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ERIC Number: EJ997057
Record Type: Journal
Publication Date: 2013-Feb
Pages: 29
Abstractor: As Provided
Reference Count: 23
ISSN: ISSN-1076-9986
Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis
Luo, Wen; Azen, Razia
Journal of Educational and Behavioral Statistics, v38 n1 p3-31 Feb 2013
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"[squared], 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. (Contains 8 tables and 4 notes.)
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Publication Type: Journal Articles; Reports - Research
Education Level: Early Childhood Education; Elementary Education; Grade 1; Kindergarten; Primary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Early Childhood Longitudinal Survey