Explicit and implicit racial attitudes a test of their convergent and predictive validity

Running head: EXPLICIT AND IMPLICIT RACIAL ATTITUDES: A TEST OF THEIR CONVERGENT AND PREDICTIVE VALIDITY Explicit and Implicit Racial Attitudes: A Test of their Convergent and Predictive Validity Shanto Iyengar and Solomon Messing, Stanford University Kyu Hahn, Seoul National University Mahzarin Banaji and Christopher Dial, Harvard University Running head: EXPLICIT AND IMPLICIT RACIAL ATTITUDES: A TEST OF THEIR CONVERGENT AND PREDICTIVE VALIDITY Abstract Using data from national samples, we examine the convergent and predictive validity of explicit and implicit measures of racial prejudice First, we show that explicit measures diverge from a measure of implicit racial bias The number of respondents classified as prejudiced on the implicit measure substantially exceeds the corresponding number based on explicit indicators, suggesting that survey respondents may be masking their racial attitudes Second, in three different experimental contexts, we demonstrate that implicit racial bias predicts a preference for individuals with lighter complexions People classified as prejudiced on the basis of explicit measures, however, not discriminate on the basis of complexion Our findings suggest that future efforts to assess prejudice should incorporate both implicit and explicit racial attitudes The measurement of Americans‟ racial attitudes has become especially challenging in the post-civil rights era because survey respondents are motivated to answer questions in a manner that suggests the absence of racial bias (see McConahay, Hardee, and Batts 1981) For instance, the percentage of white Americans who use stereotypic and derogatory terms (e.g “lazy”) to describe African-Americans has declined sharply since the 1960s (Gaertner and Dovidio 2005; Virtanen and Huddy 1998; Taylor, Sheatsley, and Greeley 1978), and by the end of the twentieth century, whites evaluated blacks just as favorably as their own group But survey questions that disguise the racial cue elicit higher levels of prejudice (Kuklinski and Cobb, 1998; Crosby et al., 1980) In one study with unobtrusive measures, the authors concluded that “blatantly prejudiced attitudes still pervade the white population” (Kuklinski et al 1997, p 403) Thus, when people are unaware that they are violating egalitarian norms, they feel free to express preferences and stereotyped judgments that are hostile to minorities Evidence of lingering racial bias in Americans‟ policy preferences raises further doubts about the decline of prejudice (see Fording 2003; Quillian 2006) In the case of crime, support for punitive policies increases significantly when whites learn that the criminal perpetrator is non-white (Gilliam and Iyengar 2000; Peffley and Hurwitz 2007; Eberhardt et al 2004) Race bias also characterizes a variety of economic markets (Ayres, 2001); job applicants with European-sounding first names are preferred (by 50 percent) over applicants with identical resumes but African American-sounding names (Bertrand and Mullainathan 2004) In short, Americans say they are free of racial bias, but their attitudes and behaviors frequently indicate otherwise Partly in response to normative and impression management pressures, survey researchers have shifted the definition of prejudice away from explicit racial animus in the direction of more indirect and diffuse indicators of “symbolic racism” or “racial resentment.” In this revisionist view, prejudice in the contemporary era is more appropriately defined as a blend of racial animosity and mainstream cultural values (Kinder and Sears, 1981) Operationally, the blend is captured by survey questions that focus on beliefs about minorities‟ adherence to the American way (Kinder and Sanders 1996; Feldman and Huddy 2005) Because the new measures of racism include questions tapping both racial sentiment and support for mainstream cultural values, there is understandable concern over their measurement validity Do they, in fact, measure racial prejudice or some other construct related to prejudice? Proponents cite the close relationship between