interpreting the impact of either gender or percentage of females among assistant professors on tenure decisions, we did not attempt to untangle other associations. Figures 5-1 and 5-2 show the estimated probability of a positive tenure decision for men and women as a function of the percentage of tenure-eligible faculty who are female and as the proportion of female faculty in the department. To compute the probabilities in Figure 5-1, we held all other factors constant. Similarly, to compute the probabilities in Figure 5-2, we held the percentage of women among tenure-eligible faculty constant at 10 percent (two outer curves) or at 50 percent (two inner curves) for men and women.
Discipline, stop-the-clock policies, and overall departmental size were not associated with the probability of a positive tenure decision for either male or female faculty.
As a final comment, we note that when an interaction between a discrete covariate and other covariates in the model is present and the outcome variable is discrete (as is the case in our logistic regression model for tenure decision), unequal residual variances in each of the levels of the discrete covariate can have a profound effect on inference. Unequal group variances inflate the size of the estimated regression coefficients, thus introducing a bias in predictions relevant to differential outcomes for men and women. Therefore, trying to determine the effect of a covariate on, for example, male and female faculty cannot be done in
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"5 Gender Differences in Tenure and Promotion."
Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty.
Washington, DC: The National Academies Press, 2010.
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