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Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

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  • Razia Azen
  • Nicole Traxel

Abstract

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R 2 analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A simulation study, using both simple random sampling from a known population and bootstrap sampling from a single (parent) random sample, was performed to evaluate the bias, sampling distribution, and confidence intervals of quantitative dominance measures as well as the reproducibility of qualitative dominance measures. Results indicated that the bootstrap procedure is feasible and can be used in applied research to generalize logistic regression dominance analysis results to the population of interest. The procedures for determining and interpreting the general dominance of predictors in a logistic regression context are illustrated with an empirical example.

Suggested Citation

  • Razia Azen & Nicole Traxel, 2009. "Using Dominance Analysis to Determine Predictor Importance in Logistic Regression," Journal of Educational and Behavioral Statistics, , vol. 34(3), pages 319-347, September.
    • Handle: RePEc:sae:jedbes:v:34:y:2009:i:3:p:319-347
    DOI: 10.3102/1076998609332754
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    2. Mario Morger, 2013. "What Do Immigrants Value Most About Switzerland? Evidence of the Relative Importance of Income Taxes," CESifo Working Paper Series 4134, CESifo.
    3. Christian Deutscher & Oliver Gürtler & Joachim Prinz & Daniel Weimar, 2017. "The Payoff To Consistency In Performance," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 1091-1103, April.

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