these measures and a variety of race-related attitudes and policy preferences, e.g racial stereotypes, support for affirmative action, etc (see Kinder and Sanders 1996; Sears and Henry 2005; Tesler and Sears 2010) as evidence of their predictive validity Critics challenge this interpretation on the ground that the manifest content of the racial resentment questions which encompasses both prejudice and ideological predispositions make them definitionally intertwined with questions of public policy (see Sniderman and Tetlock 1986; Sniderman and Carmines 1997) In the most recent critique along these lines, Carmines et al argue that the criterion measures used to assess the predictive validity of new racism (e.g support for affirmative action) may in fact be alternative indicators of the same underlying construct Their analysis demonstrates that the survey items making up the racial resentment scale consistently load on the same underlying factor as a variety of racial policy questions Carmines et al therefore conclude that “both the racial resentment and racial policy measures measure a single underlying dimension, not two different concepts” (Carmines, Sniderman and Easter, 2011, p 106).1 Our goal in this paper is to compare the relative validity of survey and implicit measures of racial bias We used two validation tests First, we correlated survey measures of prejudice with an implicit measure that is not subject to conscious control We found that the survey measures correlated only weakly with implicit bias Second, we assessed the validity of explicit and implicit measures based on their ability to predict a participant‟s choice between two nonwhite individuals who were identical in all respects except for their degree of Afrocentric physical features (primarily skin complexion) Our logic was that white respondents classified as racially prejudiced, should reveal a preference for the less Afrocentric individual We found that the measure of implicit bias predicted preferences based on Afrocentric features, but the survey measures had no bearing on responsiveness to the visual racial cue; indeed, the participants classified as more prejudiced on the survey measures were “color blind” in their preferences The paper is organized as follows We begin with a brief discussion of the distinction between implicit and explicit racial attitudes We then describe our indicators of racial prejudice, both implicit and explicit Next, we present results of a convergent validity analysis, which compares racial attitudes with candidate evaluations While explicit and implicit measures of candidate preference converge, explicit measures of prejudice were quite distinct from the most commonly used measure of implicit racial bias These findings suggest that the divergence cannot be attributed simply to differences in measurement modality, but must represent an underlying behavioral reality Last, we report the results from three predictive validity tests in Using the American National Election Studies data, Carmines et al replicate their analysis on seven different election years with consistent results which respondents indicated their preference as between candidates for elective office or prospective immigrants In each case, we used morphing software to vary the level of Afrocentrism in the target individuals‟ faces Our results show that individuals classified as relatively prejudiced on the implicit measure were significantly less likely to prefer the more Afrocentric individual, while those with higher levels of prejudice according to the survey measures were unresponsive to the racial cue, i.e they treated the more and less Afrocentric targets uniformly In closing, we discuss the implications of our findings for the study of race and politics Implicit Versus Explicit Racial Attitudes The measurement of racial attitudes has long interested social psychologists, but not because of concerns that people may deliberately misrepresent their attitudes Instead, it is largely assumed that conscious aspects of attitudes and beliefs represent but a thin sliver of the mind‟s work Experiments on the most fundamental aspects of