Raising Awareness of Unconscious Assumptions and Their Influence on Evaluation of Candidates

A. INFLUENCE OF UNCONSCIOUS ASSUMPTIONS AND BIASES

Although we all like to think that we are objective scholars who judge people based entirely on merit and on the quality of their work and the nature of their achievements, copious research shows that every one of us brings with us a lifetime of experience and cultural history that shapes our evaluations of others.

Studies show that people who have strong egalitarian values and believe that they are not biased may nevertheless unconsciously or inadvertently behave in discriminatory ways (Dovidio 2001). A first step toward ensuring fairness in the search and screen process is to recognize that unconscious biases, attitudes, and other influences not related to the qualifications, contributions, behaviors, and personalities of candidates can influence our evaluations, even if we are committed to egalitarian principles.

The results from controlled research studies in which people were asked to make judgments about human subjects demonstrate the potentially prejudicial nature of our many implicit or unconscious assumptions. Examples range from physical and social expectations or assumptions to those that have a clear connection to hiring, even for faculty positions.

It is important to note that in most of these studies, the gender of the evaluator was not significant, indicating that both men and women share and apply the same assumptions about gender. Recognizing biases and other influences not related to the quality of candidates can help reduce their impact on your search and review of candidates. Spending sufficient time on evaluation (15–20 minutes per application) can also reduce the influence of assumptions.

1. Examples of common social assumptions/expectations

  • When shown photographs of people of the same height, evaluators overestimated the heights of male subjects and underestimated the heights of female subjects, even though a reference point, such as a doorway, was provided (Biernat and Manis 1991).
  • When shown photographs of men with similar athletic abilities, evaluators rated the athletic ability of African American men higher than that of white men (Biernat and Manis 1991).
  • Students asked to choose counselors from among a group of applicants with marginal qualifications more often chose white candidates than African American candidates with identical qualifications (Dovidio and Gaertner 2000).

These studies show how generalizations that may or may not be valid can be applied to the evaluation of individuals (Bielby and Baron 1986). In the study on height, evaluators applied the statistically accurate generalization that men are usually taller than women to their estimates of the height of individuals who did not necessarily conform to the generalization. If we can inaccurately apply generalizations to characteristics as objective and easily measured as height, what happens when the qualities we are evaluating are not as objective or as easily measured? What happens when, as in the studies of athletic ability and choice of counselor, the generalization is not valid? What happens when such generalizations unconsciously influence the ways we evaluate other people?

2. Examples of assumptions that can influence the evaluation of candidates

  • When rating the quality of verbal skills as indicated by vocabulary definitions, evaluators rated the skills lower if they were told an African American provided the definitions than if they were told that a white person provided them (Biernat and Manis 1991).
  • When asked to assess the contribution of skill and luck to successful performance of a task, evaluators more frequently attributed success to skill for males and to luck for females, even though males and females performed the task equally well (Deaux and Emswiller 1974).
  • Evaluators who were busy, distracted by other tasks, and under time pressure gave women lower ratings than men for the same written evaluation of job performance. Sex bias decreased when they gave ample time and attention to their judgments, which rarely occurs in actual work settings. This study indicates that evaluators are more likely to rely upon underlying assumptions and biases when they can/do not give sufficient time and attention to their evaluations (Martell 1991).
  • Evidence suggests that perceived incongruities between the female gender role and leadership roles create two types of disadvantage for women: (1) ideas about the female gender role cause women to be perceived as having less leadership ability than men and consequently impede women’s rise to leadership positions, and (2) women in leadership positions receive less favorable evaluations because they are perceived to be violating gender norms. These perceived incongruities lead to attitudes that are less positive toward female leaders than male leaders (Eagly and Karau 2002; Ridgeway 2001).
  • A study of the nonverbal responses of white interviewers to African American and white interviewees showed that white interviewers maintained (1) higher levels of visual contact, reflecting greater attraction, intimacy, and respect when talking with whites, and (2) higher rates of blinking, indicating greater negative arousal and tension, when talking with African Americans (Dovidio et al. 1997).

