance (Ridgeway, 2003). Second, the
association between status beliefs and competence ratings is likely to affect beliefs about
legitimacy of a potential leader. As a status characteristic, therefore, race in the electoral
context may inform both short-term perceptions of leader prototypicality as well as long
44 term leadership impressions, performance expectations, and the likelihood of an African
American emerging as the victor in a mixed-race context (Webster & Driskell, 1978;
Webster & Foschi, 1988). Because elections with African American candidates and
White voters can be conceptualized as mixed-race, goal-oriented processes, where the
political actors and the public are working to achieve the goals of governance, it is not unlikely that race will function as a status characteristic informing political outcomes.
This theorizing is consistent with the findings from Conway, Pizzamiglio, and
Mount’s (1996) as well as Ridgeway, Boyle, Kuipers, and Robinson’s (1998) experiments, which indicated that status-advantaged individuals (here: Whites) are seen to be more competent and possessing more leader qualities than low-status ones, whereas status-disadvantaged individuals (here: African Americans) are perceived to be more communal and friendly. Unfortunately for the status-disadvantaged group, it is competence and agency, not compassion and communality that most strongly predicts leader emergence (Ridgeway, 2003).
Overall, this research demonstrates that race may serve as a meaningful predictor of White voters’ perceptions of African American political candidates’ traits and
potential for policy performance. Moreover, these findings are consistent with the scholarship revealing that the mere exposure to a racial outgroup member may activate stereotypical cognitions that later predict individual assessments (Banaji & Hardin, 1996;
Lepore & Brown, 1996).
45
Priming, Racial Ingroup Identification, and Electoral Decisions
Ridgeway et al. (1998) argue that status beliefs are different from ingroup favoritism in that they are shared both by the status advantaged (Whites) and the status disadvantaged group (African Americans), and “it is the consensuality of status beliefs that makes them a force in social relations rather than idiosyncratic individual biases”
(Ridgeway, 2001, p. 367). Nonetheless, the salience of Whites’ perceptions of the status-
based distinctions among racial groups may in large part be driven by their need to achieve a positive distinctiveness of their own racial group. As proposed by social identity literature (Billig & Tajfel, 1973; Tajfel, 1978; Turner, 1982), group identities such as race inform individuals’ self-concept and self-esteem. In this view, race
(alongside numerous other social categories to which people belong) provides individuals with normative characteristics that define self-concept. Of course, not all group memberships are salient at all times. However, when contextually relevant, these categories and the associated normative characteristics can be used in intergroup comparisons to maintain and enhance self-concept and self-esteem.
The relevance of race as a social category in intergroup comparisons whose outcomes favor White leaders is likely to be enhanced by racial identification, as the motivation to maintain positive racial distinctiveness among such Whites strongly identifying with their racial ingroup is higher than among weak racial identifiers (Oakes,
Haslam, & Turner, 1994). The implication of strong racial identification for African
American candidates’ leadership outcomes is that the more a White voter identifies with his/her racial ingroup, the stronger the ingroup favoring response (Turner, 1999) and the
46 greater the likelihood that an African American candidate will receive less favorable evaluations than a White candidate.
At a more pragmatic level, the salience of categories used in intergroup comparisons depends on the extent to which these categories maximize the positive distinctiveness of one’s ingroup. Given plentiful negative stereotypes of African
Americans, including low-status characterizations, cultivated in the news media and in society (Oliver, 2003; Oliver & Armstrong, 1998), White perceivers can easily achieve comparative advantage by engaging in race-based evaluations. Given this, the immediate
presence of a racially stereotypical media prime may further increase the salience of race for a candidate-relevant judgment, thus enhancing the role that racial identification plays in the evaluation of a minority leader. As such, racialized news messages and news consumers’ racial identification can be expected to interact in predicting voters’ responses to African American and White political contenders.
Some indirect support exists for the interactive effects of media primes and racial identification on consumers’ reactions to outgroup targets. For instance, Mastro and
Kopacz (2005), who exposed White participants to depictions of Latinos at various levels of stereotypicality, found that, in the ambiguous prime condition, racial ingroup identification intensified the role of race in Whites’ evaluations of Latino targets.
Consistently, Valentino’s (2001) experimental research discussed above also demonstrates that stereotypical racial primes have the greatest negative effect among strong racial identifiers, making this group the most likely to make group-based (rather than societal) attributions of crime as a social problem. This initial evidence renders it
47 reasonable to expect that Whites’ racial identification will interact with media primes in
predicting evaluations of African American and White candidates.
Toward a Model of Racial Priming in Mixed-Race Elections
On the whole, the body of literature on media, race, and leadership presents a
complex and troubling image of the role that race plays in the American political process.
First, the content analytic research demonstrates that news messages contain abundant
and negative depictions of African Americans, especially in terms of portraying this
group as criminal and threatening (Dixon et al., 2003), thus placing Black citizens low in
the societal hierarchy. Second, the priming research demonstrates that such racially
stereotypical news content results in Whites’ negative responses to African Americans as
a group (Peffley et al., 1996; Bodenhousen et al., 1995), discriminatory positions on race-
related policies (Mendelberg, 1997), and unfavorable evaluations of White politicians
who are believed to be concerned with the interests of racial minorities (Valentino, 1999).
Third, these adverse effects of the racial stereotypes in news stories are augmented
among White consumers who strongly identify with their racial ingroup (Valentino,
2001). Within this context, a candidate’s race, made salient by racially stereotypical news
messages is likely to predict voters’ perceptions of leadership prototypicality,
disadvantaging African American candidates relative to the White candidates.
Furthermore, the leadership literature sheds light on the potential perceptual
outcomes of the racially stereotypical news stories for White voters’ responses to African
American (and other minority) candidates. This scholarship presents compelling evidence
in support of the contention that although race may not directly account for voters’
48 responses to political candidates, it may inform the considerations used in developing
political support. In particular, leadership prototypicality appears to be an important
predictor of organizational and political leader evaluations (Foti et al., 1982; Palich &
Hom, 1992) and can predict electoral outcomes (Maurer et al., 1993). More specifically, this research implies that competence (understood as the ability to handle “hard” political issues, such as economy or defense) is considered more central to political leadership than is compassion and other qualities facilitating the performance on social problems like poverty, group discrimination, etc. White politicians receive better assessments on the former than do African Americans because White candidates are seen as more likely to possess instrumental and agentic qualities (viewed as particularly desirable in political office) than are Black politicians.
In light of this literature, the present investigation expands our theoretical understanding of the role of race in political elections by introducing a causal model with leader prototypicality ratings mediating the relationship between stereotypical news content, candidate race, Whites’ racial identification, and White voters’ responses to
African American political candidates. Based on existing scholarship, it would be expected that exposure stereotypical crime news stories (depicting crime perpetrators as
African American) would adversely affect Whites’ evaluations of African American
political candidates’ leadership prototypicality, ultimately influencing the behavioral expectations and the electoral support for these candidates. In particular, exposure to such news stories is likely to prime Whites’ perceptions of African Americans as a low status group, consequently affecting the ratings of candidates’ congruence with leader ideals.
49
This effect should be particularly strong among Whites with high levels of racial identification, since strong ingroup identification should drive individuals to seek intergroup distinctions that advantage the ingroup, thus resulting in self-enhancement.
Given this reasoning, the following hypotheses are proposed.
H1: A three-way interaction is postulated between the race of the suspect featured in a
news story, race of the political candidate featured in an unrelated story, and the
level of real-world racial identification in predicting the candidate’s leadership
prototypicality ratings. Specifically, among White individuals exposed to the
African American crime suspect, as racial identification increases, the African
American candidate will be deemed less leader-prototypical than an equally
qualified White candidate. The racial identification by candidate race effect will
be weaker among individuals exposed to a non-stereotypical crime story (one
depicting either a White suspect or a suspect whose race is not disclosed).
H2a: Leadership prototypicality will significantly and positively influence expectations
of policy performance, and candidate support.
H2b : Prototypicality ratings on the dimension of competence will have a stronger
positive effect on voters’ responses than prototypicality on the dimensions of
compassion.
Specifically, it is expected that news depicting African American perpetrators will make the unfavorable racial cognitions more salient than will crime news with White
perpetrators or no-race crime stories. Moreover, such adverse racial primes will have a more negative effect on the prototypicality ratings of an African American candidate than
50 on the prototypicality ratings of a White candidate. The strength of these effects should
be positively associated with the White viewers’ racial ingroup identification because strong racial identifiers are likely to engage in intergroup comparisons that disadvantage the racial outgroup. Finally, prototypicality ratings (especially competence-related) will
positively predict the expectations of the candidate’s policy performance and electoral support. Of note, both the leader prototype, as well as the ratings of individual candidates on the prototypicality dimensions may be primed by the exposure to the racially stereotypical crime news. However, it is the magnitude of the difference between voters’ responses on these two variables (i.e., the perceived prototypicality) that is of theoretical interest in the present study.
In addition to these electoral outcomes of prototypicality perceptions, some literature suggests that leader-related affect (liking) may be an important outcome of leader prototypicality (Killburn, 2005). Given a limited volume of evidence on this relationship, the following research questions is advanced.
RQ: Does leadership prototypicality influence candidate liking?
To summarize, this reasoning leads to a conceptual model of leadership emergence which incorporates insights from theories of leadership, intergroup frameworks, as well as findings from media research on content and exposure (see Figure
1). This model predicts that exposure to crime news about African Americans is likely to interact with Whites’ racial identification in influencing perceptions of African American candidates’ prototypicality as leaders. In turn, these prototypicality perceptions may affect the expectations of a candidate’s policy performance and electoral support for the
51 candidate and might be consequential for the liking of the candidate. As such, it is expected that racially stereotypical news messages play an important role in the
American political process by priming White Americans’ race-based responses, with ultimate consequences for the electoral prospects of the members of ethnic minority groups.
52
CHAPTER II
STUDY 1
Method
Participants
A sample of 615 undergraduate students from a large, southwestern university and a mid-size northeastern university took part in this study on a voluntary and anonymous basis, 200 males and 331 females (84 individuals did not identify their sex). The average age was 21.21. The sample’s average ideology score was 3.82, the average interest in politics was 4.02, and the average interest in elections was 3.89. The participants were recruited from communication courses and were offered extra credit for their participation. Because the present investigation focuses exclusively on White American voters’ perceptions of minority candidates, only the responses from White participants who identified themselves as U.S. citizens were included in analyses. The sample size was further reduced due to a two-stage design of the study, which resulted in participant attrition. Together, participant race, citizenship, and attrition rate between the two stages of the study reduced the sample size to 277 White U.S. citizens (90 males and 187 females).
Independent Variables
A 4 (African American crime suspect, White crime suspect, suspect race not disclosed, no prime) by 3 (African American candidate, White candidate, candidate race not disclosed) experimental design was used in the examination of the relationship between media, race, prototypicality ratings, and voter preferences. The data were
53 analyzed using multiple regression analyses in order to test the mediated model posited above. Specifically, Baron & Kenny’s (1986) regression-based mediation analyses were
performed separately for each newspaper priming condition in order to analyze the impact of candidate race and viewers’ racial identification on prototypicality ratings and the subsequent effect thereof on candidate liking, expectations of policy performance, and voters’ support for the candidate.
Crime story. In order to address the influence of exposure to news coverage of crime on leadership ratings, a newspaper prime was used. Three versions of the newspaper article varied only in terms of the visual depiction of the suspect (i.e., African
American, White, no-race). In addition, a no-crime-prime control group was introduced to assess the overall effect of the crime story on voter responses..
Candidate race. The second factor visually manipulated the race of the candidate
(either African American or White). Both candidates were fictional, male candidates for the U. S. Senate and were matched on attractiveness, age, facial expression, and dress.
Each candidate was presented in a short newspaper article briefly discussing the candidate’s recent announcement of the intent to run for the Senate and his positions on a small set of international and domestic issues. The content of both articles was identical, with the exception of the accompanying candidate photo (depicting either the White or
African American candidate).
Racial ingroup identification was constructed based on items from group cohesion scales as discussed in Hogg (1992) and used in Mastro (2003) and Mastro and
Kopacz (2005). The items included such questions as: “Compared to the other
54 characteristics which define you, how much do you value your race/ethnicity?” and
“Compared to the other characteristics which define you, how much to you value your race/ethnicity?” . These items were scored using a Likert scale ranging from very much
(1) to not at all (7) (see Appendix C for a full list of items) . The items were analyzed with a principal axis factor analysis with varimax rotation, which yielded a single factor ( α =
.80) accounting for 46.39% of the score variance (see Table 3).
Dependent Variables
Leader-prototypicality. In order to measure perceived leader prototypicality of the candidates, political leader prototype was assessed with a 30-item scale based on prior research on political leader prototypes (Lord & Maher, 1991; Maurer et al., 1993) and consisting of attributes previously isolated to characterize effective political leaders. The items were rated using a Likert scale with responses ranging from does not fit my image at all (1) to fits my image very well (7) (see Appendix C). Participants also rated specific candidates on the same 30-item list of attributes. Candidate scores on each attribute were subsequently subtracted from the prototype scores. The resulting difference scores were then recoded (so that higher scores would indicate higher prototypicality) and examined using principal axis factor analysis with varimax rotation. This analysis initially resulted in a single prototypicality factor ( α = .85), which accounted for 66.86% of total score variance (see Table 1).
Expectations of policy performance . Expectations of the candidates’ performance in office (in terms of policy contributions) were examined with a 25-item measure assessing the perceived likelihood that a candidate would be effective in various areas of
55
policy-making. The responses were recorded with a 7-point Likert scale ranging from definitely wouldn’t (1) to definitely would (7). These items were analyzed with a principal axis factor analysis with varimax rotation, which resulted in three interpretable factors.
Factor 1 comprised Foreign policy issues ( α = .91) and accounted for 50.30% of total item variance. Factor 2 included key Domestic issues that appear to be on the agenda today ( α = .88), explaining 10.25% of total item variance. Finally, Factor 3, labeled
Lifestyle issues (α = .92), consisted mostly of issues related to sexuality, moral values, social groups, and gender. The item Research, which may seem unsuitable for this factor, is likely to have been interpreted as relating to stem-cell research (currently a publicly salient issue often debated in moral terms), and as such, fits within the other morality- related issues in the factor. Factor 3 accounted for 8.10% of total score variance.
Together, the three factors explained 68.66% of variance in the policy performance scores.
Candidate support . The overall impressions of the candidate’s leadership effectiveness and individuals’ willingness to vote for a candidate were measured using
Cronshaw and Lord’s (1987) scale of general leadership impressions. A principal axis exploratory factor analysis with varimax rotation yielded a single factor ( α = .90), which explained 71.84% of total score variance (see also Table 3). In addition, the likelihood of voting for a candidate was assessed with a single-item measure asking how likely
participants are to support the candidate. The 19-point response scale ranged from 5 (very unlikely) to 95 (very likely).
56
Candidate liking. Affect toward the candidates was measured using a thermometer rating scale, where participants were asked to mark the extent to which they experience favorable or unfavorable feelings with regard to a candidate. Participants were asked to enter a number from 0 (very unfavorable) to 100 (very favorable) , with 50 denoting neither positive nor negative feelings. In addition, affect was measured with several items based on Fielding and Hogg (1997) and items typically used in National
Election Studies (NES) surveys. They included items like: “I like [candidate name]” or
“[candidate name] inspires me” . The responses were ranked on a 7-point Likert scale with responses ranging from 1 (does not apply at all) to 7 (applies very strongly) (see
Appendix C for a full list of items). A principal axis factor analysis with varimax rotation
produced two factors with eigenvalues greater than 1. Factor 1, Positive affect ( α = .92)
accounted for 55.48% of total score variance, and Factor 2, Negative affect ( α = .78)
explained 20.61% of the variance. Together, the two factors accounted for 76.10% of the
variance in affect scores (see also Table 4).
Control Variables
In addition to these independent and dependent variables identified above, the
following variables have been demonstrated in past research to serve as important
predictors of political reasoning (Mastro & Kopacz, 2006; Ottati, Wyer, Deiger, &
Houston, 2002) and were measured in order to potentially account for patterns of data
that may not have been anticipated by the expectations posited in the hypotheses above .
However, due to the small sample size, these variables were ultimately not included in
data analysis.
57
Perceptions of racial groups. Real world perceptions of African Americans and
Whites were measured using 14, 7–point semantic differential items (pairs of adjectives),
based on Tan et al. (2000), Gilens (1996), and Smith (1991). Participants were asked to check a space between each set of adjectives.
Media use and perceived realism . The media consumption measure was constructed to include 5 questions about news exposure through various media.
Participants were asked to write their own answer to questions like “How many times per week do you watc h news on television?”. In addition, participants were asked to evaluate the news portrayals of reality, using questions like “In general, do you think newspapers
present events realistically?”. The responses, ranked on a 7-point Likert scale ranged from not at all realistically (1) to very realistically (7).
Interest in politics and elections . Political and electoral interest were assessed with two items measuring the interest in politics in general and interest in elections in
particular. Likert scale responses ranged from very interested (1) to not very interested
(7).
Political ideology . Political ideology was measured with a single item asking
participants to place themselves on a scale that ranges from extremely liberal (1) to extremely conservative (7).
Demographics . Finally, participants reported their gender, race/ethnicity, age, year in college, and U.S. citizenship status.
58
Procedure
The experimental materials were pilot-tested with a convenience sample of 61
White college students (20 males and 41 females; average age = 22.03). Candidate race was manipulated visually (a photo of a White or African American candidate was
presented). No differences between the candidates were perceived due to age (t(59)=.61,
p= .54 ), attractiveness (t(59)=-.80, p=.43 ), and facial expression (χ 2(1)=2.68, p=.10 ).
All participants identified the race of the candidates correctly.
