摘要：ABSTRACT Although it is 45 years since legislation made gender discrimination on university campuses illegal, salary inequities continue to exist today. The seminal work in studying the existence of salary inequities is that of the American Association of University Professors (AAUP), by Scott ( 1977 Scott, E. L. ( 1977 ), Higher Education Salary Evaluation Kit , Washington, DC : American Association of University Professors . [Google Scholar] ) and Gray ( 1980 Gray, M. W. ( 1980 ), Achieving Pay Equity on Campus , Washington, DC : American Association of University Professors . [Google Scholar] ). Subsequently, innumerable analyses based on versions of their multiple regression model have been published. Salary is the dependent variable and is modeled to depend on various independent predictor variables such as years employed. Often, indicator terms, for gender and/or discipline are included in the model as independent predicator 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. The present work draws 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.
