Multiple linear regression (MLR) remains a mainstay analysis in organizational research, yet
intercorrelations between predictors (multicollinearity) undermine the interpretation of MLR …

One of the most difficult tasks facing industrial‐organizational psychologists is evaluating the
importance of variables, especially new variables, to be included in the prediction of some …

Introduction The construction industry has hit a plateau in terms of safety performance.
Safety climate is regarded as a leading indicator of safety performance; however, relatively …

Linear regression models are an important statistical tool in evolutionary and ecological
studies. Unfortunately, these models often yield some uninterpretable estimates and …

This article advocates for the wider use of relative importance indices as a supplement to
multiple regression analyses. The goal of such analyses is to partition explained variance …

Reviews of statistical procedures (eg, Bangert & Baumberger, 2005; Kieffer, Reese, &
Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common …

Dominance analysis is a common method applied to statistical models to determine the
importance of independent variables. In this article, I describe two community-contributed …

We used univariate and multivariate relative weight analysis to assess the relative
importance of a new job attitude (employee engagement), several longstanding job attitudes …

New employees can accelerate and optimize their socialization by behaving proactively,
although the key behaviors vary across studies. Recent research suggests that newcomer …

Purpose The purpose of this study is to better understand factors related to turnover intention
(TI) and job satisfaction (JS) in the information technology and engineering sectors …