Problems with using odds ratios as effect sizes in binary logistic regression and alternative approaches


Categories: stats Tags: GLM OLS logistic-regression effect-size

This is my first (first author) journal article. We started writing it in Summer 2018, with first submission by November 2018. So my thinking has changed somewhat since then. There is a non-pay-walled version below, but the version of record is available at the Journal of Experimental Education, The best part of the review process was communicating with the editor, Professor Brian French - he was very kind.

Here’s the PDF and GitHub repository with simulation and figure reproduction code in R (very little comments).

Something I dislike in the paper is the hypothetical two-by-two contingency table repeated from Peng, Lee and Ingersoll (PLI, 2001, a well-cited review of logistic regression).1 Their hypothetical example had some inner city school children recommended to remedial reading. However, given that the example is hypothetical/invented, why invent inner city school children in need of saving?

Perhaps, PLI were analyzing a related real-life dataset when they wrote this. But what excuse do I have for choosing this example?2 None. I make these comments so methodologists can do better with our choice of examples, real or hypothetical. Given that the example datasets we use are sometimes ancillary to our work, we can be more thoughtful.

The summary of the paper is:

If we were to write this paper again, I would:

Minor interesting points:

  1. Peng, C.-Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3–14.
  2. I, not my co-authors, chose this example.
  3. Horrace, W. C., & Oaxaca, R. L. (2003, January 1). New Wine in Old Bottles: A Sequential Estimation Technique for the Lpm. Retrieved from

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