Multicollinearity and the use of regression analyses in discrimination litigation

For regression equations to be reliable and for inferences regarding discrimination to be accurately made, courts should concern themselves with multicollinearity, and not so-called "tainted" variables.

Abstract

Abstract Multicollinearity is a problem that can adversely affect the estimation of coefficients in regression equations. The types of regression models used as evidence in employment discrimination cases may be particularly susceptible to estimation problems resulting from multicollinearity, yet courts have in most instances failed to address this difficulty. This paper discusses the problems associated with multi‐ collinearity and suggests possible sources of the multicolli‐ nearity, particularly in the context of employment discrimination litigation. An academic example demonstrates how multicollinearity can be identified and corrected. Other possible techniques for curing the multicollinearity or reducing its effects are also presented. The related problem of “tainted” variables, often discussed by courts in employment discrimination litigation, is differentiated from the multicollinearity problem. For regression equations to be reliable and for inferences regarding discrimination to be accurately made, courts should concern themselves with multicollinearity, and not so‐called “tainting.”