Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis. (February 2013)

• Record Type: Journal Article
• Authors: Luo, Wen; Azen, Razia
• Is Part Of: Journal of educational and behavioral statistics. Volume 38:Number 1(2013)
• Page Start: 3
• Page End: 31
• Publication Date: 2013-02
• Subjects: dominance analysis -- ECLS-K -- HLM -- relative importance of predictors; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998612458319
• Languages: English
• ISSNs: 1076-9986

Abstract

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- R 2, 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.