Attributions to Discrimination Against Black Victims in a Multiracial Society: Isolating the Effect of Perpetrator Group Membership

Abstract

As the United States becomes more racially and ethnically diverse, interactions between Black people and other minority groups have become increasingly common. The present research examined how a perpetrator’s group membership affects judgments of employment discrimination against a Black victim. Four experiments (combined N = 1,016) tested predictions derived from the prototype model of discrimination. Participants reviewed a case file where a Black, Latino, Asian, or White manager rejected a Black job applicant. Attributions to discrimination were much stronger for a Latino, Asian, or White manager compared with a Black manager. Attributions to discrimination were slightly stronger for a White manager compared with an Asian or Latino manager; however, effect sizes for these differences were small. Attributions to discrimination were similar for the Asian and Latino managers. Whether the perpetrator had outgroup standing relative to the victim was the strongest factor influencing attributions to discrimination for a Black victim of employment discrimination.

Keywords

attributions to discrimination prototypes of discrimination intergroup relations

Jake, a White manager at a large consulting firm needs to hire a new employee. After interviewing a Black man, Jake claims that he is not a good fit for the position and does not hire him. Did Jake engage in discrimination? Would it affect your judgment if the manager was an Asian man named Xian or a Latino man named Javier? At times, people may be uncertain as to whether discrimination occurred, particularly if they are making a judgment about an isolated event (Crosby et al., 1986). In the present article, we examine how the group membership of a perpetrator affects attributions to discrimination (ATDs) when a hiring manager rejects a Black job applicant.

Understanding how people make judgments regarding when a Black person has been a victim of employment discrimination is of both practical and theoretical importance. Hiring discrimination against Black people in the United States remains strong and relatively unchanged over the past 25 years (Quillian et al., 2017). From a practical perspective, identifying the factors that influence judgments of discrimination has implications for when victims of discrimination will receive social support and how juries will make decisions in lawsuits. From a theoretical perspective, the present research tests predictions derived from the prototype model (e.g., Inman & Baron, 1996) about how the group membership of the perpetrator shapes attributions to racial discrimination for Black victims in multiracial contexts. By comparing judgments of majority and minority group perpetrators who reject a Black victim, the present research has the potential to shed light on how laypeople interpret inter-minority group interactions in a multiracial society.

The Racial Context of the Modern United States

Due to the history of enslavement of Black people within the United States, they continue to face some of the highest levels of employment discrimination among racial and ethnic groups in the United States today (e.g., Quillian et al., 2017; Sears & Savalei, 2006; Sidanius & Pratto, 1999). Moreover, White, Asian, and Latino/a people all report significant levels of both implicit and explicit prejudice directed at Black people, suggesting that Black people are likely to experience racial discrimination from White people and other minority groups alike (e.g., Axt et al., 2014).¹ Thus, understanding perceptions of discrimination against a Black victim is a meaningful starting point for an investigation into how people interpret inter-minority rejection.

For most of the 20th century, White people were the largest racial group and Black people were the largest racial minority group in the United States, leading many researchers to adopt a Black–White binary approach to the study of racial discrimination (Plaut, 2010). However, the United States is undergoing a period of increasing racial and ethnic diversity. As of 2018, the four largest racial/ethnic groups in the United States were non-Hispanic Whites (60.1%), Hispanic/Latinos(as) (18.5%), Black/African Americans (13.4%), and Asians (5.9%; U.S. Census Bureau, 2019). Over the past four decades, the percentage of the population that is White declined from 80% in 1980 to 60% in 2018 (CensusScope, 2020). During that same time, the percentages of the population that are Black, Latino/a, and Asian have all increased. Increasing diversity means increased interactions between members of different minority groups. This increased inter-minority interaction carries the opportunity for not only positive outcomes, such as inter-minority alliances, but also the opportunity for negative outcomes including prejudice and discrimination (e.g., Ball & Branscombe, 2019; Craig et al., 2018). Increasing racial and ethnic diversity within the United States has enhanced the need to identify the complex processes that undergird intergroup relations in a multiracial society.

Understanding group stratification within the United States is important for comprehending intergroup relations (Sidanius & Pratto, 1999). Black and Latina/o people both occupy low-status positions in the social hierarchy. Compared with White people, Black and Latino/a people report frequent experiences as targets of discrimination and negative stereotypes, are less likely to complete college, and have lower household income (Lee et al., 2019; Ryan & Bauman, 2016; U.S. Census Bureau, 2019; Zou & Cheryan, 2017). Compared with Black and Latina/o people, Asian people occupy a more intermediate status (Axt et al., 2014; Bergsieker et al., 2010). On one hand, Asian people report being stereotyped as superior relative to Black and Latina/o people, are more likely to complete college, and have higher household income. On the contrary, Asian people report being stereotyped as cold and un-American and also report frequent experiences as targets of discrimination (Lee et al., 2019; Zou & Cheryan, 2017). Finally, White people have the highest status of any racial group. They are less likely than minorities to report experiences as targets of discrimination or negative stereotypes, are more likely to graduate from college, and have higher household income, especially relative to Black and Latino/a people.

People are generally cognizant of the relative status different groups hold within society (e.g., Sidanius & Pratto, 1999). That is, people in the United States are generally aware that White people have higher status than Asian people who have higher status than Black and Latina/o people (e.g., Bergsieker et al., 2010). In the current research, we manipulate whether a Black job applicant is rejected by a White, Asian, Latino, or Black hiring manager and measure ATDs. Thus, the present research will help clarify whether the group status of a perpetrator influences judgments of discrimination against a Black victim.

ATDs: The Prototype Model

The prototype model of ATDs offers a theoretical framework that sheds light on the factors that influence people’s judgments about potential instances of discrimination involving Black victims (e.g., Inman & Baron, 1996). The model is derived from theories of social cognition that conceptualize a prototype as the most typical instance of a social category or as the average of category members (e.g., Fiske & Taylor, 1991). The prototype model of ATDs proposes that people have expectancies about discrimination that affect their attributions in ambiguous situations (e.g., Inman & Baron, 1996).

Prototypes of discrimination are examples of event prototypes (e.g., Lalljee et al., 1992). Event prototypes include a variety of different features such as the characteristics of people who are typically involved in the event. The more similar a particular event is to the event prototype, the more likely that particular event will be classified as a member of the category. O’Brien and colleagues (O’Brien et al., 2008; Simon et al., 2013) proposed that several different features of a situation may influence whether or not individuals interpret a particular event as discrimination including (a) whether or not the perpetrator and victim belong to the same group or a different group, (b) the group status of the perpetrator relative to the victim, and (c) stereotypes about the victim’s competency within a particular domain. To the extent that a particular event includes more features of the discrimination prototype, people should be more likely to make ATDs. An examination of the third feature of discrimination prototypes is beyond the scope of the present research (see O’Brien et al., 2008); here, we focus on the first two features.

There are several reasons why people might pay attention to whether the perpetrator and victim belong to the same group when making judgments about discrimination. Ingroup favoritism and outgroup derogation are robust social phenomena that extend across social groups, cultures, and time (Brewer, 2019; Tajfel & Turner, 1985). Moreover, people are likely to be aware of the dynamics of ingroup favoritism and outgroup derogation and expect other people to favor their ingroup and reject outgroups. Inman and Baron (1996) referred to the expectation that prejudice and discrimination are more likely to stem from the victim’s outgroup than the victim’s ingroup as an outgroup conflict effect. The vast majority of their (mostly White) participants reported that anti-Black prejudice typically emanates from White people,² whereas anti-White prejudice typically emanates from Black people (Inman & Baron, 1996, see Note 1). Thus, participants reported that they generally expected prejudice to come from outgroup members.

