Journal Article

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

Azen, Razia, Luo, Wen

2013

Overview

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.

• Publisher: SAGE Publications,American Educational Research Association
• Subject: Academic Achievement / Algorithms / Children / Comparative Analysis / Early Childhood Longitudinal Survey / Goodness of Fit / Grade 1 / Hierarchical Linear Modeling / Kindergarten / Learning / Linear regression / Longitudinal Studies / Mathematical knowledge / Mathematical models / Modeling / Motor ability / Multilevel models / Outcome variables / Predictor Variables / Reading Achievement / Regression (Statistics) / Regression analysis / Regression coefficients / School Readiness / Simulation / Statistical Analysis / Statistical variance / Young Children