Determining predictor importance in hierarchical linear models using dominance analysis
Dominance analysis (DA) is a method used to evaluate the relative importance of predictors
that was originally proposed for linear regression models. This article proposes an extension
of DA that allows researchers to determine the relative importance of predictors in
hierarchical linear models (HLM). Commonly used measures of model adequacy in HLM (ie,
deviance, pseudo-R 2, and proportional reduction in prediction error) were evaluated in
terms of their appropriateness as measures of model adequacy for DA. Empirical examples …
that was originally proposed for linear regression models. This article proposes an extension
of DA that allows researchers to determine the relative importance of predictors in
hierarchical linear models (HLM). Commonly used measures of model adequacy in HLM (ie,
deviance, pseudo-R 2, and proportional reduction in prediction error) were evaluated in
terms of their appropriateness as measures of model adequacy for DA. Empirical examples …
Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in HLM (i.e., deviance, pseudo-R2, and proportional reduction in prediction error) were evaluated in terms of their appropriateness as measures of model adequacy for DA. Empirical examples were used to illustrate the procedures for comparing the relative importance of Level-1 predictors and Level-2 predictors in a person-in-group design. Finally, a simulation study was conducted to evaluate the performance of the proposed procedures and develop recommendations.
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