Outgroup conflict expectancies may influence perceptions of prejudice and discrimination among adults and children alike (Brown, 2006; Inman & Baron, 1996). For example, children perceived more discrimination when a school teacher showed ingroup favoritism as compared with outgroup favoritism (Brown, 2006). Interestingly, this effect emerged regardless of whether the teacher was White or Latina/o. Thus, at least some evidence to date suggests that people will be more likely to make a judgment of discrimination when the perpetrator and victim are from different groups as compared with when they are from the same group.

There are also reasons why people may pay attention to whether the perpetrator is from a higher status group than the victim when making judgments of discrimination (Inman & Baron, 1996; Rodin et al., 1990). The power to discriminate requires control over resources and high-status groups control more societal resources than low-status groups. People are likely to have some awareness that discrimination is typically perpetrated by members of high-status groups against members of lower status groups. People may develop relatively accurate expectations that discrimination will typically originate from members of high-status groups and be directed at members of low-status groups (Inman & Baron, 1996; Inman et al., 1998; Rodin et al., 1990). Following Rodin and colleagues (1990), we refer to the expectation that discrimination is typically perpetrated by members of high-status groups against members of low-status groups as a status-asymmetry effect.

Evidence of status-asymmetry effects on judgments of discrimination is fairly robust. Compared with when the perpetrator is from a lower status group than the victim, people are much more likely to perceive discrimination when the perpetrator is from a higher status group than the victim. Moreover, these effects have been observed in the domains of racial, gender, age, and sexual orientation discrimination (e.g., Baron et al., 1991; Bucchianeri & Corning, 2013; Corning & Bucchianeri, 2010; Inman & Baron, 1996; Rodin et al., 1990).

Past research has made considerable progress in understanding the factors that influence judgments of discrimination. Unfortunately, however, this research is somewhat uninformative with regard to how people make judgments of racial discrimination against Black victims in situations where the perpetrator is from another racial or ethnic minority group. The available research on judgments of discrimination involving a Black victim has focused exclusively on comparisons between White and Black perpetrators (e.g., Inman & Baron, 1996; Rodin et al., 1990). For example, Inman and Baron (1996) had participants read a series of vignettes in which they manipulated both the race of the perpetrator (White or Black) and the race of the victim (White or Black). When the victim was Black, participants were more likely to label the perpetrator as prejudiced if he was White than if he was Black. However, it is not clear if participants were more likely to label the White perpetrator as prejudiced because he belonged to a higher status racial group or because he belonged to a racial outgroup more generally. Thus, the results of this study are not informative with regard to how people might interpret the actions of a racial or ethnic minority perpetrator who discriminated against a Black victim.

Interestingly, Inman and Baron (1996) found that when the victim was White, participants were equally (un)likely to label Black and White perpetrators as prejudiced (see also Rodin et al., 1990). The results of the White victim condition suggest that people do not always perceive more prejudice in cases of outgroup rejection (i.e., Black on White rejection) compared with ingroup rejection (i.e., White on White rejection). However, it is important to recognize that the White victim condition involves a situation in which the victim is from a high-status group. Comparisons of ingroup and outgroup rejection when the victim is White are of limited value for understanding how people will make judgments about Black victims.

Overview

The goal of the present research was to use the prototype model of discrimination to derive predictions for how people would make judgments of discrimination against a Black victim in cases of inter-minority group rejection. The modern United States comprises multiple racial and ethnic groups that vary in social status (e.g., Axt et al., 2014). We therefore sought to move beyond the Black/White binary framework for conducting diversity science (see Plaut, 2010) and examine ATDs against a Black victim by a perpetrator who is Black, Latino, Asian, or White.

Consistent with past research (e.g., Inman & Baron, 1996; Rodin et al., 1990), the present research focused on ATDs against Black men as opposed to Black women. We chose to focus on Black men because Black men tend to be perceived as more prototypical of their race as compared with Black women (Purdie-Vaughns & Eibach, 2008). Moreover, some evidence suggests that Black men may be more likely to be directly targeted by racial discrimination than Black women (Sidanius et al., 2018). For these reasons, our initial investigation into how people make judgments of racial discrimination in cases of inter-minority group rejection focused on male victims of discrimination. We return to the implications of this decision in the discussion.

In a series of four experiments, participants were told that they would review the hiring process in a large consulting firm and that they would be assigned to review the job application materials, interview transcripts, and employment decision (i.e., whether the person was hired) for one job candidate. All four experiments used the same between-subjects design. Participants were provided with the name and photo of the manager (either a Black, Latino, Asian, or White man) who interviewed the job candidate. They then reviewed the application materials for a moderately qualified job candidate (along with his photo to convey that he was a Black man). Finally, they received correspondence information indicating that the manager had decided not to hire the job candidate and why. After participants learned about the manager’s decisions, they completed the dependent measures including ATDs.

We drew upon past research with the prototype model of discrimination to generate predictions for the present experiments (e.g., Inman & Baron, 1996; O’Brien et al., 2008). Specifically, we tested for evidence of both outgroup conflict and status-asymmetry effects. In fact, one advantage of the present study design that included Black, Latino, Asian, and White perpetrator conditions is that it allowed for a separate examination of outgroup conflict and status-asymmetry effects.

To the extent that an outgroup conflict effect influences judgments of discrimination toward a Black victim, people should be more likely to make ATDs when the perpetrator is Latino, Asian, or White than when he is Black. When the perpetrator and the victim are both Black, ingroup favoritism and outgroup derogation are not viable explanations for the perpetrator’s behavior, and thus ATDs should be relatively low under these circumstances. In comparison, ingroup favoritism and outgroup derogation are plausible explanations whenever the perpetrator is Latino, Asian, or White and thus ATDs should be relatively high under these conditions.

To the extent that status-asymmetries between the perpetrator and victim influence judgments, ATDs should be highest for the White perpetrator, followed by the Asian perpetrator, and followed by the Latino perpetrator. Because the status-asymmetry between a White perpetrator and a Black victim is greater than the status-asymmetry between an Asian perpetrator and a Black victim (or between a Latino perpetrator and a Black victim), people should be more likely to make ATDs when the perpetrator is White as opposed to when he is Asian (or Latino). Likewise, because the status-asymmetry between an Asian perpetrator and a Black victim is greater than the status-asymmetry between a Latino perpetrator and a Black victim, people should be more likely to make ATDs when the perpetrator is Asian as compared with when he is Latino.

Experiment 1

The first study was conducted with a college student sample. After participants reviewed the job applicants’ materials and learned about the employment decision (a rejection), they completed a measure of ATDs. In contexts where people expect decisions to be made based on merit (e.g., hiring), ATDs can be distinguished from purely internal attributions such as attributing an employment rejection decision to a job applicant’s lack of qualifications. Situational factors that increase ATDs tend to decrease attributions that are more internal to the target (Major et al., 2002). Thus, in addition to ATDs, we included a measure of internal attributions. Finally, we assessed perceptions of the extent to which participants thought that the manager’s decision was justified.

Method

Participants

Participants were 197 (73 men, 124 women, M_age = 19.07, SD = 0.95) individuals enrolled in psychology courses at a private, primarily White Southern university. The majority of participants were White: 79.6% White, 9.6% Asian, 2.5% Black/African American, 2.5% Latino(a)/Hispanic, .5% Native American, 5.1% other or multiracial, and .5% missing. There were not enough participants who belonged to racial minority groups to examine the effect of participant race; however, the pattern of results was very similar when the data were analyzed with only White participants.

