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An Additional Measure of Overall Effect Size for Logistic Regression Models

Author

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  • Jeff Allen
  • Huy Le

Abstract

Users of logistic regression models often need to describe the overall predictive strength, or effect size, of the model’s predictors. Analogs of R 2 have been developed, but none of these measures are interpretable on the same scale as effects of individual predictors. Furthermore, R 2 analogs are not invariant to the base rate (overall proportion of successes), making it difficult to compare effect sizes across data sets. The authors propose a measure of overall effect size that is interpretable on the same scale as effects of individual predictors and is invariant to the base rate. They explore the properties of the overall odds ratio and illustrate its use through an example. They also provide interpretive guidance and illustrate how statistical software can be used to compute the proposed measure.

Suggested Citation

  • Jeff Allen & Huy Le, 2008. "An Additional Measure of Overall Effect Size for Logistic Regression Models," Journal of Educational and Behavioral Statistics, , vol. 33(4), pages 416-441, December.
  • Handle: RePEc:sae:jedbes:v:33:y:2008:i:4:p:416-441
    DOI: 10.3102/1076998607306081
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    Cited by:

    1. Claudio Conversano & Elise Dusseldorp, 2017. "Modeling Threshold Interaction Effects Through the Logistic Classification Trunk," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 399-426, October.
    2. Hanne van der Iest & Jacob Dijkstra & Frans N. Stokman, 2011. "Not ‘Just the two of us’: Third party externalities of social dilemmas," Rationality and Society, , vol. 23(3), pages 347-370, August.

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