Why Antibias Interventions (Need Not) Fail

Schmader, Toni; Dennehy, Tara C.; Baron, Andrew Scott

doi:10.1177/17456916211057565

Cited by 36 publications

(16 citation statements)

References 150 publications

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“…Yet, despite the best intentions driving efforts to create antibias trainings, existing interventions generally show very small, short-term effects that do not necessarily connect with biased behaviors (Forscher et al, 2019). Schmader et al (2022) argue that many trainings are ineffective because the current methods are not well aligned with the scientific understanding of how and when biases emerge, and that designers of such trainings should direct more attention to creating multi-pronged interventions that address the multiple pathways in which biased behaviors may manifest. Indeed, one successful example that uses such an approach is an intervention by Devine et al (2012), which treats stereotype bias as a "bad habit" that can be broken by creating awareness of bias and interrupting it using multiple strategies.…”

Section: Biased Judgments Of Student Abilitymentioning

confidence: 99%

Teachers’ race and gender biases and the moderating effects of their beliefs and dispositions

2023

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Background Women and people of color continue to be underrepresented in many STEM fields and careers. Many studies have linked societal biases against the mathematical abilities of women and people of color to this underrepresentation, as well as to earlier measures of mathematical confidence and performance. Recent studies have shown that teachers may unintentionally have biases that reflect those in broader society. Yet, many studies on teachers’ reports of students’ abilities use data in the field—not experimental data—and thus often cannot say if the findings reflect bias or actual differences. The few experimental studies conducted suggest bias against the abilities of girls and students of color, but the prior work has limitations, which we seek to address (e.g., local samples, no exploration of moderators, no preregistration). Methods In this preregistered experiment of 458 teachers across the U.S., we randomly assigned gender- and race-specific names to solutions to math problems, then asked teachers to rate the correctness of the solution, as well as the student’s math ability and effort. Teachers also completed scales reflecting their own beliefs and dispositions, which we then assessed how those beliefs/dispositions moderated their biases. We used multilevel modeling to account for the nested data structure. Results Consistent with our preregistered hypotheses, when the solution was not fully correct, findings suggest teachers thought boys had higher ability, even though the same teachers did not report differences in the correctness of the solution or perceived effort. Moreover, teachers who reported that gender disparities no longer exist in society were particularly likely to underestimate girls’ abilities. Although findings revealed no evidence of racial bias on average, teachers’ math anxiety moderated their ability judgments of students from different races, albeit with only marginal significance; teachers with high math anxiety tended to assume that White students had higher math ability than students of color. Conclusions The present research identifies teachers’ beliefs and dispositions that moderate their gender and racial biases. This experimental evidence sheds new light on why even low-performing boys consistently report higher math confidence and pursue STEM—namely, their teachers believe they have higher mathematical ability.

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Section: Biased Judgments Of Student Abilitymentioning

confidence: 99%

Teachers’ race and gender biases and the moderating effects of their beliefs and dispositions

2023

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“…To effectively intervene, then, we should focus on changing the processes captured in the flows associated with a stock and thereby change the dynamics of the system governing the phenomenon (Olenick et al, 2020). For organizations attempting to improve their diversity, this might imply a special focus on organizational culture factors (Byrd & Scott, 2014), which is one potential way to address bias within the organization (Schmader et al, 2022). For example, Ferdman (2013) notes diversity without inclusion may not result in expected positive outcomes.…”

Section: Theoretical Principlesmentioning

confidence: 99%

Clarifying dynamics for organizational research and interventions: A diversity example

Olenick

Dishop

2022

Organizational Psychology Review

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Interventions backfire for many reasons, one being that the dynamics governing a system are not well-understood. To better explain organizational phenomena, and to intervene in ways that yield desired consequences, an appreciation of the core insights from dynamics may be necessary to include into a researcher's toolkit. Although substantial buzz surrounds the term dynamics in organizational science, conceptual missteps are present in the literature, ultimately limiting its application. We provide a coherent description of what dynamics encompasses by (1) advancing a definition and of dynamics and comparing it to other longitudinal concepts, (2) proposing theoretical principles to help researchers apply dynamics concepts to their own research, and (3) demonstrating how dynamics may refine our ability to explain organizational phenomena and devise practical implications. Plain Language Summary Organizational researchers and practitioners are faced with many complex issues and understanding how they unfold over time is difficult. Those difficulties often lead to unexpected results when we try to address those issues. Viewing organizations from a more dynamic lens can shed light on how phenomena evolve over time as governed by the iterative application of mathematical rules. Illumination of dynamics can assist with better targeting interventions and understanding their potential effects. A dynamics lens also emphasizes continuing needs to improve research methods, such as collecting longitudinal data and engaging in computational modeling. To illustrate these points, we discuss how diversity interventions in organizations may be better understood once dynamics are applied. For example, organizations wishing to improve the diversity of their work forces must focus both on why underrepresented groups enter the organization and why they might leave. Focus on only one or the other is unlikely to generate the desired effect. Once identifying the drivers of both entering and leaving and how those factors might reinforce each other, interventions to improve diversity might be better targeted.

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“…Most of our included studies were published in either economics ( 24 ) or sociology ( 14 ) journals, with a handful of studies published in interdisciplinary, psychology, or management science journals ( 13 ). All five groups of theories are represented in multiple disciplines.…”

Section: Introductionmentioning

confidence: 99%

Gender composition predicts gender bias: A meta-reanalysis of hiring discrimination audit experiments

Galos

Coppock

2023

Sci. Adv.

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Since 1983, more than 70 employment audit experiments, carried out in more than 26 countries across five continents, have randomized the gender of fictitious applicants to measure the extent of hiring discrimination on the basis of gender. The results are mixed: Some studies find discrimination against men, and others find discrimination against women. We reconcile these heterogeneous findings through a “meta-reanalysis” of the average effects of being described as a woman (versus a man), conditional on occupation. We find a strongly positive gender gradient. In (relatively better paying) occupations dominated by men, the effect of being a woman is negative, while in the (relatively lower paying) occupations dominated by women, the effect is positive. In this way, heterogeneous employment discrimination on the basis of gender preserves status quo gender distributions and earnings gaps. These patterns hold among both minority and majority status applicants.

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Why Antibias Interventions (Need Not) Fail

Cited by 36 publications

References 150 publications

Teachers’ race and gender biases and the moderating effects of their beliefs and dispositions

Teachers’ race and gender biases and the moderating effects of their beliefs and dispositions

Clarifying dynamics for organizational research and interventions: A diversity example

Gender composition predicts gender bias: A meta-reanalysis of hiring discrimination audit experiments

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