To calculate a targeted sample size, we conducted an a priori power analysis in GPower 3.1 (Faul et al., 2007). We used an estimated effect size of η² = .044 (f = .215), a common effect size in social psychology (Richard et al., 2003). The analysis suggested that we needed 240 participants to obtain 80% power. We collected as many participants as possible over two semesters (N = 238); however, 41 individuals were excluded for failing attention or manipulation checks. Specifically, 26 people identified the perpetrator’s race incorrectly, 11 people identified the victim’s race incorrectly, and nine individuals incorrectly stated that the victim was hired (numbers sum to more than 41 because some individuals missed multiple manipulation/attention checks). A sensitivity power analysis revealed that, with a final sample size of 197, we had 80% power to detect an effect size η² = .054 in a one-way analysis of variance (ANOVA).

Procedure

Following institutional review board (IRB) approval, participants were recruited through Sona Systems, a cloud-based subject pool software, for partial course credit in their psychology courses. When participants arrived for the experiment, they were provided with informed consent forms to read and sign. The experimenter seated participants at a computer monitor and the study materials were administered with Qualtrics software. Participants were told that the purpose of the experiment was to learn more about the processes that influence hiring decisions. Furthermore, they were told that they would review the credentials of a randomly chosen job applicant along with transcripts of his job interview.

At this point, participants were randomly assigned to one of four conditions: the Black, Latino, Asian, or White manager condition. Manager race/ethnicity was manipulated by providing participants with an ostensible photo of the manager and a name stereotypical of the manager’s group (Jamal Walker, Leo Rodriguez, Scott Lee, or Scott Foster). The four pictures (one for each manager) originated from the Chicago Face Database (Ma et al., 2015) and were high in racial prototypicality and of equal attractiveness. Using Photoshop, each face was digitally superimposed over another photo of a male torso in a business suit.

Next, participants in all four conditions were given a description of the job. They were shown a picture of a Black job applicant named DeShawn Williams and they were provided with some details about his major (management), his minor (marketing), his GPA (3.72), and his past experience. Thereafter, they read a short transcript of an interview between DeShawn and the manager. At this point, participants were told that the manager decided not to hire DeShawn because the manager thought that the company could “find someone more qualified,” that DeShawn was not “the right kind of person,” and that he would not “fit in” on the team. After learning about the manager’s decision, participants completed the dependent measures. Finally, participants received debriefing information explaining the purpose of the study. All study materials can be found online at https://osf.io/hdkja.

Measures

All participants completed the questions in the same order. The ATDs items were embedded between the items assessing internal attributions and beliefs that the decision was justified to reduce suspicion about the purposes of the study (see also Simon et al., 2019). Participants also completed two attention checks and a manipulation check. Finally, at the end of the study, participants completed four exploratory items assessing their beliefs about the extent to which the average person from each of the four racial/ethnic groups is prejudiced against Black people along with demographic information. A description of the exploratory items (which were included in all four experiments) and exploratory analyses are included in the online supplemental material.

ATDs

ATDs were assessed with three items directly adapted from past research (O’Brien et al., 2008): “To what extent do you think the hiring decision was due to discrimination?” “To what extent do you think the hiring decision was based on race?” and “To what extent do you think the hiring decision was due to racism?” Participants responded to each question on an 11-point Likert-type type scale, ranging from 0 (not at all) to 10 (very). We created an aggregate measure by averaging items (α = .97).

Internal attributions

Internal attributions to DeShawn’s qualifications were assessed with two items that were averaged (r = .71, p < .001): “To what extent do you think the hiring decision was based on the applicant’s qualifications?” and “To what extent do you think the hiring decision was based on the applicant’s past experience?” These two items were measured on the same 11-point Likert-type type scale as ATDs.

Justified

Perceptions that the hiring decision was justified were assessed with two items that were averaged (r = .80, p < .001): “How fair do you think the hiring decision was?” and “How justified do you think the hiring decision was?” These two items were measured on the same 11-point Likert-type type scale as ATDs.

Manipulation and attention checks

Participants were asked to indicate the manager’s ethnicity, DeShawn’s ethnicity, and whether or not DeShawn was hired for the position.

Data Cleaning and Screening

We used the same data cleaning and screening procedures across all four experiments. First, data were screened to remove participants who failed the attention checks or manipulation check. Next, we examined the data to determine whether there were statistical outliers on any of the dependent variables, using a cutoff of ±3.00SDs. In the relatively few cases where there were statistical outliers, the distributions for those variables were winsorized. Finally, we assessed skew and kurtosis for all variables and determined whether they were within acceptable ranges. The data files and syntax for all experiments can be found online at https://osf.io/hdkja.

Results

The same data analysis strategy was used across all four experiments. First, we conducted a multivariate analysis of variance (MANOVA) in which perpetrator group membership (Black, Latino, Asian, or White) was the independent variable and ATDs, internal attributions, and perceptions that the decision was justified were the dependent variables. Next, we conducted univariate ANOVAs on each dependent variable, following up with Tukey–Kramer post hoc tests when appropriate.

The MANOVA was significant, V = .209, F(9, 579) = 4.81, p < .001, η² = .070, 90% confidence interval (CI) = [.028, .091]. In addition, the ANOVA on ATDs was significant, F(3, 193) = 11.24, p < .001, η² = .149, 90% CI = [0.071, 0.216]; see Figure 1. When the perpetrator was Black, participants were less likely to make ATDs than when the perpetrator was Latino, Asian, or White; all ps < .001. ATDs did not differ in the Latino, Asian, or White conditions.

Figure 1.

Attributions to discrimination as a function of perpetrator race.

The ANOVA on internal attributions was also significant, F(3, 193) = 2.87, p = .037, η² = .043, 90% CI = [0.001, 0.086]; see Figure 2. When the perpetrator was Black, participants were more likely to make internal attributions than when the perpetrator was White, p = .029. No other comparisons were significant.

Figure 2.

Internal attributions as a function of perpetrator race.

The ANOVA on perceptions that the decision was justified was not significant, F(3, 193) = 1.05, p = .374, η² = .016, 90% CI = [0, 0.043]; see Figure 3.

Figure 3.

Perceptions that the decision was justified as a function of perpetrator race.

Discussion

When participants reviewed a negative employment decision affecting a Black victim, they made stronger ATDs if the perpetrator was Latino, Asian, or White as compared with if the perpetrator was Black. However, there were no significant differences in ATDs depending on whether the perpetrator was Latino, Asian, or White. Thus, the results provide evidence of an outgroup conflict effect. The results did not provide statistically significant evidence of a status-asymmetry effect. Participants were no more likely to make ATDs when the rejection originated from a high-status perpetrator (i.e., a White manager) as compared with a low-status perpetrator (i.e., a Latino manager).

The group membership of the perpetrator had a weaker, but significant, effect on internal attributions, such that internal attributions were higher when the perpetrator was Black than when he was White. Internal attributions in the Asian and Latino perpetrator conditions were not reliably different from either the White or the Black perpetrator condition. The group membership of the perpetrator had no impact on whether participants viewed the decision as justified. Thus, the pattern of results for the internal attributions measure and perceptions that the decision was justified did not show strong evidence of either an outgroup conflict effect or a status-asymmetry effect.

Experiment 2

In the second experiment, we sought to conduct an exact replication with a different, online sample from Amazon’s Mechanical Turk. By conducting the same experiment with a different sample, we were able to determine whether the results would generalize to a different population that was older and more likely to have more work experience.

