Date of Award
Morgridge College of Education, Research Methods and Information Science, Research Methods and Statistics
P. Bruce Uhrmacher
LASSO regression, Pay equity, QuantCRiT, Regression, Ridge regression
Since the late 1970s, multiple linear regression has been the preferred method for identifying discrimination in pay. An empirical study on this topic was conducted using quantitative critical methods. A literature review first examined conflicting views on using multiple linear regression in pay equity studies. The review found that multiple linear regression is used so prevalently in pay equity studies because the courts and practitioners have widely accepted it and because of its simplicity and ability to parse multiple sources of variance simultaneously. Commentaries in the literature cautioned about errors in model specification, the use of tainted variables, and the lack of causal explanations. An empirical study comparing multiple linear regression, ridge regression, and LASSO regression models on a university employment data set was conducted next, focusing on racial inequity and methods informed by the literature review. The study results showed that while multiple linear regression yielded the highest coefficient of determination and the lowest mean squared error, LASSO regression yielded the highest predictive accuracy as measured by the standard error of the estimate. The study discovered the presence of racial inequity for Hispanic and Black employees at the university, including racial inequity for women in these groups.
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Christopher M. Peña
Received from ProQuest
Peña, Christopher M., "The Use of Regularization to Detect Racial Inequities in Pay Equity Studies: An Empirical Study and Reflections on Regulation Methods" (2023). Electronic Theses and Dissertations. 2350.
Social research, Statistics