Gender and Letters of Recommendation: Agentic and Communal Differences Juan Madera, Mikki Hebl, and Randi Martin Rice University ABSTRACT BACKROUND Letters of recommendation were analyzed to assess gender differences in agentic and communal attributions. Women were described more than men as affectionate, warm, and kind. Men were described more than women as ambitious, dominant, and self-confident. Letters written for women contained more references to their physical appearance than for men. Letters of recommendation are important and commonly used selection tools. Data suggests that they are the most important criterion used to screen applicants when hiring psychology faculty (Sheehan, McDevitt, & Ross, 1998). The purpose of this research was to examine gender differences in letters of recommendation using objective methods (i.e., language content analysis) and including indicators of productivity as control variables (e.g., honors and teaching experiences). Drawing from social role theory of sex differences (Eagly, Wood, & Diekman, 2000), we assessed differences in agency and communal attributions in letters of recommendation for psychology faculty. Thus, men are expected to engage in a masculine gender role that reflects agentic qualities and women are expected to engage in a feminine gender role that reflects communal qualities (Wood & Eagly, 2002). H1: Men are more likely to be described in agentic terms in letters of recommendation than are women. H2. Women are more likely to be described in communal terms in letters of recommendation than are men. H3. Letters written for women are more likely to mention physical appearance than are letters written for men. METHOD Sample The sample consisted of 685 recommenders and 194 applicants for eight junior faculty positions at a southern university in the United States from 1998 to % (n = 89) of the applicants were female and 54% (n = 103) were male 30% (n = 193) of the recommenders were female and 70% (n = 477) were male. RESULTS DISCUSSION Results confirmed our hypotheses demonstrating that female applicants are more likely to be described with communal terms (e.g., affectionate, warm, kind, and nurturing) than male applicants. Male applicants were more likely to be described in agentic terms (e.g., ambitious, dominant, and self-confident) than female applicants. The results have implications for the advancement of women, because research shows that agency is linked with advancement in the workplace. Dependent Measures (Linguistic Category) Example of terms Communal AdjectivesAffectionate, helpful, kind, sympathetic, sensitive, nurturing, agreeable, and caring. Agentic AdjectivesAssertive, confident, aggressive, ambitious, dominant, forceful, and intellectual. Social-Communal OrientationHusband, wife, kids, babies, brothers, children, family, and they. Grindstone AdjectivesHardworking, conscientious, dependable, meticulous Physical-BodyArms, breast, eyes, face, hips, hair, muscle, nails, pregnancy, and mouth. We ran a two-way (Applicant Gender x Letter Writer Gender) MANCOVA Control variables: years in graduate school, the number of total publications, the number of first author publications, the number of honors, the number of post-doc years, the applied position, and the number of courses taught. Dependent Measures (Linguistic Category) Main Effects of Applicant Gender Communal Adjectives F(1, 470) = 6.95, p <.01, η 2 =.015 Women (M =.80, SD =.03) > Men (M =.65, SD =.03). Agentic AdjectivesF(1, 470) = 11.19, p <.01, η 2 =.023 Men (M =.89, SD =.03) > Women (M =.71, SD =.03). Social-Communal Orientation F(1, 470) = 7.58, p <.01, η 2 =.016 Women (M = 8.75, SD =.13) > Men (M = 8.23, SD =.13). Grindstone AdjectivesF(1, 470) = 2.13, n.s.; Women (M =.14) Men (M =.14) Physical-BodyF(1, 470) = 5.89, p <.01, η 2 =.012 Women (M =.34, SD =.03) > (M =.21, SD =.03). Procedure This study used a computer text analysis program, the Linguistic Inquiry and Word Count program (LIWC; Pennebaker, Francis, & Booth, 2001) to examine gender differences in communal and agentic attributions. The LIWC program analyzes text files and computes the percentage of words from that file that fall into each linguistic category. We developed five linguistic categories: communal adjectives, agentic adjectives, social-communal orientation, grindstone adjectives, and physical body terms.