Method

Participants

Participants were 270 individuals (148 men, 120 women, and two nonbinary/genderless; M_age = 34.99, SD = 10.04) residing in the United States, who were recruited using the online platform Amazon Mechanical Turk® (MTurk®). The majority of participants were White: 76.3% White, 10.0% Black/African American, 7.4% Asian, 4.4% Latino(a)/Hispanic, .7% Native American, and 1.1% other or multiracial. As in Experiment 1, there were not enough participants who belonged to racial minority groups to examine the effect of participant race. However, the results were very similar when the data were analyzed with only White participants.

For Study 2, we increased our recruitment goal from 240 to 300 participants to prospectively account for the need to exclude inattentive participants. We used the MTurk Toolkit on TurkPrime to request 300 participants and manage data collection (Litman et al., 2017). There were 306 participants who completed the study; however, 36 participants had to be excluded due to failing manipulation or attention checks. Specifically, 28 people identified the perpetrator’s race incorrectly, 12 people identified the victim’s race incorrectly, and seven individuals incorrectly stated that the victim was hired.

A sensitivity power analysis revealed that, with a final sample size of 270, we had 80% power to detect effect size η² = .035 in a one-way ANOVA.

Procedure

Following IRB approval, participants were recruited using TurkPrime’s MTurk Toolkit (Litman et al., 2017). Except for the fact that participants were recruited online, all study procedures, materials, and measures were identical to Experiment 1.

Measures

The measures of ATDs (α = .98), internal attributions (r = .78, p < .001), and perceptions that the decision was justified (r = .89, p < .001) were all reliable.

Results

The MANOVA was significant, V = .166, F(9, 798) = 5.20, p < .001, η² = .055, 90% CI = [0.024, 0.073]. In addition, the ANOVA on ATDs was significant, F(3, 266) = 10.17, p < .001, η² = .103, 90% CI = [0.046, 0.155]; see Figure 1. Compared with when the perpetrator was Black, participants were more likely to make ATDs when the perpetrator was Latino, Asian, or White; all ps < .001. ATDs did not differ in the Latino, Asian, or White conditions.

The ANOVA on internal attributions was also significant, F(3, 266) = 3.13, p = .026, η² = .034, 90% CI = [0.002, 0.068]; see Figure 2. When the perpetrator was Black, participants were more likely to make internal attributions as compared with when the perpetrator was White, p = .029. None of the other comparisons were significant.

The ANOVA on perceptions that the decision was justified was not significant, F < 1 (see Figure 3).

Discussion

Experiment 2 replicated Experiment 1. Participants who reviewed a negative employment decision affecting a Black victim were more likely to make an attribution to discrimination if the perpetrator was Latino, Asian, or White than if he was Black. Moreover, there were no statistically significant differences in ATDs for the Latino, Asian, or White perpetrator conditions. Thus, once again, the effect of perpetrator group membership on ATDs showed evidence of an outgroup conflict effect, but did not show statistically significant evidence of a status-asymmetry effect. Replicating Experiment 1, the racial group membership of the perpetrator had no impact on whether participants viewed the decision as justified and only a weak impact on whether participants made internal attributions to the candidate’s qualifications for the rejection.

Experiment 3

The results of the first two experiments with two different samples were very consistent. However, one limitation of both samples in Experiments 1 and 2 is that the majority of the participants were White. White people often perceive racial discrimination differently than Black people and members of other racial minority groups (e.g., Carter & Murphy, 2015). Compared with White people, Black people have more accurate knowledge of racism throughout American history, which partially explains why Black people perceive more racial discrimination in modern-day events (Bonam et al., 2019; Nelson et al., 2013, although cf. Strickhouser et al., 2019). This increased knowledge of racism may explain why Black people may have more nuanced prototypes of racial discrimination as compared with White people (Flournoy et al., 2002). Moreover, some evidence suggests that, compared with White people, Black people are more sensitive to status-asymmetry effects (Simon et al., 2013). Thus, for Experiment 3, we recruited an online sample of Black people to examine how the racial group membership of the perpetrator affected ATDs when the victim was a member of the participant’s ingroup (i.e., a Black person).

Method

Participants

Participants were 218 Black/African American individuals residing in the United States who were recruited online through MTurk (71 men, 146 women, and one nonbinary/genderless; M_age = 36.35, SD = 10.93). We used TurkPrime to manage data collection and to sample Black participants (Litman et al., 2017). TurkPrime profiles MTurk workers by randomly asking demographic questions over time and awarding qualifications to people who give consistent responses. Because the method of determining demographic information is separated from the study, it minimizes concern that workers might misrepresent themselves to qualify for the study.

As in Study 2, we set a recruitment goal of 300 to prospectively account for the need to exclude inattentive participants. Out of 298 participants who completed the study, 23 individuals were excluded because they did not identify as Black/African American and 57 individuals were excluded for failing manipulation or attention checks. Specifically, 47 people identified the perpetrator’s race incorrectly, eight people identified the victim’s race incorrectly, and 38 individuals incorrectly stated that the victim was hired. A sensitivity power analysis revealed that with a final sample size of 218, we had 80% power to detect effect size η² = .049 in a one-way ANOVA.

Procedure

Following IRB approval, participants were recruited using TurkPrime’s MTurk Toolkit (Litman et al., 2017). Participants were unaware that they qualified for the study because of their race. All study procedures, materials, and measures were identical to Experiment 2. After the data were collected, but prior to accessing the data, we registered our hypotheses and data analysis plan on Open Science Framework, https://osf.io/8ydp2.³

Measures

The measures of ATDs (α = .97), internal attributions (r = .69, p < .001), and perceptions that the study was justified (r = .87, p < .001) were reliable.

Results

The MANOVA was significant, V = .292, F(9, 642) = 7.70, p < .001, η² = .097, 90% CI = [0.054, 0.123]. In addition, the ANOVA on ATDs was significant, F(3, 214) = 28.04, p < .001, η² = .282, 90% CI = [0.194, 0.351]; see Figure 1. Compared with when the perpetrator was Black, participants were more likely to make ATDs when the perpetrator was Latino, Asian, or White; all ps < .001. ATDs did not differ in the Latino, Asian, or White conditions.

The ANOVA on internal attributions was also significant, F(3, 214) = 10.73, p < .001, η² = .131, 90% CI = [0.060, 0.193]; see Figure 2. When the perpetrator was Black, participants were more likely to make internal attributions as compared with when the perpetrator was Latino, Asian, or White; all ps > .01. None of the other comparisons were significant.

Finally, the ANOVA on perceptions that the decision was justified was also significant, F(3, 214) = 7.00, p < .001, η² = .089, 90% CI = [0.030, 0.145]; see Figure 3. When the perpetrator was Black, participants were more likely to perceive the decision as justified as compared with when the perpetrator was Latino, Asian, or White; all ps > .05. None of the other comparisons were significant.

Discussion

The effect of the perpetrator group membership on ATDs in Experiment 3 replicated Experiments 1 and 2 with a Black/African American sample. Consistent with an outgroup conflict effect, participants were more likely to make ATDs if the perpetrator belonged to an outgroup (i.e., a Latino, Asian, or White perpetrator) than if he was a member of the victim’s ingroup (i.e., a Black perpetrator). Moreover, there were no statistically significant differences in ATDs depending on the outgroup to which the perpetrator belonged. Thus, like the first two experiments, Experiment 3 did not provide support for a status-asymmetry effect.

In Experiment 3, the pattern of results for perceptions that the decision was justified, and for internal attributions, mirrored the pattern of results for ATDs. Perceptions of justification and internal attributions were higher when the perpetrator was an ingroup member as opposed to an outgroup member. Thus, in Experiment 3, evidence of an outgroup conflict effect was found not only on ATDs, but also on internal attributions and perceptions that the decision was justified.