the human mind, such as vision and memory, have shown not only that the human brain operates outside conscious awareness, but also that unintentional thought and feeling may be the dominant mode of operation (Bargh 1999) Based on this evidence, psychologists now believe that the mind‟s architecture precludes introspective access for the most part and have therefore sought to develop measures of attitudes and beliefs that exist independent of conscious attitudes (see Banaji and Heiphetz 2010, for a review) Explicit racial attitudes may or may not reflect genuine conscious racial preferences, but in either case they shed no light on less conscious or implicit preferences There is a rapidly growing literature on the relationship between implicit and explicit attitudes and the effect of each on behavioral outcomes (Nosek 2005; Nock and Banaji 2007; McConnell and Liebold 2001; Greenwald et al., 2009) In the area of race, the evidence suggests that implicit attitudes predict race-related behaviors For example, Dovidio et al (2002) found that whites‟ implicit attitudes predicted their non-verbal behavior toward blacks in a classroom task setting, while survey measures only predicted their verbal behavior Towles-Schwen and Fazio (2006) found that anti-black implicit attitudes of white freshmen who had been randomly assigned a black roommate, predicted the stability and duration of the roommate relationships Rooth (2010) found that implicit measures of anti-Muslim stereotypes among Swedish hiring managers predicted the decision to favor Swedes over Arab and Muslim job applicants A recent meta-analysis of attitude-behavior linkages (Greenwald et al., 2009) found not only that implicit racial attitudes reliably predicted relevant behavioral outcomes, but also that the predictive validity of explicit attitudes was compromised in socially sensitive attitude domains In fact, race was the only domain in which the predictive validity of implicit attitudes surpassed explicit attitudes by a significant margin The nature of implicit attitudes necessitates measurement approaches that bypass the standard method posing of questions altogether Researchers rely instead on reaction time to concepts (such as “black” and “white”) and attributes (such as “good” and “bad”) Based on the theory that people respond faster to category-attribute pairs for which they have learned automatic associations, these measures focus on the time taken to respond to pairings of white + good and black + bad and the opposite (black + good and white + bad) to generate an indirect measure of racial preference.2 There are several such latency-based methods, the most common being the Implicit Association Test (IAT; Greenwald, McGhee, and Schwarz, 1998) and Automatic associations should exhibit lower error rates, but error rates have been shown to be a noisier signal than latencies alone (see Greenwald et al., 1998, p 1467) evaluative priming (see Banaji and Heiphetz 2010) As described below, our analysis relies on the former Study This study focused on the convergent and predictive validity of implicit and explicit measures of racial attitudes We assess the former by comparing the distributions of several indicators of explicit racial prejudice with the IAT We find that the level of prejudice captured by the implicit measure is substantially higher than that revealed by the explicit measures Unlike the case of racial attitudes, indicators of explicit and implicit candidate preference have the same distribution and are highly correlated Our criterion of predictive validity is reduced support for Democratic presidential candidate Barack Obama when his complexion is darkened The expected aversion for the darker-skinned version of Obama occurs among study participants with high levels of anti-black implicit bias High scorers on the measures of explicit bias, although less likely to support Obama, are unaffected by the visual cue Indicators The Race Implicit Association Test (IAT) The IAT (Greenwald, McGhee, and Schwartz 1998) is a computer-based task that requires participants to rapidly sort items into categories Based on the time it takes to sort