3. Examples of assumptions or biases in academic contexts 
Several research studies have shown that biases and assumptions can affect the evaluation and hiring of candidates for academic positions. These studies show that the assessment of résumés and postdoctoral applications, evaluation of journal articles, and the language and structure of letters of recommendation are significantly influenced by the sex of the person being evaluated.

  • A study of over 300 recommendation letters for medical faculty hired at a large U.S. medical school in the 1990s found that letters for female applicants differed systematically from those for males. Letters written for women were shorter, seemed to provide “minimal assurance” rather than solid recommendation, raised more doubts, and portrayed women as students and teachers while portraying men as researchers and professionals. While such differences were readily apparent, it is important to note that all letters studied were for successful candidates only (Trix and Psenka 2002).
  • In a national study, 238 academic psychologists (118 male, 120 female) evaluated a résumé randomly assigned a male or a female name. Both male and female participants gave the male applicant better evaluations for teaching, research, and service and were more likely to hire the male than the female applicant (Steinpreis et al. 1999). Another study showed that the preference for males was greater when women represented a small proportion of the pool of candidates, as is typical in many academic fields (Heilman 1980).
  • A study of postdoctoral fellowships awarded by the Medical Research Council in Sweden found that women candidates needed substantially more publications to achieve the same rating as men, unless they personally knew someone on the panel (Wenneras and Wold 1997).
  • In a replication of a 1968 study, researchers manipulated the name of the author of an academic article, assigning a name that was male, female, or neutral (initials). The 360 college students who evaluated this article were influenced by the name of the author, evaluating the article more favorably when it was written by a male than when written by a female. Questions asked after the evaluation was complete showed that bias against women was stronger when evaluators believed that the author identified only by initials was female (Paludi and Bauer 1983).

These sorts of built-in assumptions can impede your efforts to recruit and review an excellent and diverse pool of candidates. It is best to talk to your committee about being conscious of assumptions and biases in order to build a broad pool from diverse sources and evaluate the candidates fairly.

It is also essential to remind your search committee that considerable time and attention, 15–20 minutes per application, are required to evaluate candidates fairly and adequately. Underlying assumptions and biases are more likely to play a role in evaluation when the evaluator cannot or does not give sufficient time and attention to the task.

In addition, it is useful to note that many of our colleagues have followed nontraditional career paths and been exceedingly successful. If your committee rejects candidates who have not held a postdoctoral position, come from a less prestigious research institution, or are teaching at a small college, be sure that you apply the same criteria uniformly across the pool and are certain that you don’t want to know more about the candidates before rejecting their applications.

B. POTENTIAL INFLUENCE OF UNCONSCIOUS ASSUMPTIONS AND BIASES ON YOUR SEARCH

  • Women and minorities may be subject to higher expectations in areas such as number and quality of publications, name recognition, or personal acquaintance with a committee member. (Recall the example of the Swedish Medical Research Council.)
  • Candidates from institutions other than the major research universities that have trained most of our faculty may be undervalued. (Qualified candidates from institutions such as historically black universities, four-year colleges, government, or the private sector might offer innovative, diverse, and valuable perspectives on research and teaching.)
  • The work, ideas, and findings of women or minorities may be undervalued or unfairly attributed to a research director or collaborators despite contrary evidence in publications or letters of reference. (Recall the biases seen in evaluations of written descriptions of job performance and the attribution of success to luck rather than skill.)
  • The ability of women or minorities to run a research group, raise funds, and supervise students and staff may be underestimated. (Recall assumptions about leadership abilities.)
  • Assumptions about possible family responsibilities and their effect on the candidate’s career path may negatively influence evaluation of merit, despite evidence of productivity. (Recall studies of the influence of population generalizations on evaluation of an individual.)
  • Negative assumptions about whether female or minority candidates will “fit in” to the existing environment can influence evaluation. (Recall students’ choice of counselor.)
  • The professional experience candidates may have acquired through an alternative career path may be undervalued. (As examples, latecomers to a field may be more determined and committed; industrial or other nonacademic experience may be more valuable for a particular position than postdoctoral experience.)
  • Other possible biases, assumptions, or unwritten criteria may influence your evaluation. (Some examples include holding a degree from a prestigious research university, recognizing the names of the candidates, and/or recognizing the name of or knowing the references provided by the candidates. Such candidates are not necessarily the most qualified. Be sure that such factors don’t serve to disadvantage highly qualified candidates, especially candidates from diverse backgrounds.)

