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Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

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  • Wen Luo
  • Razia Azen

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.

Suggested Citation

  • Wen Luo & Razia Azen, 2013. "Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 3-31, February.
    • Handle: RePEc:sae:jedbes:v:38:y:2013:i:1:p:3-31
    DOI: 10.3102/1076998612458319
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    References listed on IDEAS

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    1. Stan Lipovetsky & Michael Conklin, 2001. "Analysis of regression in game theory approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(4), pages 319-330, October.
    2. Niels Waller, 2008. "Fungible Weights in Multiple Regression," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 691-703, December.
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    2. Patrick Röhm & Andreas Köhn & Andreas Kuckertz & Hermann S. Dehnen, 2018. "A world of difference? The impact of corporate venture capitalists’ investment motivation on startup valuation," Journal of Business Economics, Springer, vol. 88(3), pages 531-557, May.

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