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Abstract:
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Although it is more than forty-five years since legislation made gender discrimination on university campuses illegal, salary inequities continue to exist today. Innumerable analyses based on multiple regression models have been published. Salary is the dependent variable and is modeled to depend on various independent predictor variables such as years employed. Indicator terms, for gender and/or discipline are included in the model as independent predictor variables. Unfortunately, many of these studies are not well grounded in basic statistical science. The most glaring omission is the failure to include indicator by predictor interaction terms in the model when required. We draw attention to the broader implications of using these models incorrectly, and the difficulties that ensue when they are not built on an appropriate sound statistical framework. Another issue surrounds the inclusion of “tainted” predictor variables that are themselves gender- biased, the most contentious being the (intuitive) choice of rank. Therefore, a brief look at this issue is included; unfortunately, it is shown that rank still today seems to persist as a tainted variable.
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