these items and the errors made in sorting, the IAT measures the strength of association between any set of categories (say animals vs plants, Hispanics vs Africans) and attributes (good vs bad, strong vs weak) Most IATs contain four distinct categories consisting of a pair of targets (e.g., African American and European American) and a pair of attributes (e.g., good and bad) These category labels are displayed on either the left or right side of the screen while words or pictures representing the categories appear one by one in the center of the screen Participants sort each item as it appears into its corresponding category using only two computer keys For example, „E‟ might be the key assigned for items representing category A (say African American), which then appears on the left side of the screen, and „I‟ might be assigned to items representing category B, which then appears on the right side of the screen In the case of the race IAT, the target categories African American and European American are represented by images of black and white faces, while the attribute categories good and bad are represented by words conveying positive and negative concepts (e.g., wonderful, joy, laughter and terrible, hurt, failure) Implicit racial attitudes are assessed by subtracting the response times during blocks with hypothesized compatible pairings (e.g., African American paired with bad and European American paired with good) from the response times during blocks with hypothesized incompatible pairings (e.g., African American paired with good and European American paired with bad) Positive values represent faster sorting when African American is paired with bad and European American is paired with good (compared to the opposite); negative values represent faster sorting when African American is paired with good and European American is paired with bad (compared to the opposite).3 An “IAT score” (also referred to as a “D-score”) ranging from -2 to is calculated for each participant based on the difference in response times between the „white-good, black-bad‟ and „white-bad, black-good‟ pairings (full details on scoring an IAT are We used the full version of the race IAT with five blocks in our first study of presidential vote choice In the second study, featuring a pair of prospective immigrants, and in the third study, featuring an unknown black candidate for an Illinois state office, we substituted the brief version of the IAT, which consisted of four trial blocks The brief version of the IAT has been found to yield results that are consistent with those based on the full IAT (see Sriram et al., 2010) For the two studies based on the brief IAT, we removed respondents who incorrectly classified more than 15 percent of all IAT trials presented in Greenwald et al, 2003) Thus, positive IAT scores represent a racial bias in favor of whites over blacks.4 Explicit Racial Attitudes We relied on two widely utilized survey indices of explicit racial attitudes overt racism and racial resentment The former is based on a set of four trait ratings that respondents apply to African-Americans and whites.5 The latter is based on a set of four agree-disagree items that tap beliefs about minorities, individualist cultural values, and support for racial equality.6 In addition to the indices of overt racism and racial resentment, we also compare respondents‟ thermometer ratings (on a 1-10 scale) of self-reported warm or cold feelings towards African-Americans and European-Americans Since it was developed in the 1990s, the race IAT has been used in dozens of papers as a measure of implicit race bias and in studies of intergroup variation in race attitudes (for a review see, Nosek et al 2002; for critical commentary on the IAT and responses, see Blanton and Jaccard 2006; Greenwald, Nosek, and Sriram 2006) The first item in the set was worded as follows: “We‟re interested in your opinions about different groups in our society Using the scale shown below, where a score of would mean that you think most of the people in the group tend to be “hard working,” while a score of would mean that most of the people are “lazy,” where would