Please discuss the potential influence of unconscious assumptions and biases with your search committee.

C. OVERCOMING THE INFLUENCE OF UNCONSCIOUS BIASES AND ASSUMPTIONS

  • Learn about research on biases and assumptions.
  • Discuss research on biases and assumptions and consciously strive to minimize their influence on your evaluation of candidates.
  • Develop criteria for evaluating candidates and apply them consistently to all applicants.
  • Spend sufficient time (15–20 minutes) evaluating each applicant.
  • Evaluate each candidate’s entire application; don’t depend too heavily on only one element such as the letters of recommendation, or the prestige of the degree-granting institution or postdoctoral program.
  • Be able to defend every decision for rejecting or retaining a candidate.
  • Periodically evaluate your decisions and consider whether qualified women and underrepresented minorities are included. If not, consider whether evaluation biases and assumptions are influencing your decisions.

D. RESOURCES

Bielby, William T. and James N. Baron. 1986. Sex segregation and statistical discrimination. American Journal of Sociology 91:759–799.

Biernat, Monica and Melvin Manis. 1991. Shifting standards and stereotype-based judgements. Journal of Personality and Social Psychology 66:5–20.

Deaux, Kay and Tim Emswiller. 1974. Explanations of successful performance on sex-linked tasks: What is skill for the male is luck for the female. Journal of Personality and Social Psychology 29:80–85.

Dovidio, John F. and S. L. Gaertner. 2000. Aversive racism and selection decisions: 1989 and 1999. Psychological Science 11:315–319.

Dovidio, John F. 2001. On the nature of contemporary prejudice: The third wave.Journal of Social Issues 57(4):829–849.

Dovidio, John F., Kerry Kawakami, Craig Johnson, Brenda Johnson, and Adaiah Howard. 1997. On the nature of prejudice: Automatic and controlled processes.Journal of Experimental Psychology 33:510-540.

Eagly, Alice H. and Steven J. Karau. 2002. Role congruity theory of prejudice toward female leaders. Psychological Review 109:573–597.

Heilman, Madeline E. 1980. The impact of situational factors on personnel decisions concerning women: Varying the sex composition of the applicant pool. Organizational Behavior and Human Performance 26:386–395.

Martell, Richard F. 1991. Sex bias at work: The effects of attentional and memory demands on performance ratings for men and women. Journal of Applied Social Psychology 21:1939–60.

Ridgeway, Cecilia L. 2001. Gender, status, and leadership. Journal of Social Issues57:637–655.

Paludi, Michele A. and William D. Bauer. 1983. Goldberg revisited: What’s in an author’s name. Sex Roles 9(3):387-390.

Steinpreis, Rhea, Katie A. Anders, and Dawn Ritzke. 1999. The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex Roles 41:509–528.

Trix, Frances and Carolyn Psenka. 2003. Exploring the color of glass: Letters of recommendation for female and male medical faculty. Discourse & Society 14:191–220.

Wenneras, Christine and Agnes Wold. 1997. Nepotism and sexism in peer-review.Nature 387:341–43.

 

This document is based on Searching for Excellence & Diversity: A Guide for Search Committee Chairs, a guide developed by the Women in Science & Engineering Leadership Institute (WISELI) at the University of Wisconsin Madison.