Abstract
Many different approaches, almost all of which use some form of regression, have been used to study the issue of gender equity in university faculty salaries. One major point of contention in all of these approaches is whether faculty rank, which is university conferred, should be included as a predictor variable. Two illustrations are presented to demonstrate how omitting faculty rank as a predictor variable from gender equity studies of university faculty salaries can lead to incorrect conclusions concerning gender discrimination. The first illustration uses hypothetical data constructed so that there is no difference in salary due to gender. However, when faculty rank is not included as a predictor variable in the regression model, there is a significant difference in salary due to gender. The second illustration uses actual data from a study of gender equity in pay at Bowling Green State University. This data set is used to construct a new data set that is totally free of gender bias. When a regression model omitting faculty rank is fit to this gender bias-free data, again a significant difference in salary due to gender is present. Therefore, it is recommended that faculty rank be included as a predictor variable in any model used to study gender equity relating to salary.