The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model strength. The authors also illustrate and test a jackknife procedure to correct for the bias in the OOR and estimate its standard error. An example applying the OOR to evaluate logistic regression models predicting organizational turnover is provided. The authors discuss implications and offer recommendations for using the OOR to quantify and compare the effectiveness of logistic regression models in applied research. Keywords effect size, logistic regression, odds ratio, R square Many important criterion variables in educational and organizational research are dichotomous in nature (e.g., college dropout, turnover), and thus require multiple logis-tic regression (MLOGR) analysis. Unfortunately, users of the method often find them-selves disadvantaged by lack of a meaningful index to describe the overall strength o