The data collection proceeded in two stages. During stage 1, participants completed a self-administered paper and pencil questionnaire measuring participants’ leader prototypes, racial ingroup identification, perceptions of Whites and African
Americans, interest in politics, state of residence/voting registration, ideology, party affiliation, media consumption, and perceptions of media portrayals of reality. The
participants were also asked to provide their mother’s initials and the last four digits of their Student Identification Number, ostensibly to avoid duplicating data in case one student participated twice, through two different courses. This information was subsequently used to match Stage 1 responses to the responses obtained during Stage 2.
Stage occurred two weeks after stage 1 and was presented as if it were a separate investigation. During stage 2, participants were exposed to sets of newspaper articles, containing one of the news primes and an article about one of the candidates. Participants were told that the articles were reprints of recent stories in a new community newspaper in their state of residence and that the present study aimed to examine their effectiveness in delivering the information to the public. After reading the two articles, participants
59 responded to the dependent measures questionnaire, which measured leader
prototypicality of the candidate, general leadership perceptions, performance expectations, candidate-related affect, candidate thermometer rating (liking), and demographics (gender, year in college, race/ethnicity, age, and U.S. citizenship status).
Results
Table 5 presents the means on candidate prototypicality and the dependent variables across the levels of suspect race in the crime article and candidate race in the
political article. The average level of racial identification for the entire sample for Study
1 was 4.75.
A mediation analysis employing a series of regression models was performed to test the hypothesized mediated model of racial priming. In order to make the interaction effects more interpretable, the mediation analysis was performed at each level of the crime story condition. As such, only candidate race, reader racial identification, and their interaction terms were entered in the model. To further facilitate interpretation, candidate race was recoded into two dummy variables ( White candidate and no-race candidate ) with African American candidate as the omitted category. Given such coding, the intercept in the model including these two dummy predictors represented the predicted dependent variable mean for participants with an average level of racial identification exposed to the African American candidate ( racial identification was centered prior to
being entered in the models, to render the interpretation more intuitive [Cohen, Cohen,
West, & Aiken, 2003]). The dummy coefficients demonstrate the differences in means of the predicted variables between participants in the White candidate condition versus
60
African American candidate condition (White candidate dummy) and between no-race candidate condition versus African American candidate condition (no-race candidate dummy), thus representing the key comparisons of interest in the study.
The interaction terms between reader racial identification and candidate race were created by multiplying the score on each candidate race dummy variable by the centered identification score for each participant. This resulted in two interaction variables, which were entered in the regression models. The resulting racial identification x White candidate interaction coefficient represented the difference between the effect of reader racial identification on the responses to White candidate and the effect of identification on the responses to African American candidate (reference category). In a parallel fashion, the racial identification x no-race candidate coefficient was interpreted as the difference between the effect of reader racial identification on the response to no-race candidate and the effect of identification on the response to African American candidate.
This analysis proceeded as follows. Consistent with the Baron and Kenny (1986) approach, the prototypicality variables (competence, integrity) were first regressed on the independent variables. Subsequently, all the dependent variables (candidate affect, leadership impressions, performance expectations, and vote likelihood) were regressed on the independent variables. Finally, the dependent variables were regressed jointly on the candidate race and the prototypicality ratings.
The sections that follow summarize the results of these analyses. Given the small sample size used in the analyses, these results should be interpreted cautiously.
Nonetheless, these data are useful for assessing the general patterns of relationships
61 among media primes and candidate responses. In addition, considering the small sample, any significant findings are particularly notable and deserving of further exploration.
Hypothesis 1 predicted that suspect race in the crime story would interact with candidate race in the political story and the reader’s racial identification in affecting candidate prototypicality ratings. Specifically, it was expected that participants exposed to the African American suspect in the crime article would rate the African American candidate featured in the political article as less leader-prototypical than the White candidate or the no-race candidate. This effect was expected to increase as participants’ racial identification increased. Table 6 presents the effects of candidate race and reader racial identification on prototypicality ratings. As indicated in the Table, none of the regression models were statistically significant. This qualifies the interpretation of any results within the models. Still, considering that the nonsignificant models might be a function of the small sample size, rather than the lack of relationships among the variables, a cautious inspection of the data trends within the models seems a reasonable approach.
First, there was no significant interaction between reader racial identification and candidate race in any of the conditions. The full Hypothesis 1 was, therefore, not supported. However, candidate race had a significant effect on prototypicality in the no- race crime story condition, such that both White candidate and race unidentified candidate received significantly lower prototypicality ratings than the Black candidate.
This result is in the opposite direction to that hypothesized. To test the statistical significance of the interaction between crime story and candidate race, both candidate
62 coefficients in the no-race condition were compared to corresponding coefficients in the other conditions using a z-test for comparing regression coefficients from independent groups (Cohen et al., 2003). The comparisons of the White candidate coefficient to the
White candidate coefficients in the African American crime story, White crime story, and no-crime story conditions revealed no significant differences ( z = 1.70, ns , z = 1.60, ns , and z = 1.90, ns , respectively). Similarly, the z-tests comparing the no-race coefficient to its counterparts in the he candidate race coefficients in the African American crime story,
White crime story, and no-crime story were not significant ( z = 1.19, ns, z = 1.09, ns , and z = 1.44, ns, respectively).
Moreover, reader racial identification significantly increased candidate
prototypicality ratings in the African American suspect condition and in the no-crime-
prime condition, regardless of the candidate race. The z-test comparisons revealed no significant difference between the two significant coefficients ( z=-.33, ns) . Likewise, no significant differences emerged between the coefficient in the African American crime condition and the White crime or the no-race crime condition ( z.= 0, ns, and z = 1.5, ns, respectively). There were also no significant differences between the coefficient in the no-crime-article condition and and the White crime or the no-race crime condition ( z =
.31, ns; and z = 1.69, ns, respectively).
Hypothesis 2a predicted that prototypicality ratings would influence expectations of policy performance and electoral support for the candidates. As illustrated in Tables
8.1-8.4. and 9, prototypicality was a significant positive predictor of policy performance expectations for all issue factors and across all crime story conditions, with the exception
63 of foreign issues in the White crime story condition. Even that coefficient, however, was in the predicted direction and approached significance at p = .08. Furthermore,
prototypicality significantly and positively predicted leadesrship impressions and vote likelihood across all crime story conditions. Hypothesis 2a was, therefore, supported.
Hypothesis 2b, which predicted that prototypicality ratings on the dimension of competence would have a stronger positive effect on voters’ responses than
prototypicality on the dimensions of compassion, could not be tested in Study 1, as only a single prototypicality factor emerged in the present data.
Research Question 1 inquired whether prototypicality would predict candidate- related affect. Again, Tables 8.1-8.4 and 9 illustrate the relevant results. This output shows that prototypicality was a significant and positive predictor of feelings thermometer and positive affect across all crime story conditions. In addition, it was a significant negative predictor of negative affect in the African American and no-race crime story conditions, however, the latter regression model was not significant. Overall, these data suggest that the more leader-prototypical a candidate is perceived to be, the more favorable is the voters’ affective response toward that candidate.
In sum, the results thus far lend limited support for the mediated model of media effects on voters’ responses to African American candidates. Specifically, some evidence for the hypothesized mediated relationship can be observed in the no-race crime story condition, as illustrated in Tables 6, 7.3, and 8.4. First, Table 6 demonstrates that candidate race significantly and negatively predicts prototypicality for both racial candidate contrasts (with the qualification that the model F value is not significant),
64 however, this relationship is not moderated by reader racial identification. Second, Table
7.3 shows that candidate race in the no-race crime story condition significantly predicted a number of dependent variables (such direct effects are required for a mediation to hold
[Baron & Kenney, 1986]). In particular, the White candidate received significantly less favorable responses on the feelings thermometer, positive affect, issue performance (all factors), and vote likelihood. In addition, the no-race candidate received significantly lower expectations of performance on lifestyle issues. Third, Table 8.3 shows that after adding prototypicality as a predictor in the models, the effects of White candidate on all dependent variables (except lifestyle issues) disappear, as does the effect of no-race candidate on lifestyle issues.
In summary, this pattern of results suggests that suspect race in the crime news story interacted with candidate race in predicting prototypicality ratings, which, in turn
predicted candidate-related affect, expectations of policy performance, and vote likelihood. However, some aspects of these results do not support the theoretical model
proposed in this study. First, reader racial identification did not interact with the other independent variables in affecting prototypicality. Second, the direction of the effect of candidate race on prototypicality and on the dependent variables is opposite to the one hypothesized, such that the White candidate, not the African American one, received more negative responses. Third, the significant mediation relationship occurred in the no- race crime story condition, not, as had been hypothesized, in the African American crime story condition.
65
Although the mediation relationships revealed in this analysis are limited and qualified, the data point to interesting direct relationships between candidate race, reader racial identification, and a number of outcome measures. First, candidate race appears to interact with reader racial identification in the African American crime story condition in
predicting the affect variables (feelings thermometer, positive affect, negative affect) and electoral support (leadership impressions and vote likelihood). To further explore these relationships, Table 10 presents the relationships between racial identification and the dependent variables in the African American crime story condition across levels of candidate race. This output suggests that racial identification significantly increased
positive feelings toward the White candidate (feelings thermometer and positive affect), improved the leadership impressions of the White candidate, significantly decreased the negative affect toward the candidate, and significantly increased the likelihood of voting for that candidate. In addition, racial identification significantly increased the positive affect and decreased the negative affect toward the no-race candidate. Although racial identification had no significant effects on the responses toward the African American candidate, many coefficients were in the negative direction and the negative affect coefficient was in the positive direction. No such interactions emerged in the White crime or the control condition and there was only one such interaction effect in the no-race suspect condition. Table 11 explores that effect by presenting the relationship between racial identification and negative affect toward White and African American candidates
(as this was the candidate pair for which the interaction was significant). The results show that in racial identification significantly decreased negative affect toward the White
66 candidate, while the coefficient for the African American candidate was in the positive direction but non-significant.
Second, since candidate race appeared to significantly influence the feelings thermometer, positive affect, issues performance, and vote likelihood at various levels of the crime story (see Tables 7.1 – 7.4), a series of 4 (African American suspect, White suspect, no-race suspect) x 3 (African American candidate, White candidate, no-race candidate) factorial ANCOVAs with identification as a covariate were performed for the two affect variables, the issues variables, and the vote variable, in order to explore the
potential interaction effects among the primes in a more intuitive fashion. Tables 12- 17 contain the output of these analyses. The factorial ANOVAs demonstrated no significant interactions between candidate race and crime story for any of the dependent variables.
However, there was a significant main effect of candidate race on feelings thermometer,
positive affect, foreign issues, and lifestyle issues. The planned simple comparisons revealed that the African American candidate received significantly higher ratings on feelings thermometer ( p<.05), positive affect ( p<.001), foreign issues ( p<.01), and lifestyle issues ( p<.001) than the White candidate. No significant differences on these variables emerged for the contrast between the African American candidate and the no- race candidate. In addition, the African American candidate received significantly higher ratings on lifestyle issues than the no-race candidate ( p<.05). Of note, however, many of the effect sizes for these ANOVA models were not greater than zero.
67
CHAPTER III
STUDY 2
In order to expand the findings from Study 1 to a non-student population, an additional data collection was conducted with a sample of employees at a large southwestern university. Due to the short time period for which the participants were available a two-stage design was not possible. Therefore, the effects of racial identification could not be tested in this study. As such, the following, constrained version of Hypothesis 1 from Study 1 was tested (Hypothesis 2 and Research Question 1 remained unchanged).
H1a : Racial exposure to crime news story will interact with candidate race in predicting
candidates’ leadership prototypicality ratings, such that among individuals
exposed to racially stereotypical crime news, African American candidate will be
deemed as less leader-prototypical than a parallel White candidate. This effect
will be weaker among individuals exposed to non-stereotypical crime news
stories.
Method
Participants
A convenience sample of 191 employees from a large, southwestern university
were recruited to participate on a voluntary and anonymous basis. All participants were
offered a chance to enter a raffle of twelve $25 prizes for their participation. After
removing the responses of non-White participants (12 cases), the final sample size for the
study was 179 (38 males and 140 females). The average age of participants was 48.52.
68
10.6% of participants reported high school diploma as their highest education certificate,
49.7% reported having four-year college education, 29.1% reported holding Masters or
professional degrees, and 10.6% reported holding a Ph.D. degree. The sample’s average ideology score was 3.41, the average interest in politics was 4.82, and the average interest in elections was 5.02. No significant differences were observed on ideology, political interest, and election interest between the employee sample and the student sample used in Study 1.
Independent Variables
A 4 (African American crime suspect, White crime suspect, suspect race not disclosed, no prime) by 3 (African American candidate, White candidate, candidate race not disclosed) experimental design was used to examine the relationship between news exposure, race, prototypicality ratings, and voter preferences. Subsequently, the data were analyzed using multiple regression analyses (to test the mediation in the causal model) and factorial ANOVA (to analyze interaction effects).
Crime story. This first factor was manipulated using three versions of the newspaper crime story, which varied only in terms of the visual depiction of the suspect
(i.e., African American, White, No-race, Control). The content of the article remained constant across the crime story conditions.
Candidate race. The second factor visually manipulated the race of a male Senate candidate (African American, White, or No-race) presented in a newspaper article discussing the candidate’s recent announcement of the intent to run for the Senate and his
69
positions on issues. The content of the article remained constant across the candidate race conditions.
Dependent Variables
Leader-prototypicality. Political leader prototype was assessed with the same 30- item scale as was used in study 1. The scale included attributes previously isolated to characterize effective political leaders. The items were rated using a Likert scale with responses ranging from does not fit my image at all (1) to fits my image very well (7) . The
prototypicality of specific candidates was then be assessed by asking participants to rate the candidates on the same 30-item list of attributes and by subtracting the candidate scores on each attribute from the prototype scores. These difference scores were then recoded (so that higher numbers would indicate higher prototypicality) and analyzed using principal axis factor analysis with varimax rotation. The analysis yielded two factors with eigenvalues greater than 1 (see Table 18), the first including characteristics relating to competence (educated, good verbal skills, good administrator, experienced,
persuasive, good judgment in a crisis; α = .85), the second including items relating to integrity (fair, strong moral principles, trustworthy, patriotic; α = .81). These two factors
jointly explained 61.51% of the item variance.
Expectations of policy performance . Expectations of the candidates’ performance in office were examined with the same items as in Study 1, assessing the perceived likelihood that a candidate would be effective in various areas of policy-making. A
principal axis factor analysis with varimax rotation resulted with three factors with eigenvalues above 1 (see Table 19) The first factor comprised key issues on the current
70
policy agenda (immigration, economy, spending, environment, healthcare, minorities, unemployment, and poverty; α = .94), the second included mostly civil rights issues
(abortion, research, gays, and women; α = .92), and the final factor included issues frequently framed in moral terms (military, values, family; α = .76). Together, these factors explained 73.80% of the item variance.
Candidate support . Cronshaw and Lord’s (1987) scale of general leadership impressions was again used to measure the overall impressions of the candidate’s leadership effectiveness and individuals’ willingness to vote for a candidate. A principal axis factor analysis with varimax rotation resulted with a single factor (α = .92), which accounted for 76% of score variance (see Table 20). The likelihood of voting for a candidate was also assessed with a single-item measure asking how likely participants are to support the candidate.
Candidate liking. Affect was measured with a scale constructed from Fielding and
Hogg (1997) items and NES items. These items were analyzed with principal axis factor analysis with varimax rotation, which resulted in two factors with eigenvalues greater than 1 (see Table 21). The first factor included positive affect items ( like, inspire, admire, hopeful, proud, inspires ; α = .95) and the second factor comprised of two negative affect items ( angry, afraid; α = .91). Jointly, these two factors accounted for 83.79 of the total score variance. Additionally, affect was measured using a thermometer rating scale.
71
Control Variables
In addition to these independent and dependent variables identified above, the following control variables were measured, however, due to the small sample size, they were not included in data analysis.
Media use and credibility . The media consumption measure included questions about news exposure through various media, as in Study 1.
Interest in politics and elections . Political and electoral interest was again assessed with two items measuring the interest in politics in general and interest in elections in particular. Likert scale responses ranged from very interested (1) to not very interested (7).
Political ideology . Political ideology was be measured with a single item asking
participants to place themselves on a scale that ranges from extremely liberal (1) to extremely conservative (7).
Demographics . Participants also reported their gender, race/ethnicity, age, and
U.S. citizenship status.
Procedure
The data collection proceeded in a single stage and took the form of an online experiment. All participants were recruited via email messages and were invited to
participate in the study by following a link included in the recruitment email. Having done so, participants were randomly assigned to experimental conditions through a random hyperlink generation script. Upon accessing the study page, each participant read a crime article (or no article if placed in the control condition), followed by a series of
72 filler questions (or no filler questions if placed in the control condition). Next, each
participant read the candidate article (all experimental stimuli were identical to those used in Study 1, except they were scanned into a computer file and presented in an electronic format). Participants were again told that the articles were reprints of recent stories in a local newspaper and that the present study aimed at examining their effectiveness in delivering the information to the public. After reading the articles, participants responded to the dependent measures questionnaire, which assessed leader prototypicality of the candidate, general leadership perceptions, performance expectations, candidate-related affect, candidate thermometer rating (liking), leader prototype (the prototype scale was randomly administered at one of two different points in the questionnaire), interest in
politics, ideology, party affiliation, media consumption, perceptions of media portrayals of reality, and demographics (gender, year in college, race/ethnicity, and age). Upon completion of the online experiment, participants read the debriefing and were invited to
participate in the raffle.
Results
A mediation analysis consistent with Baron and Kenny’s (1986) approach was
performed in order to test the proposed causal model of media priming. As in Study 1, the mediation analysis was performed at each level of the crime story, so that only the candidate race was used as the independent variable in the models. Candidate race was again recoded into two dummy variables ( White candidate and no-race candidate ), with
African American candidate as the omitted category. Since Study 2 did not include a
73 measure of racial identification, the two candidate race dummies were the only independent variables entered in the models at each level of the crime suspect race.
