Industrial and organizational psychologists frequently use regression to predict and explain organizational outcomes (e.g., employee job performance, team cohesion, organizational culture). However, given that most organizational phenomena are complex and multiply determined, regression models used to predict and explain these phenomena include more than one predictor. In such cases, researchers frequently want to know which of the multiple predictors in a regression model are most influential in predicting and explaining a given outcome. Relative importance analysis is designed to help researchers answer this question and allows comparing predictors in multiple regression models in terms of their contributions to explaining variance in the outcome variables.

Historically, predictor importance in regression models has been assessed using zero-order correlations between predictors and outcome variables (r), standardized regression weights (β), ...

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