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The Use of Regularization to Detect Racial Inequities in Pay Equity Studies : = An Empirical Study and Reflections on Regulation Methods.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	The Use of Regularization to Detect Racial Inequities in Pay Equity Studies :/
Reminder of title:	An Empirical Study and Reflections on Regulation Methods.
Author:	Pena, Christopher M.
Description:	1 online resource (137 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Social research. -
Online resource:	click for full text (PQDT)
ISBN:	9798381159936

The Use of Regularization to Detect Racial Inequities in Pay Equity Studies : = An Empirical Study and Reflections on Regulation Methods.
Pena, Christopher M.

The Use of Regularization to Detect Racial Inequities in Pay Equity Studies :An Empirical Study and Reflections on Regulation Methods. - 1 online resource (137 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--University of Denver, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381159936Subjects--Topical Terms:

1179269
Social research.
Subjects--Index Terms:

LASSO regressionIndex Terms--Genre/Form:

554714
Electronic books.

The Use of Regularization to Detect Racial Inequities in Pay Equity Studies : = An Empirical Study and Reflections on Regulation Methods.
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504 $a Includes bibliographical references
520 $a 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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