Science Policy For All

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The Threat of Stereotype Threat in STEM: How do we address it?

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By: Patrick Wright, Ph.D


source: Eryk via wikimedia CC-BY-SA-4.0

Stereotype threat (ST) was first described by Claude Steele and Joshua Aronson in 1995 and is defined as the risk of confirming and fulfilling a negative stereotype about one’s group and the subsequent potential impact on performance that can result. In their initial study, Steele and Aronson describe the apparent equalization of performance on a verbal exam between African American and Caucasian students in an exam after stating the exam was a tool for studying problem solving (to better understand the “psychological factors involved in solving verbal problems), and thus making no reference to ability, compared to conditions in which ability was made salient to participants (“genuine test of your verbal abilities and limitations so that we might better understand the factors involved in both”). Steele and Aronson argued that priming the African American group of race-based performance stereotypes alone was sufficient to impair their performance. In the decades since this original study, substantial work has been done to characterize this seemingly profound driver of performance and achievement disparities, especially as they pertain to science, technology, engineering, and medicine (STEM) education and careers. However, the application of these findings (both those supporting the existence of ST or calling it into question) into tangible, meaningful changes to guide future research and policy implementation has been uneven at best. What role does ST play in STEM itself, and how can we move constructively into the future to address the broader blight of stereotyping and bias in the sciences?

ST experienced by and biases against minority groups can have a substantial effect across all cogs of the scientific process, from professional progression to publishing. Underrepresentation of minority and female professors is driven predominantly by marginalization in STEM fields. For example, women make up around 50% of faculty in non-STEM fields, whereas they only account for 24% of faculty in STEM fields. African-American faculty make up only approximately 1.5% of faculty in chemistry, biology, and economics, but around 9% of faculty in English, sociology, and educational leadership/policy. Once in a faculty position, however, the problems can continue. A recent study by Magali and colleagues on junior faculty (n=174: n=108 women, n=66 men) at the Stanford School of Medicine using novel ST measures showed that women reported greater susceptibility to ST than men across all items including ST vulnerability (p < 0.001); rejection sensitivity (p = 0.001); gender identification (p < 0.001); perceptions of relative potential (p = 0.048); and, sense of belonging (p = 0.049). Women also reported lower beliefs in advancement (p=0.021). An example statement and Likert scale used to test ST vulnerability includes “I feel that people in academic medicine judge me negatively because of what they think of (my gender) as a group”; 1=strongly disagree, 7=strongly agree). Similarly, for Career Advancement: “I can see myself completing enough research to advance to Associate Professor”; 1= strongly disagree, 7=strongly agree). Finally, when it comes to scientific publishing, Budden et al. showed that after the journal Behavioral Ecology introduced double-blind peer review, there was a significant increase (7.9%, p=0.01) in the proportion of papers with a female first author and a corresponding decrease in papers with a male first author over a four year period, whereas similar journals in the field without blinded review showed not differences in gender representation across the same time period.

ST can even have a profound effect on day to day personal interactions. A recent investigation used an Electronically Activated Recorder (EAR), worn by participants, that records nearby audio for 30 seconds every 12 minutes, as an unobtrusive sampling technique of daily interpersonal interactions. Male and female scientists wore the recorders while at work. Researchers found that when male and female scientists were not talking about work, women reported feeling more engaged, compared to having feelings of disengagement and sounding less competent when talking about work. This behavior was not observed during similar conversations with female colleagues. Toni Schmader, a psychologist at the University of British Columbia and a lead investigator on the study, noted “For a female scientist, particularly talking to a male colleague, if she thinks it’s possible he might hold this stereotype, a piece of her mind is spent monitoring the conversation and monitoring what it is she is saying, and wondering whether or not she is saying the right thing, and wondering whether or not she sounding competent, and wondering whether or not she is confirming the stereotype. By merely worrying about that more, one ends up sounding more incompetent.”

Despite the extensive data outlining the damaging effects of stereotype threat, many scientists and studies question the experimental approaches of these ST studies and the interpretation of their results. Lee Jussim, Professor of Social Psychology at Rutgers University, has noted concerns about the analysis of covariance (ANCOVA) approach that was used in the initial, seminal Steele and Aronson study to compare the performance of both the African American and Caucasian groups , specifically calling into question the use of prior SAT scores as a covariate to adjust the performance scores of both groups. While their covariate-adjusted scores are statistically equivalent, there is little meaning when pre-existing differences are still intact without this adjustment; the seeming primary driving force behind these groupwise differences was entirely controlled for when prior SAT scores were selected as a covariate. Similarly, a meta-analysis on the effects of ST on girls in stereotyped domains, reported that publication bias may be underlying the ostensible effect of ST as it pertains to women’s math performance. Many of these ST studies also have small effect sizes, are underpowered, and are not robust nor replicable.

Despite the ongoing dialogue over the validity of the ST field, progress has been made to implement policies to minimize gender and racial biases and stereotypes across academic and industry settings. Daisy Grewel, a social psychologist in the Office of Diversity and Leadership at Stanford University School of Medicine, has proposed three steps that individuals can use to buffer their own susceptibility to negative stereotypes: adopting a growth mindset, educating themselves and others about the science of stereotypes and how stereotypes affect decision-making, and expanding their professional networks to increase a sense of belonging. National Academy of Sciences released a report  in 2006 called Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering to provide interventional strategies and guidance to academic institutions to minimize stereotyping. The document systematically addresses common stereotypes and beliefs against women in science and engineering such as “Women faculty are less productive than men” or broader stereotypes about the research “Behavioral research is qualitative; why pay attention to the data in this report?” and provides extensive evidence refuting these beliefs. The report states: “Federal funding agencies and foundations should ensure that their practices—including rules and regulations—support the full participation of women and do not reinforce a culture that fundamentally discriminates against women“ and recommends that all research funding agencies should provide workshops to minimize gender bias, and expand support for research on the efficacy of organizational programs designed to reduce gender bias. It also states that Federal agencies should establish guidelines, leverage resources, and enforce existing laws to increase the STEM talent developed in these populations. Schmader and Hall succinctly conclude in a review evaluating current polices implemented to minimize stereotypes and biases the role of policy in this realm: “Policy designed with social psychology in mind can help to recover the human potential lost from stereotype threat. However, only informed implementation can reduce the risk that policies inspire backlash from the majority or exacerbate stereotype threat among minority group members.”

The debate on the significance of ST in STEM and the broader dispute on causes of minority-related performance disparities demonstrates an increased need for research funding to allow studies to recruit larger cohorts, to maximize statistical power, enable collaboration and recruitment of biostatisticians, and pursue more appropriate analysis to give these data their appropriate due and more conclusively illustrate the weight of ST in STEM. The self-esteem, livelihood, and productive output of large groups of people are what is at stake. The questions on the existence of “stereotype threat” as it is currently known is somewhat tangential to the point; these performance disparities in STEM educational and professional settings exist, and the scientific community is attempting to put a name to a face. Despite debate regarding ST research, if these studies have catalyzed the implementation of policies at an institutional level to address implicit biases and change world views, is this not a net gain for all of us? Even if the quantifiable impact of ST does ultimately prove minimal, is it not in everyone’s best interest to implement policies to minimize stereotyping and expand perspectives regardless?

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Written by sciencepolicyforall

July 5, 2018 at 1:20 pm

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