The prototypicality variables ( competence , integrity ) were first regressed on the independent variables. Next, all the dependent variables (candidate affect, leadership impressions, performance expectations, and vote likelihood) were regressed on the independent variables. Finally, the dependent variables were regressed jointly on the candidate race and the two prototypicality factors. Table 22 presents mediating and dependent variable means across levels of crime story and candidate race and Tables 23 –
25 present the output of the mediation analysis.
As was the case with Study 1, the results below should be approached with caution, given the small sample size, however, any statistically significant findings obtained with this small sample are particularly notable.
Hypothesis 1a predicted that the race of the suspect featured in a crime news story would interact with candidate race in predicting candidates’ leadership prototypicality ratings, such that among individuals exposed to racially stereotypical crime news, the
African American candidate would be deemed less leader-prototypical than the White candidate. This effect was expected to be weaker among individuals exposed to non- stereotypical crime news stories. As noted in Table 23, candidate race had little effect on either of the two prototypicality factors across any of the race prime conditions. The exception was the significant effect of exposure to the White candidate on evaluations of integrity prototypicality in the African American crime story condition ( B = -.82, p <
.05). However, this effect was in the opposite direction to that hypothesized (the White
74 candidate is viewed as less prototypical in terms of integrity than the African American candidate). In addition, the effect did not differ significantly from the corresponding effects in the White crime story condition, no-race crime story condition, or the no-article condition ( z = .96, ns , z = .68, ns , and z = 1.07, ns , respectively). Moreover, the regression model in which this effect appears is not significant ( F (2,38) = 2.31, p =.11).
In light of these findings, it should be concluded that hypothesis 1a was not supported in
Study 2.
Hypothesis 2a stated that leadership prototypicality would significantly and
positively influence expectations of policy performance and electoral support. Tables
25.1- 25.8 present the effects of prototypicality on the dependent variables across crime article conditions, controlling for candidate race. In addition, Table 26 presents a
bivariate correlation matrix for the prototypicality variables and the dependent variables.
As indicated in Tables 25.1-25.8, competence and integrity prototypicality were significant, positive predictors of expected agenda issues performance in the no-race crime story (p < .01 and p < .05, respectively) and no-crime-story condition ( p < .01 and
p < .05, respectively). Competence also significantly predicted expected performance on
civil rights issues in the White crime story condition ( p < .05). It had no significant
relationship to performance on moral issues in any crime story condition. In turn,
integrity significantly predicted expected performance on moral issues in African
American crime condition ( p < .01) and no-crime-story condition ( p < .05) but it did not
predict performance on civil rights issues in any crime story condition. Moreover,
competence significantly and positively predicted leadership impressions across all
75 conditions (( p < .05, p < .001, p < .001, and p < .001) and integrity predicted leadership impressions in all but the African American crime story condition ( p < .001, p < .05, and p < .001). Further, both prototypicality variables significantly and positively predicted vote likelihood (see Tables), with the exception of the no-crime-story condition, where integrity had no effect on vote.
Hypothesis 2b predicted that prototypicality on the dimension of competence would have a stronger positive effect on voters’ responses than prototypicality on the dimensions of compassion. A direct test of this hypothesis could not be performed
because a compassion factor of the prototypicality ratings did not reliably emerge in the
present data. Instead, the integrity factor was isolated which encompassed the traits of fairness, moral principles, trustworthiness, and patriotism. Since this factor has
previously been isolated in political research (Funk, 1999; Kinder, 1986; Pancer, Brown,
& Barr, 1999; Williams, 1990), it was included in an exploratory analysis examining the relative strength of the prototypicality factors. It was expected that compassion prototypicality would be the stronger of the two predictors in this analysis as prior research on candidate traits has shown that competence traits have been among the strongest candidate characteristics to predict electoral support (Funk, 1999). This analysis was performed by entering both prototypicality variables into the regression models alongside other independent variables and comparing the regression coefficients of the competence and integrity factors within each level of the crime story. The comparisons were performed by means of t-tests for comparing regression coefficients within the same sample, as suggested by Cohen et al. (2003). These comparisons revealed that
76 competence prototypicality exerted a significantly stronger effect on a number of outcomes than integrity prototypicality. In particular, competence prototypicality was a significantly stronger predictor of leadership impressions across all crime story conditions ( t (35) = 2.09, p < .05; t (42) = 2.59, p < .05; t (42) = 4.29, p < .01; t (32) =
4.57, p < .01), agenda issues performance expectations in the no-crime-story condition ( t
(38) = 2.65, p < .05), and vote likelihood in the White crime story condition ( t (42) =
2.87, p < .01) and no-crime-story condition t (32) = 2.80, p < .01. Also, a number of
consistent but non-significant differences were observed between the coefficient pairs.
Specifically, in the African American crime story condition, competence was the stronger
predictor of feelings thermometer and positive affect, and in the White crime story
conditions, it predicted negative affect more strongly. Of note, integrity prototypicality
was not a significantly stronger predictor than competence of any dependent variable in
any of the conditions. Furthermore, the condition where the fewest differences occurred
between the two prototypicality factors was the African American crime story. This
somewhat increased relative importance of compassion prototypicality in the African
American crime story condition might indicate that the normative perceptions of Blacks
as compassionate were primed by the news story.
Research Question 1 inquired whether prototypicality would predict candidate
affect. The data, as presented in Tables 25.1-25.8, suggest an affirmative answer to that
question. Both competence and integrity prototypicality predicted affective responses
across conditions. In particular, both prototypicality variables significantly and positively
predicted ratings on the feelings thermometer in all but the no-crime-story condition,
77 where integrity had no effect on the thermometer ratings ( p < .01, p < .001, p < .001, and p <. 05 for competence, and p < .0.01, p < .01, p < .05 for integrity). Similarly, both
prototypicality factors were significant positive predictors of positive affect, except for the African American crime story condition, where integrity did not predict positive affect ( p < .05, p < .001, p < .001, and p < .001 for competence, and p <.001, p < .01, and p < .01 for integrity). Finally, competence prototypicality significantly and negatively
predicted negative affect in the White crime story condition ( p < .05) and integrity significantly and negatively predicted negative affect in the African American crime story condition ( p < .05). Given that some of the models within which these coefficients occurred were non-significant or marginally significant, one may conclude that
Hypothesis 2a received partial indirect support.
In light of the affirmative answer to Research Question 1, follow up analyses were
performed to examine of predictions of Hypothesis 2b would hold for the affect
measures. To do so, t-test were conducted comparing prototypicality coefficients for
these dependent variables. Consistent with the results obtained earlier for performance
expectations and electoral support, competence prototypicality influenced affect more
strongly than did integrity prototypicality in several instances. Specifically, it was a
stronger predictor of the feelings thermometer in the White crime story ( t (35) = 4.54, p <
.01) and no-race crime story condition t (38) = 3.74, p < .01), positive affect in the no-
race crime story condition ( t (42) = 2.52, p < .05) and the no-crime-story condition ( t (32)
= 3.35, p < .01),
78
One must acknowledge, however, that regardless of this partial evidence supporting Hypotheses 2a and 2b, the effects of prototypicality on the dependent variables are of qualified importance at this point, given the lack of support for
Hypothesis 1, and with it, the causal model, which proposed that prototypicality mediates the relationship between the news primes and the candidate responses (Figure 1).
Indeed, a closer inspection of the patterns of regression coefficients in Tables 24 –
25.8 reveals that most effects of the independent variables on the dependent variables decreased in magnitude and some coefficients in the three crime story conditions (not the control condition) lose significance when prototypicality variables were added into the models. While this pattern cannot serve as substantive evidence of mediation, it is reasonable to expect that a larger sample might yield more significant relationships, especially considering that both prototypicality factors significantly predicted most dependent variables across crime article conditions.
Taken together, these results suggest that prototypicality ratings in Study 2 did not mediate the relationship between the prime variables (crime story and candidate race) and the dependent variables (candidate affect, performance expectations, leadership impressions, and vote likelihood). Therefore, the causal model of media priming in which leader prototypicality ratings mediate the relationship between crime story and candidate race and electoral support, does not appear to hold for the non-student sample.
On the other hand, Table 24 suggests numerous significant effects of candidate race on affect, leadership impressions, performance expectations (except for moral issues
performance), and electoral support at various levels of the crime story. In light of these
79 findings, additional analyses were conducted to examine whether crime story and candidate race interacted in affecting voter responses. Specifically, a series of 4 (African
American suspect, White suspect, no-race suspect, no prime) x 3 (African American candidate, White candidate, no-race candidate) factorial ANOVAs were performed to assess these relationships. Tables 27-35 and Figures 8-14 contain the output from these analyses.
The factorial ANOVAs revealed a significant crime story by candidate race interaction effect on the feelings thermometer (Table 27, Figure 8). Consequently a oneway ANOVA was conducted at each level of the crime article to examine the simple main effect of candidate race on feelings thermometer (Table 28). A oneway ANOVA was also conducted at each level of the candidate race to analyze the effects of crime story on the feelings thermometer for each candidate (Table 29). As Table 28 demonstrates, candidate race significantly affected the feelings thermometer among
participants exposed to the African American suspect and the White crime suspect.
Scheffe’s post-hoc tests were used to examine the differences among means. These tests revealed that among participants exposed to the African American suspect, the African
American candidate received significantly higher thermometer ratings than the White candidate ( p<.001) and significantly higher thermometer ratings than the no-race candidate ( p<.01) (see also Table 22 for cell means). In the White crime suspect condition, the post-hoc tests showed that the African American candidate received significantly higher thermometer ratings than the White candidate (p<.05). Furthermore, as Table 28 illustrates, the analysis revealed no significant effects of the crime story on
80 the thermometer ratings toward each candidate. However, the F ratio for the African
American candidate approached significance ( F(3,49) = 2.67, p = .058).
Tables 30-35 and Figures 9-14 present factorial ANOVA output for the remaining dependent variables. This output demonstrates that the crime story had no significant effect on any of the remaining dependent variables, nor did it significantly interact with the candidate race in affecting these variables. However, candidate race did significantly influence candidate-related affect, leadership impressions, expectations of performance on agenda issues, and the vote likelihood. Scheffe’s post-hoc tests for these main effects revealed that the African American candidate received significantly higher scores on
positive affect than the White candidate and the no-race candidate ( p<.001 and p<.01, respectively), significantly lower negative affect than the White candidate ( p<.05), significantly more positive leadership impressions than the White candidate ( p<.001), and significantly more favorable performance expectations on agenda issues than the
White candidate and the no-race candidate ( p<.001 and p<.05, respectively). Moreover, the vote likelihood was significantly greater for the African American candidate than for the White candidate and for the no-race candidate ( p<.001 and p<.05, respectively).
In sum, the African American candidate appears to have received the most favorable responses on all of these variables and the White candidate received the least favorable responses (see also Figures 9-14 for illustration).
81
CHAPTER IV
DISCUSSION
The purpose of the present study was to examine the effects of racially stereotypical news messages on voters’ responses to African American political candidates. The content-analytic research demonstrates that news presents a fundamentally distorted and factually inaccurate view of African Americans by overrepresenting members of this group as criminal and dangerous to society (Dixon &
Linz, 2000a, 2000b; Dixon & Azocar, 2006; Entman, 1994; Entman & Rojecki, 2000).
Given the low representation of Blacks across various levels of the U.S. government
(Canon, 1999; Walton & Smith, 2000), it was of interest whether the racially stereotypical news discourse might result in a detrimental effect of race on African
American politicians’ electoral outlook.
Based on predictions derived from priming literature (Dixon, 2006; Valentino,
1999, 2000), it was expected that exposure to racially stereotypical crime news stories
(i.e. stories depicting crime suspects as African American) would adversely influence
White voters’ responses to African American political candidates. The assumptions of
LCT (Phillips & Lord, 1981; Lord, 1977; Lord & Alliger, 1985; Lord, Foti, & Phillips,
1982; Lord et al., 1984) and status characteristics literature (Conway et al., 1996;
Ridgeway, 2003; Ridgeway et al., 1998) informed the expectation that when exposed to a racially stereotypical depiction (African American crime suspect), White voters would rate an African American candidate less leader-prototypical than an equally qualified
White candidate. Since the LCT research overwhelmingly suggests that leadership
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prototypicality (congruence with one’s mental image of an ideal leader) is a key predictor of support (Foti et al., 1982; Maurer et al., 1993), the lower prototypicality ratings were subsequently expected to adversely affect the policy performance expectations and the electoral support for the African American candidate relative to the White candidate. In addition, the study explored whether lower prototypicality ratings would also reduce liking of the candidate. Finally, consistent with social identity literature (Mastro &
Kopacz, 2005; Oakes et al., 1994) White news consumers’ racial ingroup identification was expected to moderate the relationships between racial crime primes, candidate race, and responses to the candidates. As such, this investigation proposed a causal model with leadership prototypicality mediating the relationship between racial crime news, candidate race, and candidate responses (see Appendix D, Figure 1).
These predictions were examined by means of two experimental investigations which exposed participants to crime newspaper stories and political campaign stories where the race of the crime suspect and the race of the political candidate were manipulated visually. The mediating variable of interest was the leadership
prototypicality rating of the candidate. The dependent variables were (1) expectations of
policy performance, (2) electoral support (leadership impressions and vote likelihood), and (3) candidate-related affect (feelings thermometer, positive affect, and negative affect). The sections that follow summarize the results of both studies and interpret them in light of extant literature. Next, this investigation’s limitations are discussed and recommendations for future research are proposed.
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Racial crime news, candidate race, reader racial identification, and leadership
prototypicality
LCT suggests that individuals form implicit, context-specific theories about effective leadership that are cognitively organized and stored as leader prototypes (Lord et al., 2001). Consequently, individuals assess the extent to which most potential leaders,
(including those in the political context, as shown by Maurer et al., 1993) are similar to these prototypes before follower support is granted or denied. As the status characteristics literature suggests, race may inform these prototypicality ratings by serving as a heuristic link to competence and status (Ridgeway, 2003). This is meaningful given priming research demonstrating that exposure to media content emphasizing race and presenting it in unfavorable light may prime media users to rely on their racial identity and negative racial stereotypes in evaluating minority targets (Power et al., 1996; Tan et al., 2000).
Crime news story, candidate race, and racial identification interaction . Based on this reasoning, Hypothesis 1 predicted that the race of the crime suspect featured in a news story would interact with (a) the race of the candidate featured in a campaign story and (b) White readers’ level of racial identification in predicting candidates’ leadership
prototypicality ratings. In particular, it was expected that among individuals exposed to racially stereotypical crime news, an African American candidate would be deemed as less leader-prototypical than an equally qualified White candidate, and this effect would strengthen as readers’ racial ingroup identification increased. This relationship was not expected to be as strong in the other crime story conditions. In Study 1, the predicted
84 three-way interaction did not emerge and since Study 2 did not include a measure of racial identification, one must conclude that Hypothesis 1 was not supported.
Crime news story and candidate race interaction. In turn, Hypothesis 1a predicted a two-way interaction between the crime news story and candidate race. Specifically, among individuals exposed to the African American crime story, the African American candidate was expected to be rated as less prototypical than the White candidate and the no-race candidate. The ratings of the Black candidate were predicted to be lower in the
African American crime story condition than in any other condition. Interaction effect did emerge in both studies. However, in Study 1, candidate race had a significant effect on
prototypicality ratings in the no-race crime condition only. Moreover, the effect was in the direction opposite to that hypothesized. In other words, participants who read the no- race crime story subsequently judged the African American candidate to be more leader-
prototypical than the White candidate and the no-race candidate. No such effect was observed in the African American, White, or no-race crime story condition. A somewhat similar interaction emerged in Study 2, where the effect of candidate race was significant in the Black crime story condition. Here, too, the results showed favorable ratings for the
African American candidate. In addition, this effect emerged only for the integrity
prototypicality factor, and only for the White-Black candidate contrast.
Overall, then, it appears that although the crime story (in the African American
and race-unidentified condition) did prime candidates’ prototypicality ratings, did so to
the advantage of the African American candidate, which is not consistent with
Hypothesis 1a. The most intuitively appealing explanation for this unpredicted, outgroup-
85 favoring response (as well as the lack of group-based preferences in other conditions), can be garnered from the aversive racism literature. According to Dovidio and Gaertner
(1986, 1998), although many White Americans today declare egalitarian attitudes toward racial minorities, they continue to harbor deep, hidden, negative perceptions of racial outgroups. Motivated to act in a manner consistent with an egalitarian value system, such individuals strive to withhold any ingroup-favoring expressions for fear of appearing racist. This allows them to maintain an egalitarian identity, which is central to their self- concept (Gaertner & Dovidio, 1986). On the other hand, group identity concerns may simultaneously drive these individuals to seek positive self-concept through discriminating against members of racial outgroups. Nonetheless, these discriminatory tendencies are manifested only if the norms for evaluations derived from the communicative context are sufficiently ambiguous for the discrimination to remain unnoticed (i.e., when it is unclear what type of evaluation would be most desirable).
Conversely, whenever the context dictates egalitarian norms of behavior, aversive racists are careful to treat all individuals equally any may even outwardly favor outgroup members over ingroup members (Aberson & Ettlin, 2004; Murphy-Berman, Berman, &
Campbell, 1998). Such patterns of behavior have been observed both in interpersonal and media contexts (Coover, 2001).