The overall means for ATDs in Experiment 3 across conditions were generally higher than the means in Experiments 1 and 2. This is unsurprising, given that the majority of participants in Experiments 1 and 2 were White and White people are generally less likely to perceive discrimination as compared with Black people (e.g., Carter & Murphy, 2015; Nelson et al., 2013).

Experiment 4

Experiments 1 to 3 suggest that an outgroup conflict expectancy may affect ATDs for a Black victim in a multiracial context. However, one potential weakness of these three experiments is that the cues to discrimination were relatively weak. In fact, among the majority White samples in Experiments 1 and 2, ATDs were below the midpoint in all four conditions. Thus, we conducted one final experiment with a college student sample with stronger cues to discrimination. Specifically, in Experiment 4, the participants read that the HR director described the (rejected) job candidate in very positive terms and challenged the perpetrator’s decision not to hire him. In addition, we sought to increase our sample size to increase our power to detect differences between conditions.

Method

Participants

Participants were 331 (110 men, 220 women, and one no gender reported; M_age = 18.74, SD = 0.92) individuals recruited from a private, primarily White Southern university. The majority of participants were White: 78.2% White, 9.4% Asian, 4.5% Latino(a)/Hispanic, 2.4% Black/African American, and 5.4% other or multiracial. As in Experiments 1 and 2, the results were very similar when racial minority participants were excluded from the analyses.

In Study 4, we decided to increase our recruitment goal from 300 participants (as in Experiments 2 and 3) to 400 participants. However, we inadvertently recruited 411 participants before we discovered we had surpassed our recruitment goal. The computer failed to record the responses of two individuals; an additional 79 participants were excluded from the analyses for failing attention or manipulation checks. Specifically, 67 people identified the perpetrator’s race incorrectly, 14 people identified the victim’s race incorrectly, and four individuals incorrectly stated that the victim was hired. A sensitivity power analysis revealed that, with a final sample size of 331, we had 80% power to detect effect size η² = .032 in a one-way ANOVA.

Procedure

Following IRB approval, participants were recruited through Sona Systems for partial course credit in their psychology courses. The procedure was identical to Experiment 1; however, some of the study materials were changed slightly to increase the cues that the job applicant experienced discrimination. Specifically, participants were told that, in addition to the project manager, the human resources director also independently reviewed DeShawn’s job application. Participants read that the HR director told the project manager that she believed DeShawn “seemed great,” was “overqualified” for the position, and that the company was lucky to get him for an interview. When the project manager expressed his doubts about DeShawn, the HR director pointed out that the position has been open for several months and that his team is short-staffed. At this point, the project manager insisted that they must find the “right person for the job” and that they should leave the position open to see who else applies. These details were added to provide participants additional reasons to suspect that the project manager may be discriminating against DeShawn. All dependent measures were the same as in Experiments 1 to 3. As with Experiment 3, we registered our predictions and data analysis plan for Experiment 4 at https://osf.io/vug38.⁴

Measures

The measures of ATDs (α = .97), internal attributions (r = .61, p < .001), and perceptions that the study was justified (r = .74, p < .001) were reliable.

Results

The MANOVA was significant, V = .212, F(9, 981) = 8.28, p < .001, η² = .071, 90% CI = [0.040, 0.090]. In addition, the ANOVA on ATDs was significant, F(3, 327) = 25.90, p < .001, η² = .192, 90% CI = [0.127, 0.248]; see Figure 1. Compared with when the perpetrator was Black, participants were more likely to make ATDs than when the perpetrator was Latino, Asian, or White; all ps < .001. Furthermore, when the perpetrator was White, participants were more likely to make ATDs than if the perpetrator was Asian (p = .019) or Latino (p = .079), although in the latter case this comparison was not statistically significant. Finally, there were no differences in ATDs when the manager was Asian or Latino (p = .935).

The ANOVA on internal attributions was not significant, F(3, 327) = 1.38, p = .249, η² = .013, 90% CI = [0, 0.032]; see Figure 2.

Finally, the ANOVA on perceptions that the decision was justified was significant, F(3, 193) = 2.73, p = .044, η² = .024, 90% CI = [0.0003, 0.051]; see Figure 3. When the perpetrator was White, there was a trend, such that participants tended to view the decision as less justified than when the perpetrator was Black (p = .055). No other comparisons were significant.

Discussion

The results of Experiment 4 shared two important similarities with Experiments 1 to 3. First, consistent with an outgroup conflict effect, participants were more likely to make ATDs when the perpetrator was a member of an outgroup (i.e., a Latino, Asian, or White perpetrator) than when he was a member of the victim’s ingroup (i.e., a Black perpetrator). Second, participants were equally likely to make ATDs for perpetrators from two different minority outgroups although the groups differed in status (i.e., an Asian vs. a Latino perpetrator).

The key difference between Experiment 4 and the prior experiments was that there was a statistically significant difference in ATDs when the perpetrator was from a high-status majority outgroup (i.e., White) as compared with when the perpetrator was from a moderate-status minority outgroup (i.e., Asian). Likewise, there was a nearly statistically significant difference in ATDs when the perpetrator was from a high-status majority outgroup as compared with a low-status minority outgroup (i.e., Latino).

Figure 1 reveals that a similar trend emerged across all four experiments. That is, in all four experiments, ATDs were higher when the perpetrator was White as compared with any other group. One possibility is that, due to the use of multiple comparison procedures (MCPs), Experiments 1 to 3 may have been underpowered to detect differences between the White perpetrator condition and the Asian and Latino perpetrator conditions (see Conagin et al., 2008; Cribbie, 2003). That is, in some circumstances, researchers may have adequate power to detect a significant omnibus F, but may have inadequate power to detect comparisons between specific cells when p values are adjusted through MCPs such as Tukey’s post hoc test (Pan & Dayton, 2005). Within social psychology and related fields, many scholars have recently advocated conducting within-paper meta-analyses to provide more comprehensive understandings of psychological phenomena (e.g., Goh et al., 2016; Ledgerwood, 2019). Therefore, we decided to conduct internal meta-analyses of Experiments 1 to 4 to better estimate the effect of perpetrator group membership on ATDs.

Meta-Analyses

We used procedures specified by Borenstein and colleagues (2009) to compute six separate meta-analytic effect sizes. Three effect sizes estimated the difference in ATDs when the perpetrator was an ingroup member versus an outgroup member (i.e., Black manager vs. Latino manager, Black manager vs. Asian manager, and Black manager vs. White manager). Examining this first group of effect sizes allows us to estimate whether there was evidence of an outgroup conflict effect on ATDs. The other three effect sizes estimated the differences in ATDs when outgroup perpetrators varied in group status (i.e., White manager vs. Asian manager, White manager vs. Latino manager, and Asian manager vs. Latino manager). Examining this latter group of effect sizes allows us to estimate whether there was evidence of a status-asymmetry effect on ATDs. We computed Hedges’ g, which is an unbiased version of Cohen’s d, for all of the relevant comparisons across each experiment. Thereafter, we meta-analyzed them, using random effects models.

Table 1 compares ATDs when the perpetrator was an ingroup member with when the perpetrator was from an outgroup. Compared with when the perpetrator was Black, ATDs were consistently higher when the perpetrator was Latino, g = 0.930, z = 5.667, p < .001, Asian, g = 0.917, z = 7.463, p < .001, or White, g = 1.260, z = 8.076, p < .001. Thus, the results of these meta-analyses suggest a large effect of outgroup membership on ATDs, across three very different outgroups.

Table 1.

Meta-Analyses of Attributions to Discriminations Comparing Outgroup Perpetrators With an Ingroup Perpetrator.

