What Is the Implicit Gender-Science Stereotype? Exploring Correlations Between the Gender-Science IAT and Self-Report Measures

Abstract

Implicit measures of the gender-science stereotype are often better than explicit measures in predicting relevant outcomes. This finding could reflect a discrepancy between implicit and explicit stereotypes, but an alternative is that the implicit measure is sensitive to constructs other than the stereotype. Analyzing an archival data set (total N = 478,550), we found that self-reported liking of science versus liberal arts was the best predictor of the gender-science implicit association test (IAT). In a reanalysis of a previous study and a replication of another study, we found that evidence for the IAT’s advantage over explicit stereotypes in predicting relevant outcomes disappeared when controlling for self-reported liking. Therefore, perhaps the IAT has often outperformed the explicit stereotype because the gender-science IAT captures personal attraction, whereas the explicit stereotype does not. It is premature to conclude that implicit constructs are superior to explicit constructs in predicting science-related plans and behavior.

The relationship between implicit intergroup attitudes and beliefs

“…In line with this idea, implicit and explicit attitudes have been shown to be more highly correlated with each other when participants were instructed to focus on their feelings rather than their thoughts (39)(40)(41). Moreover, at least under some conditions, implicit beliefs seem to be more closely related to explicit attitudes than they are to explicit beliefs (42). Finally, a recent investigation using a reinforcement learning perspective has found that whereas implicit attitudes track a history of personally experienced rewards and punishments, explicit attitudes also reflect additional information about the structure of the environment (43).…”

Validating the semantic misattribution procedure as an implicit measure of gender stereotyping

“…Our SMP‐based measure has several advantages over existing IAT‐based measures of gender stereotyping (see Zitelny et al, ). As explained in detail by Teige‐Mocigemba et al (), the blocked task‐structure of the IAT has been linked to various sources of systematic measurement error that can undermine its construct validity.…”

“…Although various paradigms have been used to assess gender stereotyping (e.g., Banaji & Greenwald, ; Banaji & Hardin, ; Blair, Ma, & Lenton, ), the implicit association test (IAT; Greenwald, McGhee, & Schwartz, ) is by far the most popular one among the available options. The IAT has been used to measure various types of gender stereotypical associations, including male–science versus female–arts (Nosek, Banaji, & Greenwald, ; Nosek et al, ; for a review, see Zitelny, Shalom, & Bar‐Anan, ), male–strength versus female–weakness (Blair et al, ; Milne & Grafman, ), male–engineer versus female–teacher (White & White, ), and male–career versus female–household (Gawronski, Ehrenberg, Banse, Zukova, & Klauer, ). IAT‐measured math–gender stereotypes were found to predict academic self‐concepts, academic achievement, and enrollment preferences in 9‐year old girls (Steffens, Jelenec, & Noack, ).…”

How Can Universities Best Encourage Women to Major in Information Systems?

“…The belief that working in IS is a masculine endeavor (Cory et al, 2006;Fuller et al, 2013;Joshi & Schmidt, 2006;Lang, 2012;Sax et al, 2017;Serenko & Turel, 2016;Stout et al, 2016;Zitelny et al, 2017). These studies found that both men and women attribute such "masculine" characteristics of IS careers as isolation from other people, focus on self-promotion rather than group communion, lack of empathy, and aggression.…”

“…These studies have found that parental influence, role models, stereotypes, knowledge of the field, influence of peers, self-efficacy, and external encouragement are important factors that affect women's interest in IS. Recent studies that have examined implicit stereotypes to understand whether people implicitly relate technology to gender and also whether implicit gender identity predicts female interest in IS have found that implicit gender identity may guide "career choice" (Serenko & Turel, 2016;Stout et al, 2016;Zitelny, Shalom, & Bar-Anan, 2017).…”