Within the present investigation, the campaign article may have been an important source of the normative cues that informed candidate evaluations. Although this article was written to be fairly general, ambiguous, and editorially neutral, it may have been rated as a fairly positive depiction of the candidate. As such, it may have prompted its
86 readers to treat the candidate, regardless of his race, in an egalitarian fashion (hence the nonsignificant coefficients for candidate race in most crime story conditions. In addition, the crime article may have further alerted readers to their own perceived bias, resulting in outgroup favoring responses in the no-race crime story condition of Study 1 and the
African American crime story condition of Study 2. The finding that the African
American candidate was favored in the no-race crime condition suggests that crime may have been processed as a race-coded issue. As research on crime and race indicates,
White respondents tend to associate crime with African Americans more than any other group (Gilliam, 1999; Oliver & Armstrong, 1998). Therefore, exposure to crime news stories may activate race-related cognitions regardless of whether or not the stories
portray African Americans as suspects or not. Indeed, in light of the content analytic research indicating that news messages overrepresent African Americans as crime
perpetrators (Dixon & Linz, 2000a, 2000b), such strong minority-crime association
among White consumers would not surprising.
Overall, this pattern of findings seems to fit with the aversive racism framework,
thus suggesting that the non-significant effect of candidate race and the outgroup-
favoring responses might have been the manifestations of conflicting identity concerns
(egalitarianism vs. favorable group identity), rather than expressions of genuine
preference for the minority candidate.
Racial identification. Whereas the expected three-way interaction did not emerge,
racial identification had an unexpected main effect on the prototypicality ratings in the
African American crime story and the no-race crime story condition. Specifically, racial
87 identification positively influenced the prototypicality ratings, regardless of a candidate’s race. This pattern of findings does not lend itself to a straightforward interpretation. Had the effect occurred in the African American crime story condition only, one might speculate about a contrast effect, where the political candidate (high status, “model” citizen) might have been viewed as highly discrepant from the socially undesirably Black criminal suspect, that the candidate would have received higher than otherwise expected
prototypicality ratings. Such an effect would be consistent with the priming literature’s argument that when a prime (the criminal suspect) is highly divergent in nature from a target stimulus (political candidate), exposure to both results in the target evaluation that is in contrast with the nature of the prime (positive) (Bless & Wänke, 2000). However, the significant effect of identification in the control (no crime story) condition does not fit within this explanation and is difficult to account for within the present investigation.
Perhaps an extraneous variable, such as authoritarianism (which has been linked to racial attitudes [Van Hiel & Mervielde, 2005]) produced the spurious correlation between identification and prototypicality ratings. On the other hand, this result might be pointing to an interesting and previously unexplored relationship between racial identification and leadership perceptions but more research is needed to explore this relationship. As it is, the significant relationship between stereotypical crime news, racial identification, and responses to the candidates is generally consistent with the idea that that race is an important variable predicting leader emergence.
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Prototypicality, expectations of policy performance, electoral support, and affect
The leadership and status characteristics literature in the organizational and
political realm suggest that a potential leader’s congruence with a prototype endorsed by the followers results in favorable performance expectations and follower support (Foti &
Lord, 1982; Maurer et al., 1993; Ridgeway, 2003). Based on this research, Hypothesis 2a
proposed that leadership prototypicality would significantly predict expectations of the candidates’ policy performance and electoral support. The relevant findings are summarized as follows.
Expectations of policy performance . In each study, an exploratory factor analysis yielded three factors of policy performance expectations. However, the factors differed
between the two studies (foreign issues, domestic issues, and lifestyle issues in Stud 1 and agenda issues, civil rights issues, and moral issues in Study 2). This discrepancy might be a function of differences in age, political sophistication between the two samples (although they did not differ significantly in terms of ideology and political interest). Nonetheless, since all factors in both studies were intuitively appealing, the data were analyzed accordingly.
In Study 1, prototypicality significantly predicted expectations of policy
performance for most factors and across most conditions. In Study 2, fewer relationships
between prototypicality and expectations of policy performance reached significance but all were in the predicted direction. In sum, the data from both studies indicated that the more leader-prototypical a candidate was perceived to be, the more positive were voters’
policy performance expectations relevant to that candidate.
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Electoral support . The electoral support variables included leadership impressions and vote likelihood. In Study 1, prototypicality was a significant, positive predictor of leadership impressions and vote likelihood across all crime story conditions. Consistent findings were obtained for competence prototypicality in Study 2. Integrity
prototypicality predicted these variables also but to a lesser extent (no effect was observed on leadership impression in African American crime condition and on vote likelihood in control condition). Overall, these results suggest that as a candidate’s
perceived leadership prototypicality increased, so did his electoral support.
In short, the findings from both studies lend support to Hypothesis 2a and are consistent with the existing literature on leader emergence, further validating the notion that leadership prototypicality is an important predictor of leader emergence in the electoral process.
Prototypicality and candidate affect . Little research, thus far, has focused on the relationship between leader-prototypicality and affect toward a leader (Lord & Brown,
2004). Considering that candidate liking is a noted correlate of vote decisions (Granberg
& Brown, 1989), it is important to expand our knowledge of the implications of
prototypicality judgments for this political variable. Consequently, the present study explored the relationship between prototypicality and leader-targeted affect (Research
Question 1). Three affect variables were included in the analysis. With respect to feelings thermometer, the data indicate a consistent positive relationship with prototypicality, except for the no-crime-story condition in Study 2, where the relationship between integrity prototypicality and thermometer ratings was non-significant. Furthermore, both
90 variables significantly and positively predicted positive affect, except for the African
American crime condition in Study 2, where integrity prototypicality coefficient failed to reach significance. Finally, in some conditions, prototypicality significantly and negatively influenced negative affect, though fewer significant relationships were observed overall than with the other dependent variables. In sum, the results in both studies indicate that the more prototypical a candidate was perceived to be, the more he was liked and the less he was disliked by the participants.
Relative strength of prototypicality predictors – policy performance expectations and electoral support . The race-related leadership literature suggests that not all leader characteristics are equally strong predictors of leader emergence. Traits that indicate competence and the capacity to perform the assigned tasks effectively are the key
predictors of electoral support (Sigelman et al., 1995; Williams, 1990). In line with this reasoning, Hypothesis 2b predicted that competence-related prototypicality would be a stronger predictor of a candidate’s support than prototypicality on compassion-related traits. However, in Study 1, a single prototypicality factor emerged, which prevented a test of this hypothesis. A direct test of Hypothesis 2b was also impossible in Study 2, where the prototypicality items formed a competence factor and an integrity factor but no compassion factor emerged. Nonetheless, an analysis was performed to test the relative strength of competence and integrity in predicting performance expectations and leadership support. The results suggested that in a number of instances, competence
predicted the dependent variables more strongly than did integrity. Most notably, this was the case with leadership impressions, where competence prototypicality was the stronger
91
predictors across all conditions. Competence also predicted vote likelihood more strongly in the White crime story condition and the no-crime story condition. There also was some evidence that competence may be a stronger predictor of policy performance expectations than is integrity. In turn, integrity ratings did not predict any outcome in any condition significantly more strongly than compassion ratings.
Relative strength of prototypicality predictors – candidate affect . No hypotheses had been offered regarding the relative strength of relationships between the two
prototypicality factors and leader affect since little evidence existed about the
prototypicality-affect relationship in extant literature. Nonetheless, the data in Study 2 allowed for a comparison of the effects of the two predictors on the affect variables.
These comparisons revealed that competence prototypicality was a stronger positive
predictor of the feelings thermometer in the White crime story and no-race crime story condition and a stronger predictor of positive affect in the no-race crime story and no- crime story condition. Again, integrity was not a significantly stronger predictor of any affect outcome variables.
Taken as a whole, this set of findings supports the notion that competence
prototypicality may be the central prototypicality factor predicting the emergence of
political leaders, which is consistent with the reasoning behind Hypothesis 2b and the more general literature indicating that competence-related traits may be key criteria in evaluating political candidates (Funk, 1999; Miller et al., 1986). On the other hand, these data diverge from prior literature pointing to compassion as an important dimension for distinguishing between White and African American political candidates
92
(Sigelman et al., 1995; Williams, 1990).Those studies, however, did not consider the
potential impact of the larger news context on candidate evaluations. Perhaps the exposure of the crime article, which implicitly dealt with issues of morality, changed the structure of the criteria used in candidate evaluations and increased the overall salience of integrity-related traits (Study 2).
The mediated model of news effects
The present data offered very limited support to the mediated model of news effects posited in this investigation. However, some evidence for mediation emerged in the no-race crime condition of Study 1, where candidate race had a significant effect on
prototypicality ratings and prototypicality predicted differential responses to the White and African American candidate in terms of expectations of policy performance, vote likelihood, and affect. Prototypicality also predicted differential responses to the no-race and African American candidate in terms of performance on lifestyle issues.
Notably, this set of findings diverges from the study’s predictions in two ways.
First, the direction of the relationship between the independent variables and the mediator was opposite of that hypothesized, such that the African American candidate received more favorable prototypicality ratings relative to the White and no-race candidate.
Second, the significant mediation relationship occurred in the no-race crime condition and not, as hypothesized, in the African American crime condition. In other words, the relationship held only among participants exposed to a crime story that did not identify the race of the suspect.
93
In Study 2, the mediation relationship did not emerge at all. Although candidate race did predict integrity prototypicality in the African American crime condition and there were significant relationships between candidate race and most dependent variables
(except for civil rights and moral issues), these relationships did not disappear or weaken significantly upon adding integrity prototypicality into the models. As such, Study 2 did not support the mediated model.
Direct effects of crime story and candidate race on the dependent variables
In light of the limited support for the mediated relationship between media stimuli, racial identification, and voters’ responses, a follow-up analysis examined the direct effects of crime story, candidate race and reader racial identification on candidate affect, expectations of policy performance, and electoral support. This analysis was motivated by the political campaign literature which suggests that race-related news stories may prime negative affect and policy expectations toward White candidates
(Valentino, 1997) and that candidate race may directly predict voters’ affective responses and electoral support (Terkildsen, 1993), as well as policy performance expectations
(Williams, 1990). The analysis revealed interesting and somewhat parallel patterns of findings in the present studies.
First, the crime story was found to interact with other independent variables in
predicting the dependent variables in both studies. In Study 1, a three-way interaction emerged between the crime suspect race, candidate race, and racial identification.
Specficalt, in the African American crime condition of Study 1, racial identification
positively predicted affect and electoral support for the White and no-race candidate (the
94 responses to the African American candidate as a function of strong racial identification were unfavorable but not significant). Moreover, in the African American crime condition of Study 2, the Black candidate received significantly higher thermometer ratings than the White candidate and the no-race candidate. Additionally, he received significantly higher ratings than the White candidate in the White crime condition (that difference was smaller, though).
Although no other significant interactions emerged between the crime story and the candidate race, quick inspection of Figures 2-14 additionally suggests a trend where the advantage of the African American candidate over the White and no-race candidate changed slightly yet systematically as a function of crime story. Specifically, it seems that in all crime story conditions, the African American candidate received higher scores than the other candidates on a number of dependent variables (thermometer ratings,
policy performance expectations, and vote likelihood in Study 1; positive and negative affect, civil rights issues, and vote likelihood in Study 2). However, his advantage appears to have diminished in the control condition (where the crime story was absent).
Of course, since these results were not significant, these trends must be approached with caution.
Overall, the heightened importance of race-related variables (racial identification and candidate race) as a function of the African American crime story suggests that the implicitly stereotypical news message may have increased the salience of race-related cognitions in evaluations of political candidates. Such pattern of findings fits within the existing priming literature, which demonstrated that stereotypical racial depictions in the
95 news content increase the weight of consumers’ racial perceptions on social and political
judgments (Power et al., 1996; Mendelberg, 1997; 2001).
Second, candidate race had significant main effects on the outcome variables in
both studies: Across all crime story levels of both studies, voters’ affective responses to the African American candidate were more positive than were the responses to the White and the no-race candidate. Additionally, in Study 2, the African American candidate was favored over the White and the no-race candidate in terms agenda issues performance and electoral support.
As was observed regarding the mediation analysis, most of these direct effects
present a pattern that is most consistent with the aversive racism perspective (Gaertner &
Dovidio, 1986). Exerting caution not to appear prejudiced, participants may have overcompensated for their perceived bias by expressing preference for the outgroup candidate. Even though some ingroup-favorable response did emerge as a function of racial identification in Study 1, participants nonetheless refrained from expressing much corresponding outgroup disinclination, which resulted in the non-significant coefficient for the African American candidate.
An additional explanation for the favorable responses to the African American candidates in the present study may be the contrast effect (Herr, 1986; Herr, Sherman, &
Fazio, 1983). After exposure to the crime story, readers may have seen the smiling, successful African American veteran in a suit and tie to stand in stark contrast to the fundamentally socially undesirable crime suspect (regardless of the suspect’s race) and may have genuinely responded to him more favorably as a result. The contrast effect may
96 have taken place in the control condition as well: Considering that such relatively
positive presentation of an African American target may be inconsistent with the day-to- day portrayals of Blacks in the news, it is conceivable that participants implicitly compared the candidate in the control condition to images derived from news content they had been exposed to in the past, which resulted in the favorable responses.
In both studies, the independent variables had few statistically significant effects on the negative affect and policy performance expectations. With regard to negative affect, this may have occurred because participants were unwilling to express negative emotions. Even though existing research on political opinion suggests that voters are quite keen on expressing unfavorable emotions toward political candidates (Marcus,
1984), participants may have been unwilling to express negative emotions toward the
African American candidate, so as not to appear biased. Alternatively, the items used in the negative affect measure (anger, fear) might not be been viewed by many respondents as accurate representations of their emotional reactions.
The inconsistent effects on the expectations of policy performance (along with the finding that the content of the policy factors varied across the studies) may have been a function of limited amount of candidate information available on which to form the
policy judgments. The candidate-related article was fairly general and did not explicitly discuss all of the policy issues addressed in the outcome measure. As Peterson (2005) found, the strength of association between perceptions of candidate traits and issue
positions may be a function of varying degrees of certainty regarding issues and candidate characteristics. As such, the low amount of information about the candidates
97 and their policy stances may have resulted in heightened uncertainty among participants, which yielded the inconsistent factor structure and connections between candidate race and policy performance expectations.
Throughout the analyses, the responses toward the African American candidate differed not only from those toward the White candidate but also from the responses to the no-race candidate and both sets of differences were in the same direction (the African
American candidate was favored both over the White and the no-race candidate and racial identification increased favorable responses to both the White candidate and the no-race candidate). This parallel pattern of differences may suggest that the participants may have assumed the politician whose racial identity was not disclosed, to be White.
Given the severe underrepresentation of African Americans and other minorities
(Latinos, Asian Americans, women) in the U.S. government, most Whites might cognitively store a schema (or even a prototype) of a political candidate as a White male.
The participants in this study, then, may have projected such a mental image onto the race-unidentified candidate and evaluated him consistent with the White – African
American differentiation patter.
Theoretical and substantive implications of the study
Taken together, the findings of this investigation lend only limited support to the
proposed mediated model of news effects. In particular, these results suggest that candidate race in interaction with crime news may predict African American candidates’
prototypicality ratings, which may consequently affect emotional responses, expectations of policy performance, and electoral support. However, this mediation relationship seems
98 to be independent of White perceivers’ racial identification. Moreover, it emerged in the
present study as a function of race-neutral, rather than racially stereotypical news. This
pattern of findings would be consistent with the notion that crime is perceived by news consumers as a race-coded issue (Gilliam & Iyengar, 2001), which means that crime coverage may prime racial cognitions without making explicit or implicit references to race. Substantively, this implies that candidate race may come to play in an election as a result of even more subtle news stimuli than was originally expected. However, since the
present study did not include experimental manipulations of crime as a race-coded issue, the above conclusion must be approached with caution and subsequent research should contain tests directly addressing such effects.
Importantly, contrary to initial expectations, being Black ostensibly advantages
African American candidates in terms of prototypicality ratings and other voter responses instead of contributing to these candidates’ political demise. If this were indeed the case, one might optimistically conclude that the White majority is undergoing a positive transformation in terms of its racial attitudes. Yet, the notion that Whites are becoming more cautious when expressing themselves about race-related issues is an equally
plausible account of these findings. If egalitarian decision-making was, indeed, increasing among the White citizens, one could expect African Americans’ electoral representation to be on the rise. Instead, the most recent General Election results show virtually no changes in the percentages of Members of Congress who are African
Americans or representing other ethnic minorities (CQ Weekly, November 13, 2006), even as the turnout among the youngest (and, presumably, most egalitarian) voters has
99 increased (Pew Charitable Trusts, November 9, 2006). Therefore, a promising theoretical framework which can be applied to explain the present findings and which could guide subsequent investigations of communication in mixed race elections is the aversive racism theory, which explains the outgroup-favoring responses (and the non-significant effects of candidate race) as arising from observance of egalitarian behavioral norms, rather than as reflections of true attitudes. This reasoning is particularly appealing given that relatively recent scholarship on mixed-race elections demonstrated that White voters do provide responses that favor White candidates over African American ones (Sigelman et al., 1995; Terkildsen, 1993). In light of this literature and other recent studies that document racial ingroup favoritism among White individuals as a function of news exposure (Mastro & Kopacz, 2006; Tan et al., 2000), it is unlikely that race-based
perceptions regarding political candidates have undergone as dramatic a change as the responses in this study would suggest. In turn, this interpretation would make it less likely that a contrast effect would explain the outgroup-favoring candidate evaluations as that effect would be a function of a genuine perceptual shift rather than a mere overt declaration of judgment (Bless & Wänke, 2000). However, an empirical test of the alternative theoretical accounts would be needed to determine if the contrast effect is indeed an unlikely account of the findings in question.
The present results also provide some evidence congruent with literature positing that racial ingroup identification may serve as a moderator of ingroup-favoring responses
(Oakes et al., 1994; Turner, 1999). Specifically, Study 1 imply that racial identification may interact with racially stereotypical crime news stories in increasing Whites’ liking of
100 and electoral support for White candidates (the converse, negative effect on responses to
African American candidates failed to reach significance, which would be consistent with respondents’ motivation to act in an egalitarian manner). As such, these findings also expand the application of social identity concepts to the mass-mediated context, demonstrating that identification-based ingroup favorability may occur beyond the realm of interpersonal communication.