Black manager vs. Latino manager	Hedge’s g	[95% confidence interval]	n
Experiment 1	0.860	[0.376, 1.344]	93
Experiment 2	0.562	[0.104, 1.020]	138
Experiment 3	1.368	[0.104, 1.020]	112
Experiment 4	0.972	[0.581, 1.362]	163
Meta-analysis	0.930	[0.608, 1.252]	506
Black manager vs. Asian manager
Experiment 1	1.021	[0.535, 1.507]	92
Experiment 2	0.646	[0.203, 1.090]	134
Experiment 3	1.226	[0.723, 1.728]	114
Experiment 4	0.852	[0.437, 1.268]	153
Meta-analysis	0.917	[0.677, 1.158]	493
Black manager vs. White manager
Experiment 1	1.170	[0.709, 1.629]	98
Experiment 2	0.901	[0.441, 1.361]	144
Experiment 3	1.630	[1.158, 2.102]	114
Experiment 4	1.371	[0.997, 1.745]	169
Meta-analysis	1.26	[0.955, 1.568]	525

Table 2 reports comparisons of ATDs when the perpetrator was an outgroup member who varied in group status. ATDs were higher when the perpetrator was White compared with when he was Asian, g = 0.305, z = 3.427, p < .001. Likewise, ATDs were higher when the perpetrator was White compared with when he was Latino, g = 0.296, z = 3.38, p < .001. However, ATDs were no different when the perpetrator was Asian compared with when he was Latino, g = −0.012, z = −0.130, p = .897.

Table 2.

Meta-Analyses of Attributions to Discriminations Comparing Outgroup Perpetrators of Different Group Status.

White manager vs. Asian manager	Hedges g	[95% confidence interval]	n
Experiment 1	0.109	[−0.389, 0.607]	104
Experiment 2	0.289	[−0.207, 0.786]	132
Experiment 3	0.295	[−0.245, 0.835]	106
Experiment 4	0.448	[0.055, 0.841]	168
Meta-analysis	0.305	[0.130, 0.479]	510
White manager vs. Latino manager
Experiment 1	0.257	[−0.239, 0.752]	105
Experiment 2	0.329	[−0.178, 0.837]	136
Experiment 3	0.172	[−0.367, 0.712]	104
Experiment 4	0.366	[−0.005, 0.738]	178
Meta-analysis	0.296	[0.124, 0.467]	523
Asian manager vs. Latino manager
Experiment 1	0.144	[−0.379, 0.667]	99
Experiment 2	0.053	[−0.443, 0.549]	126
Experiment 3	−0.118	[−0.690, 0.453]	104
Experiment 4	−0.089	[−0.449, 0.321]	162
Meta-analysis	−0.012	[−0.188, 0.164]	491

General Discussion

The present series of four experiments tested predictions derived from the prototype model of discrimination about how people would make ATDs for the rejection of a Black victim. A key goal of the present investigation was to move beyond a Black/White binary framework (e.g., Plaut, 2010) to better understand ATDs in a multiracial society. Thus, we sought to compare and contrast how people would interpret rejection of a Black job applicant by a Black, Latino, Asian, or White manager.

The results provided consistent evidence that people were more likely to make ATDs when the perpetrator was not Black (i.e., he was Latino, Asian, or White) as compared with when the perpetrator was Black. Thus, the present research provides evidence of an outgroup conflict effect. The effect sizes for the differences in ATDs that emerged from comparisons between an outgroup perpetrator versus an ingroup perpetrator were quite large by psychology standards (Hedge’s gs between .91 and 1.26; Cohen, 1988; Funder & Ozer, 2019; Richard et al., 2003). In fact, one of the most noteworthy findings to emerge in the present experiments was just how large the effect sizes comparing the ingroup perpetrator with the outgroup perpetrators were.

In comparison, the results offered less robust and less consistent evidence for a status-asymmetry effect on ATDs for Black victims. There was virtually no difference between attributions for Asian versus Latino perpetrators—the meta-analytic effect size was almost zero. Notably, however, participants were more likely to make ATDs when the perpetrator was White as compared with when he was either Asian or Latino. This suggests that people may, to some extent, be influenced by the group status of the perpetrator. However, the effect sizes for differences in ATDs when the perpetrator was White compared with when he was Asian or Latino were relatively modest. In fact, these comparisons were not statistically significant in most of the individual experiments. Thus, whether the perpetrator had outgroup standing relative to the victim was the strongest factor influencing ATDs in the present experiments. Moreover, this was true regardless of whether the observers came from majority White samples (Experiments 1, 2, and 4), an all Black/African American sample (Experiments 3), college student samples (Experiments 1 and 4), or online MTurk samples (Experiments 2 and 3).

Limitations and Future Directions

The present research provides important information about how the group membership of a perpetrator will influence ATDs for Black victims of employment discrimination. Nonetheless, there are key limitations to the present studies, including the exclusive focus on Black male victims and the focus on majority White and Black/African American samples. In the next section, we discuss these limitations and suggest future avenues for research.

We have argued that people have an outgroup conflict expectancy, such that they expect racial discrimination to be more likely to occur between members of different racial groups rather than between members of the same racial group. In turn, this outgroup conflict expectancy leads people to be more likely to perceive discrimination against a Black victim when the perpetrator is Latino, Asian, or White as compared with when he is Black. However, another possibility is that people expect racial discrimination to originate from non-Black perpetrators and be directed at Black victims. That is, the primary distinction that people make when considering potential cases of discrimination may not be between racial ingroups and racial outgroups but between Black people and non-Black people (e.g., see Sears & Savalei, 2006; Warren & Twine, 1997). Because the present studies only focus on Black victims of discrimination, they are unable to disentangle whether people have a general expectation of outgroup conflict, an expectation of anti-Black discrimination by non-Black people, or both.

Nonetheless, other studies suggest that people have general expectations of outgroup conflict between members of different racial and ethnic groups. For example, evidence suggests that outgroup conflict expectancies influence judgments of discrimination in situations between White and Latino/a people (e.g., Brown, 2006; Mills & Gala, 2012). Thus, perceptions of racial/ethnic discrimination do not appear to be limited to cases in which Black people are the victims. In the future, additional research that examines perceptions of discrimination against Latino/a and Asian victims, as well as victims from other racial minority backgrounds (e.g., Native Americans, and Pacific Islanders) will be important. In particular, research on judgments of discrimination in inter-minority interactions involving non-Black victims will help to determine the extent to which outgroup conflict expectancies shape judgments of racial discrimination more broadly.

The present experiments were also limited by their focus on a Black male as opposed to a Black female victim. In the future, it will be important to compare attributions to racial discrimination when a White person rejects a Black man or Black woman. If, as some have suggested, Black men are more prototypical of their race than Black women (e.g., Purdie-Vaughns & Eibach, 2008), then attributions to racial discrimination may be higher for a Black male than a Black female victim. On the contrary, because Black women simultaneously belong to two low-status groups, perceived discrimination may be greater when the victim is a Black woman as compared with a Black man. The dynamics of how people make judgments about whether discrimination is due to race, gender, or a combination of the two when the victim is a woman of color is largely unexplored and is an important avenue for future research (e.g., see Remedios et al., 2016).

Finally, the present experiments were limited because the majority of the participants were White (Experiments 1, 2, and 4) or Black/African American (Experiment 3). It is difficult to know whether the lack of differences in ATDs when the perpetrator was Asian as compared with Latino would generalize to samples in which the participants themselves identified as Asian or Latino/a. Additional experiments with more diverse samples are necessary to obtain a fuller understanding of ATDs in a multiracial society. Despite the limitations of the present research, they constitute an important initial advance over past research on attributions to racial discrimination that has tended to focus almost exclusively on judgments of White and Black interactions (Plaut, 2010).

