Out of Shape: The Implications of (Extremely) Nonnormal Dependent Variables

Abstract

Organizational researchers have increasingly noted the problems associated with nonnormal dependent variable distributions. Most of this scholarship focuses on variables with positive values and long tails, such as employee performance, capital expenses, and assets. However, scholars frequently test organizational theories using dependent variables that include negative values, which is perhaps most prominently the case as it relates to measures of firm performance. Over the course of two studies, we investigate the implications of such nonnormally distributed dependent variables in organizational research. In Study 1, we examine the nonnormality of firm performance measures and uncover extreme levels of skewness and kurtosis that vary substantially across measures, samples, and years. We also illustrate that many transformations scholars use to address nonnormality are ineffective. In Study 2, we create simulations that seek to mirror these distributions, and we find that such extreme nonnormality reduces efficiency and increases Type II errors with most statistical approaches. Our analyses also reveal the effectiveness of quantile regression when modeling dependent variables that exhibit the nonnormal distributions often found in organizational research.