Furthermore, this study carries implications for the implicit racial priming literature (Mendelberg, 1997; 2001; Valentino, 1999; 2001) by supporting the notion that racially implicit media messages (where race is manifested visually, rather than verbally) may raise the salience of race among media consumer. Moreover, these findings expand the implicit racial priming literature by demonstrating that the visual stimuli carry implications for White voters’ assessments of minority candidates and may, in consequence, affect these candidates’ electoral outcomes.
Notably, this investigation is one of few examinations to expand the LCT assumptions to the context of electoral leadership (Foti & Lord, 1983; Maurer et al.,
1993) and to apply it specifically toward explaining voter behavior toward ethnic minority leaders. In particular, these data illustrate that leadership prototypicality is an important consideration in voters’ decision-making. Moreover, the study provides evidence on the link between leader prototypicality and affective responses to leaders, which, to date, has received limited attention in the leadership literature (Lord & Brown,
2004). Given that affect has been considered an important predictor of electoral outcomes
(Glaser & Salovey, 1998; Sullivan, 1996), this evidence also carries valuable implications
101 the political communication research. Moreover, this is one of the few investigations to examine the applicability of LCT in the mass mediated context (Foti & Lord, 1982;
Maurer et al., 1993) and, possibly, the only study to date to apply experimental design in order to test media effects on leader prototypicality ratings in a controlled fashion. The results also provide a connection between priming and leader categorization, thus suggesting that future investigations can use these integrated frameworks in examining the relationships between news messages, candidate race, and electoral decision-making.
Finally, these findings indirectly inform our understanding of the implications of race for the U.S. electoral process. Fundamentally, we learn that race continues to affect
perceptions of political actors and that racialized media discourse may carry real implications for the ability of African American candidates to succeed politically. Given that White Americans may be becoming more cautious in expressing their racial attitudes, however, more refined research tools are needed to pinpoint the precise nature of these implications for minority politicians’ electoral success.
Limitations and Recommendations for Further Research
This investigation is also marked by a number of limitations. Chief among these is the nature of the samples used in both studies. In each case, a convenience sample was employed and neither of the samples was representative of the greater voter population.
Some researchers are especially quick to raise issue with the use of student participants in studies of voter behavior (e.g. Sears, 1986). This is motivated by the argument that college students tend to be politically unsophisticated and disinterested. Therefore, their responses to political stimuli used in research may not be reflective of those in the larger
102 society. Such educated individuals may have a stronger need to provide more racially egalitarian responses than does the general population. In addition, women, who have
been demonstrated do manifest lower levels of racial discrimination than men (Qualls,
1992), were overrepresented in the present sample. As such, this sample might have been more likely than the general population to display the aversive racism tendencies. Indeed, several recent studies employing more diverse samples of participants (e.g., Terkildsen,
1993; Valentino, 2001) found evidence of racial favoritism in candidate responses, inconsistent with the results obtained here. Moreover, the seemingly egalitarian responses
provided by the participants may have been manifestations of nonattitudes (Schuman &
Presser, 1980), that is, responses offered because participants felt compelled to respond, not because they had indeed formed candidate-relevant attitudes based on the brief articles. As such, these responses may not have been expressions of a true lack of race-
based favoritism but reflections of low political interest and insufficient exposure to the attitudinal targets. Finally, the size of the samples used in the present investigation was small relative to the number of conditions and relationships tested and many of the post- hoc power analyses revealed insufficient statistical power of the tests used. In light of these considerations, future investigations of this nature should be conducted with large, representative samples of U.S. citizens and should expand the amount of information about the candidates as the basis for attitude formation.
The nature of stimulus presentation in this study may also have affected how
participants responded. First, the articles were not presented in any larger news context, such as a webpage or a newspaper page. This may have sensitized the readers to the fact
103 that race was a key variable in the study and may have alerted them to the overall study
purpose. Therefore, presenting the crime and political messages embedded in a larger news segment may be a more promising approach. Second, although the intention of this investigation was to present the candidates in relatively ambiguous terms, the relatively
positive candidate qualifications and the inspirational nature of his speech presented within the article may have imparted egalitarian norms of behavior to the White readers.
Future studies could examine the aversive racism hypothesis by manipulate the candidate qualifications and/or article tone, in addition to race, as suggested by the findings of aversive racism studies (Aberson & Ettlin, 2004; Murphy-Berman et al., 1998). As such, the candidate could be presented as qualified/in favorable light, unqualified/in unfavorable light, and in ambiguous terms. If the aversive racism hypothesis holds,
African American candidates with the ambiguous credentials should receive the least favorable responses, especially as a function of exposure to a racially stereotypical crime prime. In addition, future investigations should implement manipulation checks to ensure that targets’ race and the nature of their portrayals is perceived by study participants as intended by the investigator. Besides confirming that the experimental manipulation was
perceived by the participants as intended, the implementation of such checks in the
present study may have provided important insight into the unanticipated pattern of findings obtained here.
Second, voting was measured as participants’ self-reported behavioral intention and political research shows that voters tend to misreport if they voted/will vote
(Sigelman, 1982) and how they voted/will vote (Wright, 1990). Therefore, a more
104 rigorous approach might be to stage mock elections (see e.g. Rosenberg, Bohan,
McCafferty, & Harris, 1986), where participants would actually cast ballots for the candidates. Each participant’s ballot would unobtrusively be linked to the remainder of the instrument by means of a serial number, so that the vote could be verified against other responses. Even though the election would not be real, such a procedure should carry a greater level of realism and, therefore, might yield slightly different outcomes than merely asking participants to state their behavioral intentions. Of note, this approach would entail including more than one candidate in each condition of the induction materials.
Future research should explore employing alternatives to the explicit measure of racial identification, given the possibility that participants in the present study may have
been motivated to act in an egalitarian fashion. A promising option might be to employ an implicit test of racial attitudes, which examines racial outgroup discrimination by measuring the strength of association between pairs of racial stimuli and words of
positive and negative valence (Greenwald, Banaji, Rudman, Farnham, Nosek, & Mellott,
2002).
Further research should be conducted to provide less ambiguous evidence in support of or against crime as a race-coded issue. A noted earlier, some studies found that stories about issues like crime and welfare may prime racial stereotyping regardless of the race of these stories’ protagonists (Gilens, 1996; 1999; Gilliam, 1999; Valentino,
1997). The crime story manipulations in this study were limited to three experimental conditions (African American suspect, White suspect, no-race suspect) and the control
105 condition (no crime story). However, a more precise test of the racialized issue priming hypothesis would be to include a race-neutral issue story (e.g. about disaster relief or environment) and manipulate protagonists’ race within. Because such expansion of the experimental manipulations would require a considerable increase in the sample size, it was not incorporated in the present design. Nonetheless, future investigations should
pursue this avenue to explore the extent to which crime functions as a race-coded issue in affecting Whites’ responses to minority candidates.
Finally, many relationships here were based on correlation data. Most notably, the findings about the links from prototypicality to electoral support, affect, and policy
performance expectations cannot be treated as strong evidence of causality. That is especially so as many scholars argue that trait- or issue-based evaluations of candidates are merely post-hoc rationalizations of affective responses or prior attitudes (McGraw,
Fischle, Stenner, & Lodge, 1996; Rahn, Krosnick, & Breuning, 1994). On the other hand,
Kilburn (2005) provides evidence that candidate trait evaluations do influence overall candidate assessments (including affective responses) and electoral support, even though a degree of reverse causation has also been observed in those relationships. Considering these implications, separate investigation should be devoted to expand the understanding of the relative strength of influence between prototypicality and electoral support variables.
In spite of these limitations, this investigation demonstrates that race continues to serve as an important variable in the political context and that media messages can influence consumers’ responses to political actors. Therefore, further research efforts are
106 essential to expand our understanding of the mass communication implications for race-
based decision making in the political context. In addition, researchers should go beyond the traditional self-report studies in order to capture the subtleties of modern racial attitudes in the U.S. public. As the ethnic diversity of the U.S. society is continues to climb, the questions of race and ethnicity will increasingly be raised in the political discourse and communication scholarship must respond accordingly.
107
APPENDIX A
Stage 1 Instrument (Study 1)
INSTRUCTIONS: Please think of African Americans/Blacks in the United States today. Circle a number between each of the adjectives below to indicate how you would describe African Americans/Blacks in general. For example, if you thought GROUP X is “quite rich” in general, you would circle number 2 between “rich” and “poor” as shown below.
EXAMPLE:
rich 1 2 3 4 5 6 7 poor Very rich Quite rich Somewhat Neither Somewhat Quite Very rich rich nor poor poor poor poor
rich 1 2 3 4 5 6 7 poor lazy 1 2 3 4 5 6 7 hard working not 1 2 3 4 5 6 7 violence violence prone prone not 1 2 3 4 5 6 7 intelligent intelligent prefer to 1 2 3 4 5 6 7 prefer to be self- live off supporting welfare unpatriotic 1 2 3 4 5 6 7 patriotic not likely 1 2 3 4 5 6 7 likely to to commit commit crimes crimes can be 1 2 3 4 5 6 7 not to be trusted trusted likely to 1 2 3 4 5 6 7 not likely use drugs to use drugs
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likely to 1 2 3 4 5 6 7 not likely have to have strong strong family family ties ties not likely 1 2 3 4 5 6 7 likely to to deal deal drugs drugs educated 1 2 3 4 5 6 7 uneducated not 1 2 3 4 5 6 7 tolerant tolerant of other of other racial racial groups groups not likely 1 2 3 4 5 6 7 likely to to abuse abuse alcohol alcohol
INSTRUCTIONS: Now, please think of Caucasians/Whites in the United States today. Circle a number between each of the adjectives below to indicate how you would describe Caucasians/Whites in general. For example, if you thought GROUP X is “quite rich” in general, you would circle number 2 between “rich” and “poor” as shown below.
EXAMPLE:
rich 1 2 3 4 5 6 7 poor Very rich Quite rich Somewhat Neither Somewhat Quite Very rich rich nor poor poor poor poor
rich 1 2 3 4 5 6 7 poor lazy 1 2 3 4 5 6 7 hard working not 1 2 3 4 5 6 7 violence violence prone prone
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not 1 2 3 4 5 6 7 intelligent intelligent prefer to 1 2 3 4 5 6 7 prefer to be self- live off supporting welfare unpatriotic 1 2 3 4 5 6 7 patriotic not likely 1 2 3 4 5 6 7 likely to to commit commit crimes crimes can be 1 2 3 4 5 6 7 not to be trusted trusted likely to 1 2 3 4 5 6 7 not likely use drugs to use drugs likely to 1 2 3 4 5 6 7 not likely have to have strong strong family family ties ties not likely 1 2 3 4 5 6 7 likely to to deal deal drugs drugs educated 1 2 3 4 5 6 7 uneducated not 1 2 3 4 5 6 7 tolerant tolerant of other of other racial racial groups groups not likely 1 2 3 4 5 6 7 likely to to abuse abuse alcohol alcohol
110
INSTRUCTIONS: Now, please answer the following questions about your media consumption. Please complete the blank with the number that best represents your response.
How many hours of TV do you watch in a week? ______Hours
How many times per week do you watch news on television? ______Times
How many times per week do you read a newspaper (other than the student newspaper)? ______Times
How many times per week do you read news on internet websites? ______Times
How many times per week do you listen to news on the radio (other than the student radio)? ______Times
INSTRUCTIONS: Now, I would like to learn about your perceptions of media coverage. For each question, please circle the number that best represents your response.
In general, do you think TV presents life realistically?
Very Not at all realistically realistically 1 2 3 4 5 6 7
In general, do you think TV news presents events realistically?
Very Not at all realistically realistically 1 2 3 4 5 6 7
In general, do you think newspapers present events realistically?
Very Not at all realistically realistically 1 2 3 4 5 6 7
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In general, do you think internet news presents events realistically?
Very Not at all realistically realistically 1 2 3 4 5 6 7
In general, do you think radio news presents events realistically?
Very Not at all realistically realistically 1 2 3 4 5 6 7
INSTRUCTIONS: Please answer the following questions about your perceptions of your racial group. For each question, please circle the number that best represents your response.
Compared to the other characteristics which define you, how much do you value your race/ethnicity? Very much Not at all 1 2 3 4 5 6 7
How much do you like being defined by your race/ethnicity?
Very much Not at all 1 2 3 4 5 6 7
On what type of friendship basis are you with others of your race/ethnicity?
Good Not good friendship friend 1 2 3 4 5 6 7
How strong a sense of belonging do you have with your race/ethnicity?
Very Not at all strong strong 1 2 3 4 5 6 7
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How closely knit are you with others of your race/ethnicity?
Not at all Very close close 1 2 3 4 5 6 7
How much pride do you take in your race/ethnicity?
Very much Not proud pride at all 1 2 3 4 5 6 7
How many people of your race do you feel fit your ideal for your race/ethnicity?
All None 1 2 3 4 5 6 7
I do not enjoy being categorized by my race/ethnicity.
Enjoy Do not strongly enjoy at all 1 2 3 4 5 6 7
I feel involved with others of my race/ethnicity.
Very Not at all involved involved 1 2 3 4 5 6 7
If I could change my race/ethnicity, I would.
Agree Disagree strongly strongly 1 2 3 4 5 6 7
I feel included by others of my race/ethnicity.
Very Not at all included included 1 2 3 4 5 6 7
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I feel distant from others with my race/ethnicity.
Very Not at all distant distant 1 2 3 4 5 6 7
Some of my best friends are of my race/ethnicity.
Agree Disagree strongly strongly 1 2 3 4 5 6 7
If I were asked to participate in a group project, I would like to work with individuals of my race/ethnicity.
Agree Disagree strongly strongly 1 2 3 4 5 6 7
INSTRUCTIONS: Finally, please provide us with the following information about yourself:
Sex (please circle): Male Female
Year in school (please circle):
Freshman Sophomore Junior Senior Graduate Student
What is your racial or ethnic background:
Asian African Latino Native White/ Other (specify): American American American Caucasian ______
Your age: ______
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APPENDIX B
Stage 2 Experimental Materials (Studies 1 & 2)
Crime Sstory with African American Suspect
115
Crime Story with White Suspect
116
Crime Story with No-race Suspect
117
Campaign Story with African American Candidate
118
Campaign Story with White Candidate
119
Campaign Story with No-race Candidate
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APPENDIX C
Stage 2 Instrument (Studies 1 & 2)
INSTRUCTIONS: Please answer the following questions about the preceding article. For each question, please circle the number that best represents your response.
1. The article was easy to understand. disagree agree strongly strongly 1 2 3 4 5
2. The article was enjoyable. disagree agree strongly strongly 1 2 3 4 5
3. I was bored by the article. disagree agree strongly strongly 1 2 3 4 5
6. Based on the article, would you say that the suspect is (circle): Guilty Not Guilty
7. If you answered ‘Not Guilty’ in question no. 6, please skip to question no. 10. If you answered ‘Guilty’, please state the extent to which you agree or disagree with the following statement:
I think the suspect would repeat this behavior
disagree agree strongly strongly 1 2 3 4 5
8. If guilty, should this person go to prison? Yes No
9. If you answered ‘no’ to question no. 8, please skip to question no. 10. If you answered ‘yes’, please answer the following question:
How long (in months or years) should this person spend in prison? _____ Years _____ Months
121
10. Other people reading this article would say that the suspect is: Guilty Not Guilty
11. If you answered ‘Not Guilty’ to question no. 10, please move on to the next page. If you answered ‘Guilty’, please state the extent to which you agree or disagree with the following statement:
Other people watching this clip would say the suspect would repeat this behavior
disagree agree strongly strongly 1 2 3 4 5
12. Would other people reading this article say that this person should go to prison? No Yes
13. If yes, how long (in months or years) should this person spend in prison? _____ Years _____ Months
INSTRUCTIONS: Please think about Roger Steele described in the preceding article. How strongly does each of these qualities apply to Roger Steele? For each quality, please circle the number that best represents your response.
Courageous Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Patriotic Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Charismatic Does not Applies very apply strongly at all 1 2 3 4 5 6 7
122
Intelligent Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Trustworthy Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Fair Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Humanitarian Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Likable Does not Applies very apply strongly at all 1 2 3 4 5 6 7 Aggressive Does not Applies very apply strongly at all 1 2 3 4 5 6 7
High moral principles Does not Applies very apply strongly at all 1 2 3 4 5 6 7
123
Authoritarian Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Gentle Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Religious Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Tough Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Educated Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Optimistic Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Manipulative Does not Applies very apply strongly at all 1 2 3 4 5 6 7
124
Good verbal skills Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Honest Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Decisive Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Dishonest Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Open-minded Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Violent Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Good administrator Does not Applies very apply strongly at all 1 2 3 4 5 6 7
125
Sensitive Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Experienced Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Persuasive Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Easy-going Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Good judgment in a crisis Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Understanding Does not Applies very apply strongly at all 1 2 3 4 5 6 7
126
INSTRUCTIONS: If Roger Steele is elected, how likely is he to do each of the following (please circle the number that best represents your response)
Help reducing international terrorism Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help maintain a strong and well-prepared military Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help resolving the conflict in Iraq Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help maintaining good relations with other countries Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help controlling illegal immigration Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help stimulating the economy Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help keeping government spending under control Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help protecting the environment Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help improving the quality of education
127
Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help improving the Social Security system Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help lowering the costs of healthcare to all Americans Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help ending discrimination against minority groups Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help reducing unemployment Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help reducing poverty Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help reducing the crime rate Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help reducing illegal drug use Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help championing moral values in the nation Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
128
Help handling abortion issues Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help reducing corruption Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help supporting scientific research Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help ending discrimination against gays Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help supporting women’s rights Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help promoting strong family values Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help reducing energy prices Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
Help providing effective relief to victims of natural disasters Definitely Definitely wouldn’t would 1 2 3 4 5 6 7
129
On a scale of 0-100, how favorable/unfavorable do you feel toward Roger Steele? Zero means that you feel very unfavorable and you don’t care too much for Roger Steele. 100 means that you feel favorable and warm toward Roger Steele. Fifty means that you don't feel particularly warm or cold toward Roger Steele. Please write the number between 0 and 100 that best represents your feelings: ______
INSTRUCTIONS: Please think about Roger Steele. How strongly do each of these statements apply to you?