One of the primary conclusions of the present experiments is that whether the perpetrator belonged to the victim’s ingroup had a large and robust effect on ATDs. Given how low ATDs were in the Black perpetrator condition, the present experiments raise questions about whether people would ever view ingroup rejection as discrimination. While we would expect that ATDs would generally be low in cases of intragroup rejection, there may be some cases where people do in fact perceive discrimination among members of the same group. For example, O’Brien and colleagues found that Latino/a people were more likely than White people to make ATDs following personal rejection from an ingroup member (O’Brien et al., 2012; see also Goodwin et al., 2010 for a similar finding with White and Black participants). Although more research is needed, one possibility is that people from racial and ethnic minority groups may be more likely to recognize that discrimination can originate from ingroup members. In comparison, White people, who have very little personal experience as victims of discrimination may be unlikely to recognize that discrimination can originate from ingroup members. In any case, research on dynamics and perceptions of intragroup discrimination constitutes another important avenue for future research.

Conclusion

The United States in the 21st century is more racially diverse than it has ever been, yet racial discrimination remains a pervasive feature of everyday life. Black Americans continue to experience discrimination frequently, both in everyday life and in employment (Lee et al., 2019; Quillian et al., 2017). Moreover, White, Asian, and Latino/a Americans all report significant levels of implicit and explicit prejudice against Black Americans (Axt et al., 2014). As the United States and other countries continue to grow more racially diverse, interactions between Black Americans and members of other minority groups are becoming more common. While these interactions offer much promise for positive intergroup interaction, coalition building, and social change (e.g., Ball & Branscombe, 2019; Craig et al., 2018), there will inevitably be occasions in which negative interactions and discrimination occur. Understanding when observers interpret negative interactions as discrimination provides information about when victims of discrimination will receive social, institutional, and legal support. The results of the present experiments suggest that there is some promise for getting third parties to recognize cases of anti-Black employment discrimination that originate from a Latino, Asian, or White perpetrator. On the contrary, people who seek to get third parties to recognize anti-Black employment discrimination that originates from a Black perpetrator may face an uphill battle.

Supplemental Material

sj-docx-1-psp-10.1177_0146167220988372 – Supplemental material for Attributions to Discrimination Against Black Victims in a Multiracial Society: Isolating the Effect of Perpetrator Group Membership

Supplemental material, sj-docx-1-psp-10.1177_0146167220988372 for Attributions to Discrimination Against Black Victims in a Multiracial Society: Isolating the Effect of Perpetrator Group Membership by Laurie T. O’Brien and Sally K. Merritt in Personality and Social Psychology Bulletin

Supplemental Material

sj-docx-2-psp-10.1177_0146167220988372 – Supplemental material for Attributions to Discrimination Against Black Victims in a Multiracial Society: Isolating the Effect of Perpetrator Group Membership

Supplemental material, sj-docx-2-psp-10.1177_0146167220988372 for Attributions to Discrimination Against Black Victims in a Multiracial Society: Isolating the Effect of Perpetrator Group Membership by Laurie T. O’Brien and Sally K. Merritt in Personality and Social Psychology Bulletin

Footnotes

Acknowledgements

The authors acknowledge the assistance of Emily Ramier in supervising data collection. The authors also thank Janet Ruscher, Zachary Airington, and James Moran for the useful comments on a previous version of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Laurie T. O’Brien

Supplemental Material

Supplemental material is available online with this article.

Notes

References

Axt

J. R.

Ebersole

C. R.

Nosek

B. A.

(2014). The rules of implicit evaluation by race, religion, and age. Psychological Science, 25(9), 1804–1815. https://doi.org/10.1177/0956797614543801

Ball

T. C.

Branscombe

N. R.

(2019). When do groups with a victimized past feel solidarity with other victimized groups? In Mallet

R. K.

Monteith

M. J.

(Eds.), Confronting prejudice and discrimination (pp. 73–92). Academic Press. https://doi.org/10.3389/fpsyg.2019.01879

Baron

R. S.

Burgess

M. L.

Kao

C. F.

(1991). Detecting and labeling prejudice: Do female perpetrators go undetected? Personality and Social Psychology Bulletin, 17(2), 115–123. https://doi.org/10.1177/014616729101700201

Bergsieker

H. B.

Shelton

J. N.

Richeson

J. A.

(2010). To be liked versus respected: Divergent goals in interracial interactions. Journal of Personality and Social Psychology, 99(2), 248–264. https://doi.org/10.1037/a0018474

Bonam

C. M.

Nair Das

Coleman

B. R.

Salter

(2019). Ignoring history, denying racism: Mounting evidence for the Marley hypothesis and epistemologies of ignorance. Social Psychological and Personality Science, 10(2), 257–265. https://doi.org/10.1177/1948550617751583

Borenstein

Hedges

L. V.

Higgins

J. P. T.

Rothstein

H. R.

(2009). Introduction to meta-analysis. John Wiley. https://doi.org/10.1002/9780470743386

Brewer

M. B.

(2019). Intergroup relations. In Finkel

Baumeister’s

(Eds.), Advanced social psychology: The state of the science (pp. 249–274). Oxford University Press.

Brown

C. S.

(2006). Bias at school: Perceptions of racial/ethnic discrimination among Latino and European American children. Cognitive Development, 21(4), 401–419. https://doi.org/10.1016/j.cogdev.2006.06.006

Bucchianeri

M. M.

Corning

A. F.

(2013). Disambiguating discriminatory acts of typical versus atypical perpetrators: The moderating role of need for cognitive closure. Journal of Applied Social Psychology, 43, E293–E306. https://doi.org/10.1111/jasp.12027

10.

Carter

E. R.

Murphy

M. C.

(2015). Group-based differences in perceptions of racism: What counts, to whom, and why? Social and Personality Psychology Compass, 9(6), 269–280. https://doi.org/10.1111/spc3.12181

11.

CensusScope. (2020). Population by race: Race and ethnicity selections, 1980-2000. https://www.censusscope.org/us/chart_race.html

12.

Cohen

(1988). Statistics power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587

13.

Conagin

Barbin

Demétrio

C. G. B.

(2008). Modifications for the Tukey test procedure and evaluation of the power and efficiency of multiple comparison procedures. Scientia Agricola, 65(4), 428–432.

14.

Corning

A. F.

Bucchianeri

M. M.

(2010). Perceiving racism in ambiguous situations: Who relies on easy-to-use information? The Journal of Social Psychology, 150(3), 258–277. https://doi.org/10.1080/00224540903365398

15.

Craig

M. A.

Rucker

J. M.

Richeson

J. A.

(2018). The pitfalls and promise of increasing racial diversity: Threat, contact, and race relations in the 21st century. Current Directions in Psychological Science, 27(3), 188–193. https://doi.org/10.1177/0963721417727860

16.

Cribbie

R. A.

(2003). Pairwise multiple comparisons: New yardstick, new results. The Journal of Experimental Education, 71(3), 251–265. https://doi.org/10.1080/00220970309602065

17.

Crosby

Clayton

Alksnis

Hemker

(1986). Cognitive biases in the perception of discrimination: The importance of format. Sex Roles, 14(11–12), 637–646. https://doi.org/10.1007/bf00287694

18.

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.

19.

Fiske

S. T.

Taylor

S. E.

(1991). Social cognition (2nd ed.). McGraw Hill.

20.

