Date of Award

8-1-1986

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Teaching & Learning

Abstract

Studies have been done determining wage disparity between male and female faculty in higher education. Each methodology indicates that it is the most accurate as it reflects the existence of wage gaps.

The first study used multiple regression analysis with salary the dependent variable and the independent variables of sex, birth year, highest degree, race, and college. Variables with a significant relationship to salary revealed by t-values were sex, birth year, master’s degree, and three colleges.

The second study used multiple regression analysis with salary the dependent variable and the independent variables of degree year, publications, time in administration, time in teaching, sex, rank, highest degree, and Hegis code. Variables with a significant relationship to salary revealed by t-values we - time in administration, rank, and four Hegis codes.

The third study used multidiscriminant analysis with sex as the dependent variable and independent variables of age in years, number of advisees, salary, years in rank, and years at institution. Salary was the only variable which was significant in its relationship to sex.

Using specific formulae from these studies which eliminated different existing variables, two questions were addressed: How do competing methodologies which are used to determine pay inequity based on sex in higher education compare when applied to the same data base? and What are the effects of the elimination of different variables in the competing methodologies on the reflection of pay inequity in salaries in higher education when applied to the same data base?

The conclusions based on this study were that the choice of methodology used to look at pay inequity depends on the criteria one uses to judge its validity or usefulness. The variables market factor and rank must be included in the methodology used to determine pay inequity. Care must be taken when using statistics to determine pay inequity to prevent advocacy for a particular position.

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