Evaluation of predictors’ relative importance: Methods and applications

Subjects: Psychology >> Statistics in Psychology submitted time 2022-07-26

Zhu, Xun Gu, Xin

Abstract： Evaluating predictors’ relative importance becomes increasingly important in the context of the explosion of high-dimensional data in psychological research. The key of relative importance analysis is to choose appropriate measures and inference approaches. Dominance analysis and relative weight are the recommended measures of relative importance among others. Bootstrap sampling is often used to infer the importance of a single variable or the difference between the importance of two variables. For three or more variables, Bayesian tests were recently developed to evaluate their importance orderings. Besides linear regression models, relative importance studies  have been extended to logistic regression models, structural equation models, and multilevel models. However, only continuous predictors are concerned in these models. Although relative importance analysis has been widely used in psychological studies, researchers may incorrectly select and interpret the importance measures. Therefore, a real data example is used to illustrate how the relative importance can be evaluated.

Peer Review Status:Awaiting Review

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