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) and Gray (1980). 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.

中文翻译：

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按性别和学科研究薪资差异

摘要尽管立法将大学校园内的性别歧视定为非法行为已有45年，但如今工资不平等现象仍然存在。研究工资不平等现象的开创性工作是斯科特（1977）和格雷（1980）的美国大学教授协会（AAUP）的工作。随后，根据其多元回归模型的版本进行了无数分析。薪水是因变量，其模型被建模为取决于各种独立的预测变量，例如受雇年限。通常，用于性别和/或学科的指标术语作为独立谓语变量包含在模型中。不幸的是，这些研究中的许多都不是基础统计科学的基础。最明显的遗漏是在需要时未能在模型中包含预测变量交互作用项的指标。当前的工作提请人们注意错误使用这些模型的更广泛的含义，以及当它们没有建立在适当的合理统计框架之上时所带来的困难。另一个问题包括是否包含“偏见”的预测变量，这些变量本身就存在性别偏见，最有争议的是排名的（直观）选择。因此，简要介绍了此问题。不幸的是，这表明今天的排名似乎仍然是一个受污染的变量。另一个问题包括是否包含“偏见”的预测变量，这些变量本身就存在性别偏见，最有争议的是排名的（直观）选择。因此，简要介绍了此问题。不幸的是，这表明今天的排名似乎仍然是一个受污染的变量。另一个问题包括是否包含“偏见”的预测变量，这些变量本身就存在性别偏见，最有争议的是排名的（直观）选择。因此，简要介绍了此问题。不幸的是，这表明今天的排名似乎仍然是一个受污染的变量。