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Faculty Recruitment Resources

Cropped image of a woman and a man holding resumes and waiting for an interview


Faculty Recruitment Resources

The STRIDE Handbook is designed to assist faculty search committees with faculty recruitment. The objective of the handbook is to provide evidence-based practices to increase the diversity of applicant pools, to ensure the fair and equitable review of all candidates, and to demonstrate that Texas A&M University is committed to creating an inclusive, welcoming, and supportive environment for all faculty, staff, and students.  

This is one of several short essays that was prepared as a set of resources during the original NSF ADVANCE grant period. The essay, written by Samantha January, provides an overview of the concept of implicit or unconscious bias. The STRIDE workshop helps faculty members learn strategies to reduce the influence of implicit bias in faculty searches.

What is it?

Implicit bias represents the unconscious mental models we have about social groups. Specifically, implicit bias refers to the favorable or unfavorable attitudes or stereotypes that affect our unconscious assessment of others. These biases are automatic and are based on characteristics such as gender, race, age, country of origin, or other dimensions of identity. For example, implicit bias toward women would be represented by an unconscious linkage between concepts such that the activation of the people concept (i.e, “women”) unconsciously also activates the secondary concept (i.e., “incompetent”); this secondary concept unconsciously influences our understandings about and judgments of the people concept (Fiske, 2002; Greenwald & Banaji, 1995). The unconscious mental models we have may not align with our declared beliefs or even reflect positions we would explicitly endorse.

How does it relate to women?


Research shows that implicit bias toward women is widespread and influences numerous employment-related decisions including those related to recruitment, selection, and promotion (Goldin & Rouse, 2000;Trix & Penska, 2003; Moss-Racusin et al., 2012;Wold & Wenneras, 1997). For example, there are implicit biases about women relative to important work-related characteristics such as competence, commitment, leadership, and authority. There are also implicit biases about jobs, such as who the ideal workers are—including the sex of the ideal worker—as well as the characteristics necessary to perform the job well. Because implicit biases create the perceptions people have about people and jobs, suboptimal personnel decisions are made because decisions are not made on the “true score” of people and jobs but rather on the biased perceptions of the person and the job.

One of the most compelling papers documenting implicit bias on the selection of women into male dominated fields examined the change in orchestras’ demographic compositions with the initiation of the blind audition (Goldin & Rouse, 2000). A blind audition occurs when musicians audition behind a screen, leaving only the performance itself to be judged by the selection panel. Goldin and Rouse (2000) examined orchestra audition records and rosters before and after the implementation of blind auditions at 11 US major city orchestras. Prior to blind auditions, orchestras were predominantly male; women comprised only 10% or fewer new hires or roster members of most major orchestras. Switching to blind auditions increased the probability that women would advance from early audition rounds by 50% and accounted for nearly a one-third increase in the proportion of women among new hires.

How does it relate to women in STEM?


The evidence for sex-based implicit bias affecting employment-related decisions in academia, and especially STEM fields, is substantial. For example, a content analysis of letters of recommendation for faculty applying for a STEM position revealed that letters written on behalf of men were longer and focused on achievements and research whereas letters written for women were shorter, focused more on teaching, and included more “doubt raisers” like superficial or irrelevant praise (Trix & Penska, 2003). Using an experimental paradigm, researchers (e.g., Moss-Racusin et al., 2012; Steinpreis, Anders, & Ritzke, 1999) found that male applicants received higher scores, were preferred more, and received higher starting salaries than women based on identical CVs that differed only in the obviously gendered name of the applicant; interestingly, this effect was found whether the rater was a man or a woman. Another study found that women had to be nearly 2.5 times more productive than men in order to receive the same ratings of competence from judges for a STEM-related postdoctoral fellowship (Wold & Wenneras, 1997).

Computer models demonstrate that even a 1% advantage toward men over women in promotion creates a large imbalance at the top of the organizational hierarchy (Martell et al., 1996). Thus, the perception that there are insufficient women near the top of organizations to be promoted to executive positions might be correct, but this appears to be based upon promotion biases at lower ranks preventing the advancement of women, not the lack of talented women.



Fiske, S.T. (2002). What we know about bias and intergroup conflict, the problem of the century. Current Directions in Psychological Science, 11, 123-128.

Greenwald, A.G., & Banaji, M.R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4-27.

Goldin, Claudia and Cecilia Rouse. “Orchestrating Impartiality: The Impact Of ‘Blind’ Auditions On Female Musicians,” American Economic Review, 2000, v90(4,Sep), 715-741.

Martell, R. F., Lane, D. M., & Emrich, C. (1996). Male-female differences: A computer simulation.

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41), 16474-16479.

Steinpreis, R. E., Anders, K. A., & Ritzke, D. (1999). The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex roles, 41(7-8), 509-528.

Trix, F., & Psenka, C. (2003). Exploring the color of glass: Letters of recommendation for female and male medical faculty. Discourse and society, 14, 191-220.

Wold, A., & Wenneras, C. (1997). Nepotism and sexism in peer review. Nature, 387(6631), 341-343.

Sample Evaluation Rubrics for Faculty Searches.  The ADVANCE Center at the University of North Carolina-Charlotte has compiled a set of sample evaluation rubrics.

Sample Evaluation Rubric for Diversity Statements.  The University of California-Berkeley has prepared a sample evaluation rubrice for diversity statements.

Letters of Recommendation Letters of Recommendation