I like Roger Steele. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
I respect Roger Steele. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
I admire Roger Steele. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Roger Steele makes me angry. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Roger Steele makes me hopeful. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Roger Steele makes me afraid. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
130
Roger Steele makes me proud. Does not Applies very apply strongly at all 1 2 3 4 5 6 7
Roger Steele inspires me Does not Applies very apply strongly at all 1 2 3 4 5 6 7
INSTRUCTIONS: Please think about Roger Steele and answer the following questions.
How much leadership does Roger Steele exhibit? No Very much leadership leadership at all 1 2 3 4 5 6 7
How willing would you be to vote for Roger Steele? Completely Very willing unwilling 1 2 3 4 5 6 7
How typical is Roger Steele of an effective leader? Very Very typical atypical 1 2 3 4 5 6 7
To what extent did Roger Steele engage in leadership behavior? Does not Engages a engage at great deal all 1 2 3 4 5 6 7
How well does Roger Steele fit your image of an effective leader? Does not fit Fits very at all strongly 1 2 3 4 5 6 7
131
If the election were held today, how likely would you be to vote for Roger Steele? Please circle one number that best represents your response
Extremely Extremely unlikely likely 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
INSTRUCTIONS: Most American voters have an idea about the type of political leader that they would like to represent them in the United States Senate. Please think about the type of person that you think would be an effective Senator. Now please rate the extent to which each of these characteristics fits your image of an effective Senator.
Courageous Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Patriotic Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Charismatic Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Intelligent Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Trustworthy Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
132
Fair Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Humanitarian Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Likable Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Aggressive Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
High moral principles Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Authoritarian Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Gentle Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
133
Religious Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Tough Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Educated Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Optimistic Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Manipulative Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Good verbal skills Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Honest Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
134
Decisive Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Dishonest Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Open-minded Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Violent Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Good administrator Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Sensitive Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Experienced Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
135
Persuasive Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Easy-going Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Good judgment in a crisis Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
Understanding Does not fit Fits my my image image very at all well 1 2 3 4 5 6 7
INSTRUCTIONS: Please answer the following questions about your political interest. For each question, please circle the number that best represents your response.
How interested are you in politics? Very Not at all interested interested 1 2 3 4 5 6 7
How interested are you in political elections? Very Not at all interested interested 1 2 3 4 5 6 7
136
In terms of your political views, where would you place yourself on the following scale? (please circle one number that best represents your response)
extremely liberal slightly moderate slightly conservative extremely liberal liberal conservative conservative 1 2 3 4 5 6 7
Are you a U.S. citizen? ______Yes ______No
137
APPENDIX D
Tables and figures
Figure 1
Conceptual Model of Media-Based Leadership Emergence
Candidate Liking Crime Story Stereotypicality x Candidate Race Prototypicality Electoral x Perceptions Support Racial Ingroup Identification Expectations of Policy Performance
138
Table 1
Exploratory factor analysis of prototypicality (Study 1)
Items Factor loadings
Intelligent .71
Trustworthy .90
Fair .87
Humanitarian .67
Strong moral principles .79
Honest .72
% variance explained 66.86
139
Table 2
Exploratory factor analysis of expectations of policy performance (Study 1)
Items Factor 1 Factor 2 Factor 3
Terrorism .74 .16 .26
Military .79 .24 .20
Iraq .87 .21 .19
International relations .72 .33 .31
Economy .36 .65 .12
Spending .30 .72 .18
Environment .14 .55 .38
Education .10 .55 .48
Social security .30 .59 .25
Unemployment .13 .67 .30
Poverty .14 .68 .43
140
Table 2 - continued
Items Factor 1 Factor 2 Factor 3
Discrimination .17 .36 .63
Drug .24 .40 .60
Abortion .23 .16 .74
Corruption .31 .30 .65
Research .23 .27 .69
Gays .19 .20 .84
Women .19 .28 .77
% variance explained 50.30 10.25 8.10
Table 3
Exploratory factor analysis: Leadership impressions (Study 1)
Items Factor 1
Amount of leadership exhibited by candidate .82
Willingness to vote for candidate .79
How typical candidate is of effective leader .72
Extent to which candidate engages in leadership behavior .84
Candidate fit with the image of effective leader .86
% variance explained 71.84
141
Table 4
Exploratory factor analysis of candidate affect (Study 1)
Items Factor 1 Factor 2
I like Roger Steele .80 -.20
I respect Roger Steele .76 -.27
I admire Roger Steele .84 -.10
Roger Steele makes me hopeful .85 -.01
Roger Steele makes me proud .73 .06
Roger Steele inspires me .82 -.01
Roger Steele makes me angry -.18 .79
Roger Steele makes me afraid .03 .81
% variance explained 55.48 20.61
142
Table 5
Descriptive Statistics: Means and Standard Deviations for Prototypicality and Dependent Variables across Levels of Crime
Story and Candidate Race (Study 1)
Dependent Crime story Candidate Race Variable
African American White No-race
M SD M SD M SD
Prototypicality African American 4.41 .96 4.38 1.21 4.30 .74
White 4.13 .86 4.25 .96 4.33 1.04
No-race 4.51 .82 3.72 .85 3.83 1.23
No prime 4.52 1.10 4.37 .81 4.51 .85
142
143
Table 5 - continued
Dependent Crime story Candidate Race Variable
African American White No-race
M SD M SD M SD
Feelings therm. African American 67.69 20.39 61.87 21.40 60.33 24.81
White 63.64 13.16 52.37 21.32 64.40 15.89
No-race 64.65 17.33 52.62 14.11 64.40 16.00
No prime 62.81 19.83 64.95 14.88 64.77 18.97
Positive affect African American 4.44 1.35 3.59 1.48 3.83 1.35
White 3.97 1.09 3.13 1.29 3.53 1.59
No-race 4.08 1.14 3.27 1.02 3.81 1.50
No prime 4.14 1.33 3.34 1.02 3.88 1.19 143
144
Table 5 – continued
Dependent Crime story Candidate Race Variable
African American White No-race
M SD M SD M SD
Negative affect African American 1.73 1.26 1.64 .99 2.14 1.27
White 1.67 .92 1.83 1.10 1.60 1.00
No-race 2.31 1.13 2.40 1.10 2.05 .92
No prime 1.69 1.04 1.62 .89 1.71 .87
Leadership impr. African American 5.00 1.17 4.70 1.17 4.79 1.05
White 4.89 1.27 4.41 1.09 4.53 1.42
No-race 4.73 .86 4.38 .77 4.81 1.20
No prime 4.84 1.25 4.51 .97 4.89 .99 144
145
Table 5 - continued
Dependent Crime story Candidate Race Variable
African American White No-race
M SD M SD M SD
Foreign issues African American 3.98 1.40 3.44 1.41 3.85 1.44
White 4.33 1.20 3.34 1.33 4.05 1.09
No-race 4.51 1.04 3.82 1.23 4.14 1.18
No prime 4.04 1.23 4.04 1.19 3.98 1.42
Domestic issues African American 4.40 1.13 3.96 1.04 4.11 .85
White 4.36 1.00 3.88 1.01 4.30 1.02
No-race 4.83 1.13 4.12 .81 4.28 1.13
No prime 4.27 1.21 4.40 1.01 4.21 1.21 145
146
Table 5 - continued
Dependent Crime story Candidate Race Variable
African American White No-race
M SD M SD M SD
Lifestyle issues African American 3.82 .95 3.31 1.11 3.45 1.24
White 3.70 1.06 2.91 .93 3.64 .85
No-race 4.04 1.19 2.97 .84 3.34 1.27
No prime 3.61 1.32 3.47 1.33 3.10 1.38
Vote likelihood African American 61.83 22.84 55.65 22.28 59.44 23.63
White 59.04 21.96 53.89 20.25 56.00 21.56
No-race 63.00 17.43 49.78 19.97 58.18 20.68
No prime 55.83 21.84 55.24 21.59 61.35 20.62 146
147
Table 6
The effect of candidate race on prototypicality across prime conditions (Study 1)
Crime story condition Independent Variables Prototypicality
B SE B
White candidate -.12 .26
No-race candidate -.18 .30
African American suspect Racial identification .19* .08
Racial ident. x White candidate .24 .19
Racial ident. x no-race candidate .21 .21
F 1.47 (5, 73), p = .21
Power 1.00
White candidate .16 .26
No-race candidate .22 .30
White suspect Racial identification .19 .10
Racial ident. x White candidate -.17 .24
Racial ident. x no-race candidate -.06 .29
F 8.68 (5, 62), p = .51
Power 1.00
*p < .05; B – unstandardized regression coefficient; SE B – standard error
148
Table 6 - continued
White candidate -.80* .30
No-race candidate -.69* .31
No-race suspect Racial identification -.01 .09
Racial ident. x White candidate .09 .23
Racial ident. x no-race candidate .14 .22
F 1.68 (5, 60), p = .15
Power 1.00
White candidate -.02 .28
No-race candidate .10 .27
No prime Racial identification .23* .09
Racial ident. x White candidate -.31 .28
Racial ident. x no-race candidate -.12 .27
F 1.70 (5, 59), p = .15
Power 1.00
*p < .05; B – unstandardized regression coefficient; SE B – standard error
149
Table 7.1
The effect of candidate race on dependent variables in African American suspect article condition (Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
-7.19 -.94* -.02 -.33 White candidate (5.69) (.36) (.30) (.30)
Race-unid. -8.43 -.68 .46 -.24 Candidate (6.56) (.42) (.35) (.34)
2.56 .18 -.14 .07 Racial identification (1.81) (.11) (.10) (.10)
Racial ident. x White 12.86** .84** -.48* .57** candidate (3.91) (.24) (.21) (.21)
Racial ident. x race- 11.13* .76** -.63** .47* unid. candidate (4.3) (.27) (.23) (.23)
3.36 4.83 2.72 2.04 (5,72) (5,73) (5,73) (5,73) F p<.01 p=.001 p<.05 p=.08
Power .76 .76 .78 .76
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
150
Table 7.1 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.58 -.49 -.62* -7.51 White candidate (.37) (.27) (.27) (5.86)
Race-unid. -.16 -.33 -.45 -3.39 Candidate (.43) (.31) (.31) (6.78)
.06 .12 .23* 2.78 Racial identification (.12) (.08) (.09) (1.86)
Racial ident. x White .18 .30 .21 11.82** candidate (.27) (.20) (.20) (4.08)
Racial ident. x race-unid. .54 .33 .20 11.08* candidate (.30) (.22) (.22) (4.51)
1.20 1.64 2.41 2.84 (5,73) (5,73) (5,73) (5,73) F p=.32 p=.16 p<.05 p<.05
Power .84 .79 .74 .73
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
151
Table 7.2
The effect of candidate race on dependent variables in White suspect article condition
(Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
-10.97* -.79* .12 -.45 White candidate (4.86) (.25) (.28) (.34)
Race-unid. .81 -.42 -.09 -.34 Candidate (5.71) (.35) (.33) (.40)
1.85 .20 -.16 .16 Racial identification (1.97) (.14) (.11) (.14)
Racial ident. x White 2.59 .16 -.10 .07 candidate (4.66) (.33) (.26) (.32)
Racial ident. x race-unid. -.63 .57 -.02 .40 candidate (5.66) (.40) (.32) (.39)
1.66 1.92 .54 .93 (5,61) (5,62) (5,62) (5,62) F p=.16 p=.10 p=.75 p=.47
Power .80 .76 .89 .85
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
152
Table 7.2 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.95** -.47 -.75** -4.60 White candidate (.34) (.28) (.26) (5.85)
Race-unid. -.26 -.05 -.05 -2.82 Candidate (.40) (.33) (.31) (6.88)
.13 .09 .17 2.41 Racial identification (.14) (.11) (.11) (2.35)
Racial ident. x White -.37 -.22 .16 6.01 candidate (.31) (.25) (.25) (5.43)
Racial ident. x race-unid. -.43 .46 .22 3.12 candidate (.39) (.31) (.30) (6.66)
2.30 1.99 2.69 .61 F (5,62) (5,62) (5,62) (5,62)
p=.06 p=.09 p<.05 p=.69
Power .79 .78 .77 .89
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
153
Table 7.3
The effect of candidate race on dependent variables in no-race suspect article condition
(Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
-11.96* -.90* -.10 -.35 White candidate (5.06) (.37) (.32) (.30)
Race-unid. .75 -.34 -.29 .08 Candidate (5.09) (.38) (.33) (.30)
.14 -.18 -.05 .00 Racial identification (1.54) (.11) (.10) (.09)
Racial ident. x White 2.89 .32 -.66** .36 candidate (3.97) (.28) (.24) (.22)
Racial ident. x race-unid. 2.76 .19 -.23 .30 candidate (3.60) (.26) (.22) (.21)
1.78 1.77 1.81 1.13 (5,55) (5,60) (5,60) (5,60) F p=.13 p=.13 p=.12 p=.35
Power .79 .79 .78 .84
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
154
Table 7.3 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.78* -.67* -1.10** -13.40* White candidate (.36) (.32) (.34) (5.90)
Race-unid. -.46 -.51 -.73* -4.99 Candidate (.36) (.32) (.34) (5.95)
-.13 .07 -.05 -.29 Racial identification (.11) (.10) (.10) (1.81)
Racial ident. x White .05 .07 -.03 7.62 candidate (.29) (.24) (.26) (4.60)
Racial ident. x race-unid. .14 .39 -.05 4.68 candidate (.27) (.22) (.24) (4.29)
1.09 1.90 2.06 1.67 (5,59) (5,60) (5,60) (5,59) F p=.38 p=.11 p=.08 p=.16
Power .82 .80 .78 .79
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
155
Table 7.4
The effect of candidate race on dependent variables in no-crime-article condition (Study
1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
1.94 -.84* -.11 -.36 White candidate (6.07) (.28) (.30) (.45)
Race-unid. 1.77 -.30 -.01 .02 Candidate (5.81) (.39) (.29) (.33)
-.41 -.08 -.06 -.06 Racial identification (1.85) (.12) (.10) (.11)
Racial ident. x White -.55 -.59 .11 -.43 candidate (6.14) (.38) (.30) (.35)
Racial ident. x race-unid. -2.34 -.59 -.17 -.34 candidate (5.75) (.36) (.28) (.33)
.10 1.65 .44 .69 (5,57) (5,59) (5,59) (5,59) F p=.99 p=.16 p=.82 p=.64
Power .98 .79 .92 .89
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
156
Table 7.4 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.02 .12 -.13 .20 White candidate (.43) (.38) (.44) (6.97)
Race-unid. -.07 -.08 -.50 6.27 Candidate (.41) (.36) (.42) (6.65)
-.02 -.03 .03 1.48 Racial identification (.13) (.12) (.14) (2.17)
Racial ident. x White -.18 -.22 .10 -2.62 candidate (.43) (.38) (.45) (7.03)
Racial ident. x race-unid. -.13 -.26 .00 -4.13 candidate (.40) (.36) (.42) (6.56)
.05 .18 .36 .40 (5,59) (5,59) (5,59) (5,59) F p=1 p=.97 p=.87 p=.85
Power 1 .97 .93 .92
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
157
Table 8.1
The effect of independent variables and prototypicality on dependent variables in African
American suspect crime condition (Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
-6.40 -.87* -.08 -.29 White candidate (5.30) (.33) (.28) (.29)
Race-unid. -7.12 -.58 .38 -.18 Candidate (6.12) (.39) (.32) (.33)
1.04 .08 -.05 .00 Racial identification (1.74) (.11) (.09) (.10)
Racial ident. x White 11.00** .73** -.38 .51* candidate (3.74) (.23) (.20) (.21)
Racial ident. x no-race 9.50* .67* -.55* .41 candidate (4.10) (.26) (.22) (.23)
8.28** .54*** -.44** .33* Prototypicality (2.34) (.15) (.12) (.13)
4.77 6.41 4.24 2.64 F (6,71) (6,72) (6,72) (6,72) p<.0001 p<.001 p=.001 p<.05
Power .63 .86 .75 .77
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
158
Table 8.1 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.52 -.44 -.57* -6.68 White candidate (.35) (.25) (.26) (5.63)
Race-unid. -.07 -.25 -.39 -2.14 Candidate (.40) (.29) (.30) (6.51)
-.04 .04 -.16 1.46 Racial identification (.12) (.08) (.08) (1.85)
Racial ident. x White .07 .21 .14 10.43* candidate (.26) (.18) (.19) (4.00)
Racial ident. x no-race .44 .25 (.13) 9.87* candidate (.28) (.20) .21 (4.40)
.48** .42*** .34** 6.92** Prototypicality (.16) (.11) (.12) (2.50)
2.55 3.64 2.62 3.46 F (6,72) (6,72) (6,61) (6,72) p<.05 p<.01 p<.05 p<.01
Power .81 .74 .64 .76
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
159
Table 8.2
The effect of independent variables and prototypicality on dependent variables in White
suspect crime condition (Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
-11.84* -.92** .11 -.54 White candidate (4.68) (.30) (.28) (.31)
Race-unid. -.405 -.59 -.11 -.46 Candidate (5.50) (.35) (.33) (.37)
.745 .78 -.18 .06 Racial identification (1.94) (.14) (.12) (.13)
Racial ident. x White 3.69 .29 -.08 .17 candidate (4.47) (.27) (.26) (.29)
Racial ident. x no-race -.16 .62 -.01 .44 candidate (5.41) (.33) (.32) (.35)
5.73* .78*** .12 .55** Prototypicality (2.26) (.14) (.14) (.15)
2.63 7.35 .56 3.12 F (6,60) (6,61) (6,61) (6,61) p<.05 p<.001 p=.76 p=.01
Power .77 .92 .91 .80
*p<.05; ** p < .01; *** p<.001. Standard errors are in parentheses.
