Quantile Regression and Its Application in Inequality Studies

Abstract

Organizational researchers have long used the linear regression model (LRM) as the fundamental method of exploring the relationships between continuous dependent variables and predictor variables. However, rarely do scholars find that their data satisfy the assumption of the LRM. Heavy-tailed distributions occur often in empirical settings, making it difficult for organizational scholars to investigate the nuanced relationships between dependent and predictor variables. The quantile regression model (QRM) is an alternative technique that can help organizational scholars overcome the hurdles associated with using LRM. This study aims to introduce QRM to a broad audience of organizational researchers, first by comparing LRM and QRM and then thoroughly exploring the strengths and weaknesses of both models. Following this exploration, this paper will explain how organizational scholars can systematically apply QRM in concrete empirical settings. Finally, the author will review empirical studies to illustrate how QRM can be further applied to organizational research. This introduction and analysis reveals that QRM is a comprehensive strategy capable of helping organizational researchers obtain a complete regression picture for data with heavy-tailed distributions.