Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues

Many meta-analysts incorrectly use correlations or standardized mean difference statistics to compute effect sizes on dichotomous data. Odds ratios and their logarithms should almost always be preferred for such data This article reviews the issues and shows how to use odds ratios in meta-analytic data, both alone and in combination with other effect size estimators. Examples illustrate procedures for estimating the weighted average of such effect sizes and methods for computing variance estimates, confidence intervals, and homogeneity tests. Descriptions of fixed- and random-effects models help determine whether effect sizes are functions of study characteristics, and a random-effects regression model, previously unused for odds ratio data, is described. Although all but the latter of these procedures are already widely known in areas such as medicine and epidemiology, the absence of their use in psychology suggests a need for this description.