160
Table 8.2 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-1.00 -.51 -.79** -5.73 White candidate (.34) (.27) (.26) (5.61)
Race-unid. -.33 -.12 -.10 -4.38 Candidate (.40) (.32) (.30) (6.60)
.08 .03 .12 1.03 Racial identification (.14) (.11) (.11) (2.31)
Racial ident. x White -.32 -.17 .20 7.28 candidate (.31) (.24) (.24) (5.18)
Racial ident. x no-race -.41 .48 .24 3.57 candidate (.38) (.30) (.29) (6.34)
.30 .31* .26* 7.23* Prototypicality (.16) (.13) (.13) (2.74)
2.46 3.64 3.08 1.83 F (6,61) (6,72) (6,61) (6,61) p<.05 p<.01 p<.05 p=.11
Power .80 .83 .77 .81
*p<.05; ** p < .01; *** p<.001. Standard errors are in parentheses.
161
Table 8.3
The effect of independent variables and prototypicality on dependent variables in no-race
suspect crime condition (Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
-6.40 -.44 -.12 -.01 White candidate (4.79) (.35) (.34) (.28)
Race-unid. 6.45 .06 -.30 .37 Candidate (4.83) (.35) (.34) (.28)
.01 -.17 -.05 .00 Racial identification (1.39) (.10) (.10) (.08)
Racial ident. x White 1.84 .27 -.66** .32 candidate (3.60) (.26) (.24) (.20)
Racial ident. x no-race 2.59 .11 -.23 .24 candidate (3.25) (.24) (.24) (.19)
7.37*** .58*** -.02 .42*** Prototypicality (1.96) (.14) (.14) (.11)
4.07 4.58 1.49 3.38 F (6,54) (5.47) (6,59) (6,59) p<.01 p=.001 p=.20 p<.01
Power .88 .58 .83 .84
*p<.05; ** p < .01; *** p<.001. Standard errors are in parentheses.
162
Table 8.3 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.34 -.30 -.79* -8.78 White candidate (35) (.30) (.34) (6.07)
Race-unid. -.07 -.20 -.46 -.96 Candidate (.34) (.30) (.34) (6.03)
-.11 .07 -.05 -.09 Racial identification (.10) (.09) (.10) (1.76)
Racial ident. x White -.05 .03 -.06 6.67 candidate (.26) (.22) (.25) (4.50)
Racial ident. x no-race .01 .32 -.11 3.48 candidate (.25) (.20) (.23) (4.21)
.51** .45*** .38** 5.35* Prototypicality (.14) (.12) (.14) (2.37)
3.30 5.06 3.24 2.19 F (6,58) (6,58) (6,59) (6,58) p<.01 p<.001 p<.01 p<.06
Power .80 .79 .81 .80
*p<.05; ** p < .01; *** p<.001. Standard errors are in parentheses.
163
Table 8.4
The effect of independent variables and prototypicality on dependent variables in no-
crime-article condition (Study 1) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Variables thermometer affect affect impression
2.21 -.82* -.11 -.35 White candidate (4.80) (.32) (.29) (.31)
Race-unid. .418 -.38 .03 -.04 Candidate (4.60) (.31) (.28) (.30)
-3.44* -.25* .03 -.19 Racial identification (1.55) (.11) (.10) (.10)
Racial ident. x White 4.53 -.36 .00 -.26 candidate (4.86) (.32) (.29) (.31_
Racial ident. x no-race .16 -.50 -.21 -.28 candidate (4.51) (.30) (.27) (.29)
12.73*** .75*** -.36** .56*** Prototypicality (2.11) (.15) (.13) (.14)
6.40 6.11 1.57 3.19 F (6,56) (6,58) (6,58) (6,58) p<.001 p<.001 p=.17 p<.01
Power .84 .81 .83 .80
*p<.05; ** p < .01; *** p<.001. Standard errors are in parentheses.
164
Table 8.4 – continued
Dependent Variables
Independent Foreign Domestic Lifestyle Vote Variables issues issues issues likelihood
-.01 -.13 -.11 .45 White candidate (.40) (.31) (.38) (6.23)
Race-unid. -.13 -.16 -.58 5.08 Candidate (.39) (.30) (.36) (5.96)
-.14 -.21* -.16 -1.11 Racial identification (.13) (.10) (.12) (2.05)
Racial ident. x White -.02 .02 .37 .99 candidate (.41) (.32) (.38) (6.43)
Racial ident. x no-race -.06 -.16 .10 -2.76 candidate (.38) (.29) (.36) (5.87)
.52** .76*** .81*** 11.26*** Prototypicality (.18) (.14) (.17) (2.81)
1.31 4.70 4.34 3.09 F (6,58) (6,58) (6,58) (6,58) p=.27 p<.001 p=.001 p<.05
Power .85 .63 .76 .77
*p<.05; ** p < .01; ** p<.001
165
Table 9
Bivariate Correlation Matrix for Prototypicality and Dependent Variables (Study 1)
Feelings Positive Negative Foreign Domestic Lifestyle Vote Prototypicality thermometer affect affect issues issues issues
Prototypicality – .428(**) .474(**) -.205(**) .316(**) .411(**) .381(**) .359(**)
Feelings – .642(**) -.310(**) .388(**) .573(**) .482(**) .732(**) thermometer
Positive affect – -.166(**) .439(**) .575(**) .478(**) .669(**)
Negative affect - – -.098 -.212(**) -.117 .332(**)
Foreign issues – .550(**) .553(**) .419(**)
Domestic issues – .692(**) .505(**)
Lifestyle issues – .446(**)
Vote –
*p < .01; ** p < .001 165
166
Table 10
The effects of reader racial identification on responses to African American, White, and no-race candidate in the African American crime suspect condition (Study 1)
B SE B N Power
Feelings thermometer African American candidate -4.56 2.54 29 .55
White candidate 8.30** 2.69 31 .55
No-race candidate 6.57 3.98 18 .58
Positive affect African American candidate -.29 .16 30 .55
White candidate .56** .19 31 ..54
No-race candidate .48* .20 18 .57
Negative affect African American candidate .17 .16 30 ..56
White candidate -.30* .13 31 .54
No-race candidate -.46* .19 18 .59
Leadership impressions African American candidate -.24 .14 30 .39
White candidate .34* .16 31 .56
No-race candidate .23 .17 18 .57
*p<.05; ** p < .01 **; B – unstandardized regression coefficient; SE B – standard error
167
Table 10 - continued
B SE B N Power
Vote likelihood African American candidate -3.82 2.84 30 .55
White candidate 8.00** 2.86 31 .56
No-race candidate 7.26 3.68 18 .59
*p<.05; ** p < .01 **; B – unstandardized regression coefficient; SE B – standard error
Table 11
The effects of reader racial identification on the negative affect toward African American and White candidate in the no-race crime suspect condition (Study 1)
B SE B N Power
African American candidate .21 .17 21 .55
White candidate -.46* .17 23 .49
* p < .05; B – unstandardized regression coefficient; SE B – standard error
168
Table 12 Analysis of Variance for Feelings Thermometer (Study 1)
Source SS df MS F η2 p Power
Crime story 622.46 3 207.49 .60 .00 .62 .62
Candidate race 2327.21 2 1163.60 3.36 .00 .04 .04
Crime story x candidate race 2470.52 6 411.75 1.19 .00 .31 .31
Racial identification 561.92 1 561.92 1.62 .00 .20 .20
Figure 2
Analysis of Variance for Feelings Thermometer (Study 1)
69.00 Candidate race African American candidate 66.00 White candidate No-race candidate
63.00
60.00
57.00
Estimated Marginal Means Marginal Estimated 54.00
51.00
African White suspect No-race suspect No crime story American suspect Crime story
169
Table 13
Analysis of Variance for Positive Affect (Study 1)
Source SS df MS F η2 p Power
Crime story 5.93 3 1.98 1.19 .00 .31 .31
Candidate race 31.98 2 15.99 9.64 .01 .00 .00
Crime story x candidate race 1.17 6 .20 .12 .00 .99 .99
Racial identification .42 1 .42 .25 .00 .62 .62
Figure 3
Analysis of Variance for Positive Affect (Study 1)
Candidate race
4.40 African American candidate White candidate 4.20 No-race candidate
4.00
3.80
3.60
3.40 Estimated Marginal Means Marginal Estimated 3.20
3.00
African White suspect No-race suspect No crime story American suspect Crime story
170
Table 14
Analysis of Variance for Foreign Issues (Study 1)
Source SS df MS F η2 p Power
Crime story 5.96 3 1.99 1.20 .00 .31 .31
Candidate race 15.01 2 7.50 4.52 .00 .01 .01
Crime story x candidate race 6.30 6 1.05 .63 .00 .70 .70
Racial identification .02 1 .02 .01 .00 .92 .92
Figure 4
Analysis of Variance for Foreign Issues (Study 1)
Candidate race African American 4.50 candidate White candidate No-race candidate 4.25
4.00
3.75
3.50 Estimated Marginal Means Marginal Estimated
3.25
African White suspect No-race suspect No crime story American suspect Crime story
171
Table 15
Analysis of Variance for Domestic Issues (Study 1)
Source SS df MS F η2 p Power
Crime story 2.86 3 .95 .86 .00 .46 .46
Candidate race 6.21 2 3.11 2.79 .00 .06 .06
Crime story x candidate race 4.99 6 .83 .75 .00 .61 .61
Racial identification 2.07 1 2.07 1.86 .00 .17 .17
Figure 5
Analysis of Variance for Domestic Issues (Study 1)
5.00 Candidate race African American candidate 4.80 White candidate No-race candidate
4.60
4.40
4.20
Estimated Marginal Means Marginal Estimated 4.00
3.80
African White suspect No-race suspect No crime story American suspect Crime story
172
Table 16
Analysis of Variance for Lifestyle Issues (Study 1)
Source SS df MS F η2 p Power
Crime story .83 3 .28 .22 .00 .88 .88
Candidate race 17.87 2 8.93 7.05 .00 .00 .00
Crime story x candidate race 8.79 6 1.46 1.16 .00 .33 .33
Racial identification 4.37 1 4.37 3.45 .00 .06 .06
Figure 6
Analysis of Variance for Lifestyle Issues (Study 1)
4.50 Candidate race African American candidate White candidate
4.00 No-race candidate
3.50
3.00 Estimated Marginal Means Marginal Estimated
2.50
African White suspect No-race suspect No crime story American suspect Crime story
173
Table 17
Analysis of Variance for Vote Likelihood (Study 1)
Source SS df MS F η2 p Power
Crime story 258.44 3 86.15 .19 .00 .90 .90
Candidate race 1928.54 2 964.27 2.17 .00 .12 .12
Crime story x candidate race 1169.58 6 194.93 .44 .00 .85 .85
Racial identification 1187.01 1 1187.01 2.67 .00 .10 .10
Figure 7
Analysis of Variance for Vote Likelihood (Study 1)
Candidate race 62.00 African American candidate White candidate 60.00 No-race candidate
58.00
56.00
54.00 Estimated Marginal Means Marginal Estimated 52.00
50.00
African White suspect No-race suspect No crime story American suspect Crime story
174
Table 18
Exploratory factor analysis: Prototypicality (Study 2)
Items Factor 1 Factor 2
Educated .64 .35
Good verbal Skills .63 .32
Good administrator .57 .24
Experienced .69 .16
Persuasive .69 .22
Good judgment in a crisis .58 .46
Fair .47 .55
Strong moral principles .31 .71
Trustworthy .38 .81
Patriotic .12 .53
% variance explained 50.17 11.34
175
Table 19
Exploratory factor analysis: Expectations of policy performance (Study 2)
Items Factor 1 Factor 2 Factor 3
Immigration .73 .13 .16
Economy .81 .12 .24
Spending .70 .30 .22
Environment .58 .47 .23
Healthcare .80 .34 .16
Minority .59 .43 .16
Unemployment .82 .29 .21
Poverty .74 .35 .18
Abortion .22 .65 .29
Research .40 .60 .36
Gays .27 .93 .08
Women .25 .90 .18
Military .42 .12 .52
Values .31 .35 .60
Family .11 .16 .81
% variance explained 55.03 10.59 8.18
176
Table 20
Exploratory factor analysis: Leadership impressions (Study 2)
Items Factor 1
Amount of leadership exhibited by candidate .85
Willingness to vote for candidate .85
How typical candidate is of effective leader .70
Extent to which candidate engages in leadership behavior .87
Candidate fit with the image of effective leader .91
% variance explained 73.80
177
Table 21
Exploratory factor analysis: Candidate affect (Study 2)
Items Factor 1 Factor 2
I like Roger Steele .82 -.34
I respect Roger Steele .86 -.26
I admire Roger Steele .87 -.20
Roger Steele makes me hopeful .87 -.04
Roger Steele makes me proud .85 .03
Roger Steele inspires me .87 -.02
Roger Steele makes me angry -.14 .90
Roger Steele makes me afraid -.08 .90
% variance explained 61.85 21.94
178
Table 22
Descriptive Statistics: Means and Standard Deviations for Prototypicality and Dependent Variables across Levels of Crime
story and Candidate Race (Study 2)
Dependent Crime story Candidate race Variable
African American White No-race
M SD M SD M SD
Competence African American 5.40 1.04 5.14 .60 5.04 .65
White 5.11 .80 5.55 1.10 4.54 1.03
No-race 5.56 .89 4.93 1.25 5.57 .93
No prime 5.61 .92 5.70 .66 5.01 .89
178
179
Table 22 – continued
Dependent Crime story Candidate race Variable
African American White No-race
M SD M SD M SD
Integrity African American 5.52 1.20 4.70 1.11 5.12 .57
White 5.38 .78 5.09 1.43 4.72 1.22
No-race 5.25 .83 4.78 1.32 5.21 1.17
No prime 5.54 .76 5.72 .87 5.04 .95
Feelings therm. African American 72.25 17.02 34.45 22.45 49.64 10.09
White 58.00 13.20 37.00 26.41 54.08 12.43
No-race 60.25 19.57 44.13 23.85 49.58 20.61
No prime 50.00 18.44 57.00 10.83 49.44 12.93 179
180
Table 22 - continued
Dependent Crime story Candidate race Variable
African American White No-race
M SD M SD M SD
Positive affect African American 4.69 1.09 2.74 1.38 3.57 .95
White 4.07 1.03 2.91 1.28 3.05 1.39
No-race 4.10 1.28 2.95 1.44 3.57 1.53
No prime 3.94 1.28 3.12 1.00 3.24 1.19
Negative affect African American 1.31 .85 2.75 1.53 2.86 1.38
White 2.05 1.24 2.71 1.64 1.96 1.26
No-race 1.84 1.21 2.53 1.49 2.71 1.62
No prime 2.33 1.50 1.95 1.38 2.08 1.02 180
181
Table 22 - continued
Dependent Crime story Candidate race Variable
African American White No-race
M SD M SD M SD
Leadership impr. African American 4.78 .89 3.44 1.20 3.80 .78
White 4.19 1.01 3.45 1.50 3.88 1.53
No-race 4.44 1.06 3.44 1.31 4.17 1.21
No prime 4.73 1.07 3.74 .72 3.97 .95
Agenda issues African American 4.55 .97 3.42 1.02 3.80 .70
White 4.23 1.09 3.28 1.33 4.00 1.44
No-race 4.44 1.11 3.38 .99 3.63 1.10
No prime 4.31 1.12 3.96 .67 3.92 .79 181
182
Table 22 - continued
Dependent Crime story Candidate race Variable
African American White No-race
M SD M SD M SD
Civil rights issues African American 3.60 1.14 3.21 1.19 3.28 1.11
White 3.33 1.12 2.87 1.20 3.29 1.11
No-race 3.72 .87 2.98 .91 3.41 1.06
No prime 3.17 .92 3.70 .81 2.93 .90
Moral issues African American 4.46 1.18 3.90 1.14 4.10 .81
White 3.80 1.25 3.54 1.12 4.38 1.02
No-race 4.39 .92 4.20 1.08 4.28 .93
No prime 4.44 .72 4.37 .46 3.98 .96 182
183
Table 22 – continued
Dependent Crime story Candidate race Variable
African American White No-race
M SD M SD M SD
Vote Likelihood African American 14.31 4.11 6.07 5.15 8.57 3.57
White 9.72 5.41 7.05 5.80 8.00 5.22
No-race 10.46 4.83 7.53 5.24 8.93 5.36
No prime 8.67 6.65 8.40 4.03 8.74 3.71
183
184
Table 23
The effect of candidate race on prototypicality across prime conditions (Study 2)
Crime story condition Independent Competence Integrity Variables B SE B B SE B
White candidate -.26 (.31) -.82* (.38)
Afr. American suspect No-race candidate -.36 (.32) -.39 (.38)
F .61 (2, 35), p=.51 2.31 (2,38), p=.11
Power .73 .64
White candidate .44 (.35) -.29 (.40)
White suspect No-race candidate -.57 (.38) -.67 (.42)
F 3.77 (2.42), p<.05 1.26 (2,48), p=.29
Power .60 .67
*p < .05; ** p < .01; *** p < .001; B – unstandardized regression coefficient; SE B – standard error
185
Table 23 - continued
Crime story condition Independent Competence Integrity Variables B SE B B SE B
White candidate -.62 (.35) -.46 (.37)
No-race suspect No-race candidate .01 (.38) -.04 (.38)
F 1.92 (2,44), p=.16 .87 (2,56), p=.43
Power .64 .76
White candidate .09 (.43) .18 (.47)
No prime No-race candidate -.60 (.39) -.50 (.42)
F 2.70 (2,32), p=.08 2.10 (2,32), p=.14
Power .62 .66
*p < .05; ** p < .01; *** p < .001; B – unstandardized regression coefficient; SE B – standard error
186
Table 24