Flournoy

J. M.

Prentice-Dunn

Klinger

M. R.

(2002). The role of prototypical situations in the perceptions of prejudice of African Americans. Journal of Applied Social Psychology, 32(2), 406–423. https://doi.org/10.1111/j.1559-1816.2002.tb00222.x

21.

Funder

D. C.

Ozer

D. J.

(2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168. https://doi.org/10.1177/2515245919847202

22.

Goh

J. X.

Hall

J. A.

Rosenthal

(2016). Mini meta-analysis of your own studies: Some arguments on why and a primer on how. Social and Personality Psychology Compass, 10(10), 535–549. https://doi.org/10.1111/spc3.12267

23.

Goodwin

S. A.

Williams

K. D.

Carter-Sowell

A. R.

(2010). The psychological sting of stigma: The costs of attributing ostracism to racism. Journal of Experimental Social Psychology, 46(4), 612–618. https://doi.org/10.1016/j.jesp.2010.02.002

24.

Inman

M. L.

Baron

R. S.

(1996). Influence of prototypes on perceptions of prejudice. Journal of Personality and Social Psychology, 70(4), 727–739. https://doi.org/10.1037/0022-3514.70.4.727

25.

Inman

M. L.

Huerta

(1998). Perceiving discrimination: The role of prototypes and norm violation. Social Cognition, 16(4), 418–450. https://doi.org/10.1521/soco.1998.16.4.418

26.

Lalljee

Lamb

Abelson

R. P.

(1992). The role of event prototypes in categorization and explanation. European Review of Social Psychology, 3(1), 153–182. https://doi.org/10.1080/14792779243000050

27.

Ledgerwood

(2019). New developments in research methods. In Finkel

E. J.

Baumeister

R. F.

(Eds.), Advanced social psychology: The state of the science (pp. 39–61). Oxford University Press.

28.

Lee

R. T.

Perez

A. D.

Boykin

C. M.

Mendoza-Denton

(2019). On the prevalence of racial discrimination in the United States. PLOS ONE, 14(1), e0210698. https://doi.org/10.1371/journal.pone.0210698

29.

Litman

Robinson

Abberbock

(2017). TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behavior Research Methods, 49(2), 433–442. https://doi.org/10.3758/s13428.016.0727.z

30.

D. S.

Correll

Wittenbrink

(2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122–1135.

31.

Major

Quinton

W. J.

McCoy

S. K.

(2002). Antecedents and consequences of attributions to discrimination: Theoretical and empirical advances. In Zanna

M. P.

(Ed.), Advances in experimental social psychology (Vol. 34, pp. 251–330). Academic Press.

32.

Mills

Gala

A. C.

(2012). Prototype use in perceptions of prejudice in interactions between Whites and Latinos. Psi Chi Journal of Psychological Research, 17(1), 10–18. https://doi.org/10.24839/2164-8204.jn17.1.10

33.

Nelson

J. C.

Adams

Salter

P. S.

(2013). The Marley hypothesis. Psychological Science, 24(2), 213–218. https://doi.org/10.1177/0956797612451466

34.

O’Brien

L. T.

Kinias

Major

(2008). How status and stereotypes impact attributions to discrimination: The stereotype-asymmetry hypothesis. Journal of Experimental Social Psychology, 44(2), 405–412. https://doi.org/10.1016/j.jesp.2006.12.003

35.

O’Brien

L. T.

Major

Simon

(2012). Why did you choose that person over me? Ingroup rejection and attributions to discrimination. Journal of Experimental Social Psychology, 48(6), 1225–1233. https://doi.org/10.1016/j.jesp.2012.04.007

36.

Pan

Dayton

C. M.

(2005). Sample size selection for pair-wise comparisons using information criteria. Journal of Modern Applied Statistical Methods, 4(2), Article 27. https://doi.org/10.22237/jmasm/1130804760

37.

Plaut

V. C.

(2010). Diversity science: Why and how difference makes a difference. Psychological Inquiry, 21(2), 77–99. https://doi.org/10.1080/10478401003676501

38.

Purdie-Vaughns

Eibach

R. P.

(2008). Intersectional invisibility: The distinctive advantages and disadvantages of multiple subordinate-group identities. Sex Roles, 59(5–6), 377–391. https://doi.org/10.1007/s11199-008-9424-4

39.

Quillian

Pager

Hexel

Midtbøen

A. H.

(2017). Meta-analysis of field experiments shows no change in racial discrimination in hiring over time. Proceedings of the National Academy of Sciences of the United States of America, 114(41), 10870–10875. https://doi.org/10.1073/pnas.1706255114

40.

Remedios

J. D.

Snyder

S. H.

Lizza

C. A.

(2016). Perceptions of women of color who claim compound discrimination: Interpersonal judgments and perceived credibility. Group Processes & Intergroup Relations, 19(6), 769–783. https://doi.org/10.1177/1368430215591041

41.

Richard

F. D.

Bond

C. J.

Stokes-Zoota

J. J.

(2003). One hundred years of social psychology quantitatively described. Review of General Psychology, 7(4), 331–363. https://doi.org/10.1037/1089-2680.7.4.331

42.

Rodin

M. J.

Price

J. M.

Bryson

J. B.

Sanchez

F. J.

(1990). Asymmetry in prejudice attribution. Journal of Experimental Social Psychology, 26(6), 481–504. https://doi.org/10.1016/0022-1031(90)90052-n

43.

Ryan

C. L.

Bauman

(2016). Educational attainment in the United States: 2015 (Report # P20-578). U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau. https://files.eric.ed.gov/fulltext/ED572028.pdf

44.

Sears

D. O.

Savalei

(2006). The political color line in America: Many “peoples of color” or Black exceptionalism? Political Psychology, 27(6), 895–924.

45.

Sidanius

Hudson

S. T. J.

Davis

Bergh

(2018). The theory of gendered prejudice: A social dominance and intersectionalist perspective. In Mintz

Terris

(Eds.), The Oxford handbook of behavioral political science (Vol. 1, pp. 1–35). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190634131.013.11

46.

Sidanius

Pratto

(1999). Social dominance: An intergroup theory of hierarchy and oppression. Cambridge University Press.

47.

Simon

Kinias

O’Brien

L. T.

Major

Bivolaru

(2013). Prototypes of discrimination: How status asymmetry and stereotype asymmetry affect judgments of racial discrimination. Basic and Applied Social Psychology, 35(6), 525–533. https://doi.org/10.1080/01973533.2013.823620

48.

Simon

Moss

A. J.

O’Brien

L. T.

(2019). Pick your perspective: Racial group membership and judgments of intent, harm, and discrimination. Group Processes & Intergroup Relations, 22(2), 215–232. https://doi.org/10.1177/1368430217735576

49.

Strickhouser

J. E.

Zell

Harris

K. E.

(2019). Ignorance of history and perceptions of racism: Another look at the Marley hypothesis. Social Psychological and Personality Science, 10(7), 977–985. https://doi.org/10.1177/1948550618808863

50.

Tajfel

Turner

J. C.

(1985). An integrative theory of social conflict. In Austin

Worchel

(Eds.), The social psychology of intergroup relations (pp. 7–24). Nelson Hall.

51.

U.S. Census Bureau. (2019). QuickFacts. https://www.census.gov/library/visualizations/2019/comm/age-race-distribution.html

52.

Warren

J. W.

Twine

F. W.

(1997). White Americans, the new minority? Non-blacks and the ever-expanding boundaries of whiteness. Journal of Black Studies, 28(2), 200–218.

53.

Zou

L. X.

Cheryan

(2017). Two axes of subordination: A new model of racial position. Journal of Personality and Social Psychology, 112(5), 696–717. https://doi.org/10.1037/pspa0000080

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