you place African-Americans.” This was followed by scales with end points of “violent” and “peaceful,” “self-reliant” and “prefer to be on welfare,” and “interact with people of different backgrounds” and “stick to themselves.” We converted each item to a 0-1 metric, summed the four responses aimed at each group and divided by four The final indicator was the difference between the ratings of whites and blacks The Alpha values for the African-American and White indices were 77 and 67 respectively The items, taken from Kinder and Sanders (1996) were as follows (1) “Over the past few years, blacks have got less than they deserve.” (2) “The Irish, Italians, Jews, Vietnamese and other minorities overcame prejudice and worked their way up Blacks should the same without any special favors.” (3) “It‟s really a matter of some people not trying hard enough; if blacks would only try harder they could be just as well off as whites.” (4) “Generations of slavery and discrimination have created conditions that make it difficult for blacks to work their way out of the lower class.” Respondents answered each item along a four-point scale that ranged from “strongly agree” to “strongly disagree.” Items and were reflected, the items were converted to a 0-1 metric and an index score was computed as the average of the four items Coefficient Alpha was 89 Dovidio, John F., Kerry Kawakami, Samuel L Gaertner 2002 “Implicit and Explicit Prejudice and Interracial Interaction.” Journal of Personality and Social Psychology, 82(1): 62–68 Eberhardt, Jennifer L., Phillip A Goff, Valerie J Purdie, Paul G Davies 2004 “Seeing Black: Race, Crime, and Visual Processing.” Journal of Personality and Social Psychology 87(6): 876-93 Feldman, Stanley, Leonie Huddy 2005 “Racial Resentment and White Opposition to RaceConscious Programs: Principles or Prejudice?” American Journal of Political Science 49(1): 168-83 Fording, Richard C 2003 “‟Laboratories of Democracy‟ or Symbolic Politics? The Racial Origins of Welfare Reform.” In Race and the Politics of Welfare Reform, eds Sanford Schram, Joe Soss, Richard C Fording Ann Arbor: University of Michigan Press, 77-100 Gaertner, Samuel L., John F Dovidio 2005 “Understanding and addressing Contemporary Racism: From Aversive Racism to the Common In-Group Identity Model.” Journal of Social Issues 61(3): 615-39 Gilliam, Franklin D., Shanto Iyengar 2000 “Prime Suspects: The Influence of Local Television News on the Viewing Public.” American Journal of Political Science 44(3): 560-73 Greenwald, Anthony G., Debbie E McGhee, Jordan L K Schwartz 1998 “Measuring individual differences in implicit cognition: The implicit association test.” Journal of Personality and Social Psychology 74(6): 1464-1480 Greenwald, Anthony G., Brian A Nosek, Mahzarin R Banaji 2003 “Understanding and Using the Implicit Association Test: I An Improved Scoring Algorithm.” Journal of Personality and Social Psychology 85(2): 197-216 30 Greenwald, Anthony G., Brian A Nosek, N Sriram 2006 “Consequential Validity of the Implicit Association Test: Comment on Blanton and Jaccard (2006).” American Psychologist 61(1): 56-61 Greenwald, Anthony G., T Andrew Poehlman, Eric L Uhlmann, E., Mahzarin R Banaji 2009 “Understanding and Using the Implicit Association Test: III Meta-Analysis of Predictive Validity.” Journal of Personality and Social Psychology 97(1): 17-41 Hill, Seth J., James Lo, Lynn Vavreck, L., John Zaller 2007 “The Opt-In Internet Panel: Survey Mode, Sampling Methodology and the Implications for Political Research.” Unpublished Paper, Department of Political Science, UCLA Iyengar, Shanto, Kyu Hahn, Christopher Dial, Mahzarin Banaji 2009 “Comparing Measures of Implicit and Explicit Racial prejudice.” Presented at the Annual Meeting of the International Society for Political Psychology, Dublin Hochschild, Jennifer L., Vesla Weaver 2007 “The Skin Color Paradox and the American Racial Order.” Social Forces, 86(2): 643-670 Jackman, Simon, Lynn Vavreck 2010 “Primary Politics: Race, Gender, and Age in the 2008 Democratic Primary.” Journal of Elections, Public Opinion and Parties, 20(2): 153-86 Johnson, Mark H 2005 “Subcortical face