Abstract
Conditional means models such as linear regression is a conventional method that researchers
regularly employ to examine relationships between personality traits and counterproductive
work behavior. However, this method has several shortcomings limiting its utility. Quantile
regression analysis better accounts for many of these limitations. This study investigates
narrow personality traits as predictors of counterproductive workplace behavior using quantile
methods with 952 working adults. Results show that quantile regression analysis provides a
more nuanced representation of the relationship that personality traits have with
counterproductive workplace behavior. We demonstrate that the conditional mean (i.e.,
regression coefficient) observed with standard ordinary least squares regression overestimates
regression parameters at low levels of counterproductive work behavior, and underestimates it
at high levels. The findings from this study suggest that reliance on conditional means models
for the prediction of CWB may have resulted in an incomplete understanding and under
appreciation of personality’s actual value for the prediction of workplace deviance.