This article was originally submitted for publication to the Editor of Advances in Methods and Practices in Psychological Science (AMPPS) in 2015. When the submitted manuscript was subsequently posted online (Silberzahn et al., 2015), it received some media attention, and two of the authors were invited to write a brief commentary in Nature advocating for greater crowdsourcing of data analysis by scientists. This commentary, arguing that crowdsourced research "can balance discussions, validate findings and better inform policy" (Silberzahn & Uhlmann, 2015, p. 189), included a new figure that displayed the analytic teams' effectsize estimates and cited the submitted manuscript as the source of the findings, with a link to the preprint. However, the authors forgot to add a citation of the Nature commentary to the final published version of the AMPPS article or to note that the main findings had been previously publicized via the commentary, the online preprint, research presentations at conferences and universities, and media reports by other people. The authors regret the oversight.

Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 different analyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in analysis of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results.

Race and gender information overlap to shape adults’ representations of social categories. This overlap can lead to the psychological “invisibility” of people whose race and gender identities are perceived to have conflicting stereotypes. In the present research ( N = 249), we examined when race begins to bias representations of gender across development. In Study 1, a speeded categorization task revealed that children were slower to categorize Black women as women, relative to their speed of categorizing White and Asian women as women and Black men as men. Children were also more likely to miscategorize Black women as men and less likely to stereotype Black women as feminine. Study 2 replicated these findings and provided evidence of a developmental shift in categorization speed. An omnibus analysis provided a high-powered test of this developmental hypothesis, revealing that target race begins biasing children’s gender categorization around age 5 years. Implications for the development of social-category representation are discussed.

The common stereotype that brilliance is a male trait is an obstacle to women's success in many prestigious careers. This gender‐brilliance stereotype is powerful in part because it seems to be acquired early in life and might thus shape girls’ career aspirations. To date, however, research on this stereotype has not considered how its acquisition might intersect with (1) the other social identities that men and women are perceived to hold, and (2) the social identities that children themselves hold. The present study examined these open questions. First, we compared 5‐ and 6‐year‐old children's (N = 203) assumptions about the intellectual abilities of White men and women with their assumptions about the intellectual abilities of Black men and women. Second, we compared White children's assumptions about the intellectual abilities of men and women with those of children of color (primarily Latinx, Black, and Asian). The results suggested two main conclusions: First, children learn to associate White men (vs. women), but not Black men (vs. women), with brilliance. In fact, children generally see Black men as less brilliant than Black women. Second, the results suggested that the stereotype associating White men with brilliance is shared by children regardless of their own race. These results add considerable nuance to the literature on the development of gender stereotypes about intellectual ability and have implications for policies that might be implemented to prevent the negative effects of these stereotypes.

We investigated the racial content of perceivers’ mental images of different socioeconomic categories. We selected participants who were either high or low in prejudice toward the poor. These participants saw 400 pairs of visually noisy face images. Depending on condition, participants chose the face that looked like a poor person, a middle income person, or a rich person. We averaged the faces selected to create composite images of each social class. A second group of participants rated the stereotypical Blackness of these images. They also rated the face images on a variety of psychological traits. Participants high in economic prejudice produced strongly class-differentiated mental images. They imagined the poor to be Blacker than middle income and wealthy people. They also imagined them to have less positive psychological characteristics. Participants low in economic prejudice also possessed images of the wealthy that were relatively White, but they represented poor and middle class people in a less racially differentiated way. We discuss implications for understanding the intersections of race and class in social perception.

Mental images of social categories are highly consequential: They can reveal biases and help elucidate the factors that contribute to those biases. One strategy frequently used to evaluate the properties of mental images is reverse correlation, which is a data-driven method that allows researchers to visualize a person’s mental representation of individuals or groups. In social psychology, this technique often employs a unique two-phase structure. This approach, however, has not yet been carefully validated, and its structure may alter the properties of the statistical tests used to evaluate differences between conditions. Using computer simulations to evaluate the Type I error rate in a typical two-phase reverse correlation procedure, we find that it is inflated in a nontrivial set of circumstances.

There is ample evidence of racial and gender bias in young children, but thus far this evidence comes almost exclusively from children's responses to a single social category (either race or gender). Yet we are each simultaneously members of many social categories (including our race and gender). Among adults, racial and gender biases intersect: negative racial biases are expressed more strongly against males than females. Here, we consider the developmental origin of bias at the intersection of race and gender. Relying on both implicit and explicit measures, we assessed 4‐year‐old children's responses to target images of children who varied systematically in both race (Black and White) and gender (male and female). Children revealed a strong and consistent pro‐White bias. This racial bias was expressed more strongly for males than females: children's responses to Black boys were less positive than to Black girls, White boys or White girls. This outcome, which constitutes the earliest evidence of bias at the intersection of race and gender, underscores the importance of addressing bias in the first years of life.

Poor White Americans report feeling “worse off” than poor Black Americans despite the persistent negative effects of racism on Black Americans. Additionally, some health issues are rising among White but not Black Americans. Across two representative samples, we test whether White = wealthy stereotypes lead White Americans to feel relatively worse off than their racial group and whether these perceptions have health consequences. Across both samples, White Americans perceived their own status to be significantly lower than the status of the majority of White Americans. In contrast, Black Americans perceived their own status to be significantly higher than the majority of Black Americans. Critically, status comparisons between the self and one’s racial group predicted the experience of fewer positive emotions among White, but not Black, Americans, which mediated reduced mental and physical health. We conclude that race/class stereotypes may shape how poverty subjectively feels.