processing.” Nature Reviews: Neuroscience, 6: 766 – 774 Kanwisher, Nancy, Galit Yovel 2006 “The fusiform face area: a cortical region specialized for the perception of faces.” Philosophical Transactions of the Royal Society B, 361: 21092128 31 Kinder, Donald R., David O Sears 1981 “Prejudice and Politics: Symbolic Racism Versus Racial Threats to the Good Life.” Journal of Personality and Social Psychology 40(3): 414-31 Kinder, Donald R., Lynn M Sanders 1996 Divided by Color: Racial Politics and Democratic Ideals Chicago: The University of Chicago Press Kuklinski, James H., Michael D Cobb, Martin Gilens 1997 “Racial Attitudes and the „New South.‟” The Journal of Politics 59: 323-49 Kuklinski, James H., Paul M Sniderman, Kathleen Knight, Thomas Piazza, Philip E Tetlock, Gordon R Lawrence, Barbara Mellers 1997 “Racial Prejudice and Attitudes Toward Affirmative Action.” American Journal of Political Science 41(2): 402-19 Kuklinski, James H., Michael D Cobb 1998 When White Southerners Converse About Race, In Perception and Prejudice: Race and Politics in the United States Eds Jon Hurwitz and Mark Peffley, New Haven: Yale University Press Maddox, K B., & Gray, S A (2002) Cognitive Representations of Black Americans: Reexploring the Role of Skin Tone Personality and Social Psychology Bulletin, 28(2), 250-259 Maddox, K (2004) Perspectives on Racial Phenotypicality Bias Personality and Social Psychology Review, 8(4), 383-401 McConahay, John B., Betty B Hardee, Valerie Batts 1981 “Has Racism Declined in America? It Depends Upon Who is Asking and What is Asked.” Journal of Conflict Resolution 25(4): 563-79 32 McConnell, Allen R., Jill M Leibold 2001 “Relations among the Implicit Association Test, Discriminatory Behavior, and Explicit Measure of Racial Attitudes.” Journal of Experimental Social Psychology 37(5): 435-42 Mendelberg, Tali 2001 The Race Card: Campaign Strategy, Implicit Messages, and the Norm of Equality Princeton: Princeton University Press Nock, Matthew K., Mahzarin R Banaji 2007 “Prediction of Suicide Ideation and Attempts Among Adolescents Using a Brief Performance-Based Test.” Journal of Consulting and Clinical Psychology 75(5): 707–15 Nosek, Brian A 2005 “Implicit-Explicit Relations.” Current Directions in Psychological Science 16(2): 65-69 Nosek, Brian A., Mahzarin R Banaji, Anthony G Greenwald 2002 Harvesting Intergroup Attitudes and Stereotypes from a Demonstration Website Group Dynamics 6(1): 101115 Nosek, Brian A., Frederick L Smyth 2007 “A Multitrait-Multimethod Validation of the Implicit Association Test: Implicit and Explicit Attitudes are Related but Distinct Constructs.” Experimental Psychology 54: 14-29 Peffley, Mark, Jon Hurwitz 2007 “Persuasion and Resistance: Race and the Death Penalty in America.” American Journal of Political Science 51(4): 996-1012 Quillian, Lincoln 2006 “New Approaches to Understanding Racial Prejudice and Discrimination.” Annual Review of Sociology 32: 299-328 Rivers, Douglas 2005 “Sample Matching: Representative Sampling from Internet Panels.” Unpublished paper, Department of Political Science, Stanford University 33 Rooth, Dan-Olof 2010 “Automatic associations and discrimination in hiring: Real world evidence.” Labour Economics 17(3): 523–534 Sears, David O., P J Henry 2005 “Over Thirty Years Later: A Contemporary Look at Symbolic Racism” In Advances in Experimental Social Psychology, Vol 37 ed Mark P Zanna San Diego: Elsevier Academic Press Sniderman, Paul M., Edward G Carmines 1997 Reaching Beyond Race Cambridge, MA: Harvard University Press Sniderman, Paul M Philip E Tetlock 1986 “Symbolic Racism: Problems of Political Motive Attribution.” Journal of Social Issues 42(2): 129–50 Taylor, D Garth, Paul B Sheatsley, Andrew M Greeley 1978 “Attitudes Toward Racial Segregation.” Scientific American 238(6): 42-49 Tesler, Michael, David O Sears 2010 Obama‟s Race: The 2008 Election and the Dream of a Post-Racial America (Chicago Studies in American Politics) Chicago: University of Chicago Press Towles-Schwen, Tamara Russel H Fazio 2006 “Automatically activated racial attitudes as predictors of the success of interracial roommate relationships.