The effect of candidate race on dependent variables across prime conditions (Study 2) – unstandardized regression coefficients
Dependent Variables
Crime Indep. Feelings Positive Negative Leadership Agenda Civil Moral Vote story Variables thermometer affect affect impression issues rights issues likelihood
White -37.8*** -1.95*** 1.44** -1.34** -1.13** -.38 -.56 -8.24*** candidate (7.0) (.45) (.50) (.38) (.36) (.44) (.405) (1.67)
Afr. Race- -22.61** -1.12* 1.55** -.75* -.31 -.37 -5.73** -.98* Am. unid. (6.6) (.45) (.50) (.36) (.44) (.405) (1.67) (.38) suspect Candidate
14.91 9.65 5.94 6.79 5.15 .42 .97 12.53 F (2,34) (2,38) (2,38) (2,38) (2,36) (2,38) (2,38) (2,38) p<.001 p<.001 p<.01 p<.01 p<.05 p=.66 p=.39 p<.001
Power .78 .45 .60 .58 .60 .76 .70 .83
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 186
187
Table 24 - continued
Dependent Variables
Crime Indep. Feelings Positive Negative Leadership Agenda Civil Moral Vote story Variables thermometer affect affect impression issues rights issues likelihood
White -21** -1.16** .66 -.74 -.945* -.46 -.27 -2.67 candidate (6.84) (.405) (.46) (.45) (.45) (.39) (.39) (1.81)
White Race- -3.92 -1.02* -.09 -.23 -.04 .58 -1.72 -.30 suspect unid. (7.48) (.44) (.51) (.48) (.43) (.41) (1.96) (.48) Candidate
5.32 4.68 1.45 1.38 2.34 .85 2.20 1.11 F (2,41) (2,48) (2,47) (2,48) (2,42) (2,45) (2,46) (2,48) p<.01 p<.05 p=.25 p=.26 p=.11 p=.43 p=.12 p=.34
Power .55 .55 .67 .64 .90 .72 .64 .66
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 187
188
Table 24 - continued
Dependent Variables
Crime Indep. Feelings Positive Negative Leadership Agenda Civil Moral Vote story Variables thermometer affect affect impression issues rights issues likelihood
White -16.12* -1.14* .69 -1.0* -1.05** -.73* -.19 -2.92 candidate (7.27) (.47) (.47) (.39) (.36) (.31) (.33) (1.71)
Race- Race- -10.67 -.53 .87 -.80* -.305 -.11 -1.53 -.26 unid. unid. (7.77) (.48) (.48) (.38) (.32) (.33) (1.74) (.40) suspect candidate
2.60 2.99 1.96 3.26 4.88 2.72 .18 1.49 F (2,44) (2,48) (2,48) (2,48) (2,47) (2,48) (2,48) (2,48) p=.09 p=.06 p=.15 p<.05 p<.05 p=.08 p=.84 p=.24
Power .66 .62 .66 .48 .68 .63 .85 .98
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 188
189
Table 24 – continued
Dependent Variables
Crime Indep. Feelings Positive Negative Leadership Agenda Civil Moral Vote story Variables thermometer affect affect impression issues rights issues likelihood
White 7.0 -.83 -.38 -.99* -.35 .53 -.08 -.27 candidate (7.13) (.60) (.63) (.47) (.44) (.455) (.42) (2.265)
No Race-unid. -.56 -.71 -.25 -.76 -.39 -.24 -.46 .07 prime candidate (6.37) (.54) (.57) (.43) (.40) (.415) (.38) (2.05)
.99 1.08 .19 2.34 .50 2.46 1.14 .02 F (2,30) (2,32) (2,32) (2,32) (2,28) (2,31) (2,31) (2,32) p=.38 p=.35 p=.83 p=.11 p=.61 p=.10 p=.33 p=.98
Power .69 .67 .83 .68 .77 .26 .69 .93
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 189
190
Table 25.1
The effects of independent variables and competence prototypicality on dependent variables in African American suspect crime condition (Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White -33.56*** -1.63*** 1.40* -1.08** -.94* -.34 -.30 -7.35*** candidate (6.40) (.41) (.53) (.36) (.36) (.47) (.40) (1.66)
Race-unid. -18.14** -.91* 1.52* -.70 -.54 -.37 .03 -5.06** Candidate (6.30) (.43) (.56) (.37) (.37) (.47) (.42) (1.74)
10.98** .60* .05 .47* .35 -.37 .40 1.91* Competence (3.44) (.22) (.29) (.20) (.20) (.49) (.22) (.91)
14.02 8.98 3.37 5.98 4.02 .49 1.47 9.38 F (3,30) (3,34) (3,34) (3,34) (3,33) (3,34) (3,34) (3,34) p<.001 p<.001 p<.05 p<.01 p<.05 p<.69 p=.241 p<.001
Power .90 .58 .52 .55 .63 .82 .75 .64
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 190
191
Table 25.2
The effects of independent variables and competence prototypicality on dependent variables in White suspect crime condition
(Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White -23.28** -1.42*** .69 -.89* -1.00* -.55 -.32 -3.33* candidate (6.05) (.36) (.50) (.40) (.46) (.40) (.42) (.1.66)
Race-unid. 1.45 -.89* -.41 -.05 -.01 .21 .60 -.55 candidate (6.97) (.40) (.56) (.44) (.51) (.44) (.46) (1.85)
12.39*** .72*** -.44* .78*** .32 .44* .17 3.13*** Competence 2.67) (.16) (.22) (.17) (.20) (.17) (.18) (.72)
10.90 11.54 1.96 7.50 2.27 2.52 1.26 6.93 F (3,34) (3,41) (3,41) (3,41) (3,38) (3,43) (3,41) (3,41) p<.001 p<.001 p=.14 p<.001 p=.10 p=.07 p=.30 p<.01
Power .78 .85 .73 .85 .70 .74 .72 .87
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 191
192
Table 25.3
The effect of independent variables and competence prototypicality on dependent variables in No-race suspect crime condition
(Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White -9.22 -.68 .78 -.62 -.75* -.65 -.06 -1.21 candidate (6.43) (.44) (.49) (.37) (.35) (.34) (.35) (1.63)
Race-unid. -7.75 -.49 .55 -.22 -.78* -.28 -.15 -.81 candidate (7.06) (.45) (.51) (.38) (.37) (.35) (.36) (1.68)
11.81*** .76*** .11 .62*** .48** .12 .17 2.33** Competence (2.74) (.45) (.20) (.15) (.15) (.14) (.14) (.68)
8. 26 8.38 .93 8.06 6.70 1.89 .53 4.93 F (3,39) (3,43) (3,43) (3,43) (3,42) (3,43) (3,43) (3,43) p<.001 p<.001 p=.43 p<.001 p<.001 p=.15 p=.66 p<.01
Power .75 .77 .76 .74 .58 .73 .83 .68
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses.
192
193
Table 25.4
Regression analysis: The effect of independent variables and competence prototypicality on dependent variables in No-crime-
prime condition (Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White 6.31 -.91* -.37 -107** -.43 .51 -.12 -.50 candidate (6.68) (.44) (.63) (.29) (.36) (.45) (.37) (2.0)
Race-unid. 3.39 -.14 -.36 -.24 -.10 -.10 -.16 1.64 candidate (6.21) (.42) (.59) (.27) (.33) (.43) (.35) (1.88)
6.18* .94*** -.18 .87*** .71** .22 .47 2.60** Competence (2.70) (.18) (.26) (.12) (.18) (.19) (.15)** (.82)
2.50 10.50 .28 22.26 5.67 2.10 4.20 3.40 F (3,29) (3,31) (3,31) (3,31) (3,27) (3,30) (3,30) (3,31) p=.08 p<.001 p=.84 p<.001 p<.01 p=.12 p<.05 p<.05
Power .72 .87 .88 1.00 .74 .70 .56 .53
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 193
194
Table 25.5
The effect of independent variables and integrity prototypicality on dependent variables in African American suspect crime condition (Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White -31.08*** -1.80** 1.06* -1.26** -1.01* -1.0 -.15 -7.65*** candidate (6.67) (.47) (.50) (.40) (.37) (.45) (.38) (1.77)
Race-unid. -20.30** -1.05* 1.37** -.95* -.68 -.18 -.17 -5.46** candidate (5.98) (.45) (.48) (.38) (.36) (.43) (.37) (1.69)
7.92** .19 -.46* .10 .19 .34 .50** -5.46** Integrity (2.63) (.19) (20) (.16) (.17) (.18) (.16) (1.69)
15.33 6.75 6.15 4.57 3.91 1.46 4.33 8.82 F (3,33) (3,37) (3,37) (3,37) (3,35) (3,37) (3,37) (3,37) p<.001 p=.001 p<.01 p<.01 p<.05 p=.24 p=.01 p<.001
Power .99 .58 .79 .44 .61 .74 .67 .80
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 194
195
Table 25.6
Regression analysis: The effect of independent variables and integrity prototypicality on dependent variables in White suspect crime condition (Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White -18.00** -1.05** .58 -.56 -.92 -.39 -.24 -2.07 candidate (6.17) (.35) (.48) (.40) (.46) (.38) (.38) (1.67)
Race-unid. .74 -.75* .19 .04 -.18 .12 .68 -.52 candidate (6.79) (.38) (.54) (.43) (.50) (.42) (.42) (1.83)
8.37** .59*** .05 .64*** .08 .28 .18 2.23** Integrity (2.21) (.13) (.18) (.14) (.18) (.14) (.15) (.62)
9.50 11.88 1.80 7.78 1.60 1.90 2.00 5.26 F (3,39) (3,46) (3,45) (3,46) (3,41) (3,44) (3,45) (3,46) p<.001 p<.001 p=.46 p<.001 p=.20 p=.14 p=.13 p<.01
Power .84 .95 .79 .82 .70 .69 .72 .73
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 195
196
Table 25.7
Regression analysis: The effect of independent variables and integrity prototypicality on dependent variables in no-race crime condition (Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White -16.35* -1.04* .74 -.94* -.84* -.73* -.22 -2.84 candidate (6.78) (.44) (.50) (.39) (.35) (.34) (.34) (1.64)
Race-unid. -12.14 -.58 1.01* -.35 -.98* -.35 -.11 -1.7 candidate (7.20) (.45) (.50) (.39) (.36) (.34) (.34) (1.65)
7.17* .54** .03 .36* .37* .01 .13 1.63* Integrity (2.72) (.17) (.20) (.15) (.14) (.13) (.13) (.64)
5. 20 6.15 1.55 4.69 6.05 1.67 .54 3.84 F (3,41) (3,45) (3,45) (3,45) (3,44) (3,45) (3,45) (3,45) p<.01 p=.001 p=.215 p<.01 p<.01 p=.19 p=.66 p<.05
Power .71 .53 .71 .57 .79 .72 .83 .59
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 196
197
Table 25.8
Regression analysis: The effect of independent variables and integrity prototypicality on dependent variables in no-crime-
prime condition (Study 2) – unstandardized regression coefficients
Dependent Variables
Independent Feelings Positive Negative Leadership Agenda Civil Moral Vote Variables thermometer affect affect impression issues rights issues likelihood
White 6.03 -.95 -.34 -1.10** -.48 .50 -.14 -.43 candidate (7.05) (.52) (.63) (.38) (.40) (.46) (.39) (2.27)
Race-unid. 1.28 -.37 -.36 -.46 -.24 -.16 -.29 .52 candidate (6.41) (.48) (.58) (.35) (.37) (.43) (.36) (2.10)
3.66 .68** -.22 .61*** .48* .16 .34* .90 Integrity (2.63) (.20) (.24) (.14) (.18) (.17) (.15) (.86)
1.33 4.99 .41 8.33 2.71 1.92 2.68 .38 F (3,29) (3,31) (3,31) (3,31) (3,27) (3,30) (3,30) (3,31) p=.28 p<.01 p=.74 p<.001 p=.06 p=.15 p=.06 p=.76
Power .74 .67 .84 .71 .69 .72 .69 .5
*p<.05; ** p < .01; ** p<.001. Standard errors are in parentheses. 197
198
Table 26
Bivariate Correlation Matrix for Prototypicality and Dependent Variables (Study 2 )
Feelings Positive Negative Agenda Civil Moral Integrity Competence thermometer affect affect issues rights issues Vote
Integrity – .66(***) .44(***) .45(***) -.14 .26(**) .22(**) .26(**) .36(***)
Competence – .49(***) .53(***) -.10 .35(***) .24(**) .21(**) .46(***)
Feelings – .79(***) -.52(***) .71(***) .46(***) .47(***) .83(***) thermometer
Positive affect – -.241(**) .66(***) .50(***) .47(***) .82(***)
Negative affect – -.36(**) -.16(*) -.10 -.46(**)
Agenda issues – .65(***) .58(***) .62(***)
Civil rights – .56(***) .48(***)
Moral issues – .42(***)
Vote –
*p<.05; **p<.01; ***p<.001 198
199
Table 27
Analysis of Variance for Feelings Thermometer (Study 2)
Source SS df MS F η2 p Power
Crime story 153.36 3 51.12 .15 .00 .93 .93
Candidate race 6651.45 2 3325.72 9.86 .01 .00 .00
Crime story x candidate race 5426.77 6 904.46 2.68 .01 .02 .02
Figure 8
Analysis of Variance for Feelings Thermometer (Study 2)
candidate race African American 70.0 candidate White candidate No-race candidate
60.0
50.0
40.0 Estimated Marginal Means Marginal Estimated
30.0
African White suspect No-race suspect No crime story American suspect Crime story
200
Table 28
Analysis of Variance: The Effect of Candidate race on Feelings Thermometer across
Levels of Crime story (Study 2)
Crime Story Level SS df MS F η2 p Power
African American suspect 8737.00 2 4188.50 14.90 .49 .00 .97
White suspect 3971.63 2 1985.81 5.321 .21 .01 .41
No-race suspect 2352.71 2 1176.35 2.6 .10 .08 .25
No prime 361.43 2 180.72 .99 .06 .38 .48
Table 29
Analysis of Variance: The Effect of Crime story on Feelings Thermometer across Levels of Candidate Race (Study 2)
Candidate Race Level SS df MS F η2 p Power
African American 2389.47 3 796.49 2.67 .14 .06 .36
White 3144.52 3 1048.17 2.00 .11 .13 .38
No-race 194.49 3 64.83 .32 .02 .81 .82
201
Table 30
Analysis of Variance for Positive Affect (Study 2)
Source SS df MS F η2 p Power
Crime story 2.60 3 .86 .55 .00 .65 .65
Candidate race 41.57 2 20.78 13.24 .02 .00 .00
Crime story x candidate race 5.64 6 .94 .60 .00 .73 .73
Figure 9
Analysis of Variance for Positive Affect (Study 2)
candidate race African American candidate 4.50 White candidate No-race candidate
4.00
3.50 Estimated Marginal Means Marginal Estimated 3.00
African White suspect No-race suspect No crime story American suspect Crime story
202
Table 31
Analysis of Variance for Negative Affect (Study 2)
Source SS df MS F η2 p Power
Crime story 1.14 3 .38 .21 .00 .89 .89
Candidate race 10.49 2 5.24 2.88 .00 .06 .06
Crime story x candidate race 16.42 6 2.88 1.50 .00 .18 .18
Figure 10
Analysis of Variance for Negative Affect (Study 2)
3.00 candidate race African American candidate 2.75 White candidate No-race candidate 2.50
2.25
2.00
1.75 Estimated Marginal Means Marginal Estimated 1.50
1.25
African White suspect No-race suspect No crime story American suspect Crime story
203
Table 32
Analysis of Variance for Leadership Impressions (Study 2)
Source SS df MS F η2 p Power
Crime story 1.83 3 .61 .47 .00 .71 .71
Candidate race 26.07 2 13.04 9.96 .01 .00 .00
Crime story x candidate race 3.38 6 .56 .43 .00 .86 .86
Figure 11
Analysis of Variance for Leadership Impressions (Study 2)
candidate race 4.75 African American candidate White candidate 4.50 No-race candidate
4.25
4.00
3.75 Estimated Marginal Means Marginal Estimated
3.50
African White suspect No-race suspect No crime story American suspect Crime story
204
Table 33
Analysis of Variance for Agenda Issues (Study 2)
Source SS df MS F η2 p Power
Crime story 1.22 3 .41 .36 .00 .78 .78
Candidate race 18.35 2 9.17 8.12 .01 .00 .00
Crime story x candidate race 3.30 6 .55 .49 .00 .82 .82
Figure 12
Analysis of Variance for Agenda Issues (Study 2)
candidate race African American 4.50 candidate White candidate
4.25 No-race candidate
4.00
3.75
3.50 Estimated Marginal Means Marginal Estimated
3.25
African White suspect No-race suspect No crime story American suspect Crime story
205
Table 34
Analysis of Variance for Civil Rights Issues (Study 2)
Source SS df MS F η2 p Power
Crime story 1.33 3 .44 .41 .00 .75 .75
Candidate race 1.93 2 .96 .89 .00 .41 .41
Crime story x candidate race 7.54 6 1.26 1.16 .00 .33 .33
Figure 13
Analysis of Variance for Civil Rights Issues (Study 2)
candidate race African American candidate White candidate 3.60 No-race candidate
3.30
Estimated Marginal Means Marginal Estimated 3.00
African White suspect No-race suspect No crime story American suspect Crime story
206
Table 35
Analysis of Variance for Vote Likelihood (Study 2)
Source SS df MS F η2 p Power
Crime story 46.22 3 15.40 .63 .00 .59 .59
Candidate race 316.44 2 158.22 6.52 .02 .00 .00
Crime story x candidate race 211.32 6 35.22 1.45 .00 .20 .20
Figure 14
Analysis of Variance for Vote Likelihood (Study 2)
candidate race African American 15.0 candidate White candidate No-race candidate
12.5
10.0 Estimated Marginal Marginal Means Estimated 7.5
African White suspect No-race suspect No crime story American suspect Crime story
207
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