Considerations for Statistical Analysis of Salary Equity Data

Music City Center 

Modern approaches to salary equity studies, such as proposed by Billard (2017), frequently use statistical ANCOVA-like models to quantify salary differences between and within subgroups of employees of interest. In this talk, we look at some of the seminal research by Scott (1977) and promoted extensively by Haignere (2002) under the name of ``white male only model.'' We show that it too fits within the ANCOVA framework, thereby facilitating understanding of the statistical properties of the ``white male only model'' approach and enabling comparisons with other modeling approaches. In particular, we show that inference for the WMO model fits within the ANCOVA linear model thereby rendering the calculation of standard errors, confidence intervals, and hypothesis tests immediate. We will also discuss statistical power for detecting group differences. Through simulations and examples, we provide guidance on which modeling techniques to use under different scenarios.