“ Journal of Experimental Social Psychology, 42(5): 698–705 Valentino, Nicholas A., Vincent L Hutchings, Ismail K White 2002 “Cues that Matter: How Political Ads Prime Racial Attitudes During Campaigns.” American Political Science Review 96(1): 75-90 Virtanen, Simo V., Leonie Huddy 1998 “Old-Fashioned Racism and New Forms of Racial Prejudice.” The Journal of Politics 60(2): 311-32 34 Table 1: Comparing the Distribution of Implicit and Explicit Attitudes Imp-Exp Race Attitudes % Pro-white Cohen’s d Correlation Race IAT 80.9 969 Overt Racism 62.5 802 27 Racial Resentment 61.8 23 Race Thermometer 40.4 435 % Pro-Obama Cohen’s d 25 Imp-Exp Correlation Candidate IAT 45.4 094 Vote Intention Candidate Thermometer Candidate Affect 47.5 Candidate Preference 67 52.9 11 67 50.1 265 68 Table 2: Complexion Cues, Racial Attitudes, and Support fror Obama Vote Obama Net Therm Y>=.5 7.86∗∗∗ (1.15) Y>=1 6.48∗∗∗ (1.15) Intercept 110.98∗∗∗ (10.47) Darker Complexion Conditions −0.29 −4.24 (1.24) (8.26) Race IAT 0.39 4.75 (0.35) (3.92) Racial resentment −1.63∗∗∗ −26.87∗∗∗ (0.34) (3.02) Overt racism −1.74 −16.71 (1.12) (12.10) ∗ Darker Conds x IAT −0.90 −14.99∗∗ (0.51) (5.73) Darker Conds x RR 0.11 2.38 (0.44) (3.36) Darker Conds x OR 0.90 2.27 (1.52) (16.56) Party ID −0.81∗∗∗ −13.77∗∗∗ (0.08) (0.95) ∗∗∗ Ideology −0.64 −8.24∗∗∗ (0.14) (1.59) ∗ Economic vote −1.27 −34.37∗∗∗ (0.62) (7.88) Education 0.03 −0.47 (0.11) (1.37) Black 0.75 0.23 (0.52) (4.21) Hispanic 0.09 −3.78 (0.40) (5.15) N 986 879 Nagelkerke R2 0.78 Adjusted R2 0.73 Huber-White robust standard errors in parentheses ∗ significant at p < 05; ∗∗ p < 01; ∗∗∗ p < 001 Figure 1: Interaction Effects - Study µ µ+σ µ − 2σ µ + 2σ 0.8 0.6 0.4 0.0 0.0 µ−σ 0.2 P(Obama vote choice) 0.8 0.6 0.4 P(Obama vote choice) light dark 0.2 1.0 0.8 0.6 0.4 0.2 0.0 µ−σ µ µ+σ µ − 2σ µ + 2σ µ−σ µ µ+σ µ + 2σ IAT x Complexion Racial Resentment x Complexion Overt Racism x Complexion µ − 2σ µ−σ µ µ+σ µ + 2σ IAT score OLS model, interaction p =.50 1.45† 0.41 1.10∗ (0.80) (0.74) (0.56) Y>=.75 0.28 −0.62 0.01 (0.79) (0.73) (0.55) Y>=1 −0.82 −1.60∗ −1.02† (0.79) (0.74) (0.55) Dark Complexion Condition −0.27 0.68 0.16 (0.65) (0.66) (0.46) Race IAT 0.18 0.31 0.24 (0.28) (0.26) (0.19) Racial resentment −0.14 0.20 0.04 (0.19) (0.18) (0.13) Dark Con x IAT −0.76∗ −0.80∗ −0.83∗∗ (0.38) (0.39) (0.27) Dark Cond x RR 0.15 −0.15 0.02 (0.21) (0.21) (0.15) Party ID 0.15 0.08 0.10 (0.12) (0.14) (0.09) Ideology −0.09 −0.19† −0.14† (0.10) (0.10) (0.07) ∗∗∗ ∗∗∗ Policy index 2.44 2.83 2.66∗∗∗ (0.29) (0.29) (0.20) ∗∗ ∗∗ High status 0.50 0.49 0.51∗∗∗ (0.17) (0.17) (0.12) Middle East −0.73∗∗∗ −0.08 −0.40∗∗∗ (0.17) (0.17) (0.11) Education 0.06 −0.00 0.05 (0.13) (0.12) (0.09) Hispanic 0.10 −0.18 −0.04 (0.30) (0.31) (0.22) † Female −0.07 −0.32 −0.18 (0.17) (0.17) (0.12) Candidate −0.22† (0.11) N 560 560 1120 Nagelkerke R2 0.26 0.28 0.26 Huber-White robust standard errors in parentheses † significant at p < 10; ∗ p < 05; ∗∗ p < 01; ∗∗∗ p < 001 Figure 2: Interaction Effects - Study IAT x Afrocentrism 0.5 0.7 0.9 light dark 0.3 0.5 0.7 Approval index 0.9 light dark 0.3 Approval index Resentment x Afrocentrism µ − 2σ µ−σ µ µ+σ µ + 2σ IAT score Ordinal logit model, interaction p =.5 7.35∗∗∗ (0.76) Y>=1 5.84∗∗∗ (0.73) Intercept 86.26∗∗∗ (8.68) Dark Trich 0.30 −4.26 (0.60) (7.07) Race IAT −0.54 −4.54 (0.37) (5.08) Racial Resentment −0.61∗∗∗ −7.00∗∗ (0.17) (2.39) ∗ Dark Trich x IAT −0.74 −9.44∗ (0.44) (5.64) Dark Trich x RR 0.00 0.60 (0.17) (2.32) Party ID −0.55∗∗∗ −7.94∗∗∗ (0.08) (1.14) Ideology −0.29∗ −3.16 (0.15) (2.34) Education −0.14 −1.86 (0.09) (1.22) Black 0.68 0.75 (0.63) (5.00) Hispanic −1.44∗ −5.62 (0.70) (8.75) N 407 376 Nagelkerke R 0.53 Adjusted R2 0.35 Huber-White robust standard errors in parentheses ∗ significant at p < 05; ∗∗ p < 01; ∗∗∗ p < 001 Significance on interaction coefficients based on one-tailed tests Figure 3: Interaction Effects - Study 0.8 0.6 0.4 0.0 µ − 2σ µ−σ µ µ+σ µ − 2σ µ + 2σ µ−σ µ µ+σ µ + 2σ IAT x Complexion Resentment x Complexion 10 20 30 40 light dark -10 Net thermometer rating 10 20 30 40 light dark 50 Racial resentment index Ordinal logit model, interaction n.s 50 IAT score Ordinal logit model, interaction p