Effect-size indices for dichotomized outcomes in meta-analysis

It is very common to find meta-analyses in which some of the studies compare 2 groups on continuous dependent variables and others compare groups on dichoto-mized variables. Integrating all of them in a meta-analysis requires an effect-size index in the same metric that can be applied to both types of outcomes. In this article, the performance in terms of bias and sampling variance of 7 different effect-size indices for estimating the population standardized mean difference from a 2 × 2 table is examined by Monte Carlo simulation, assuming normal and nonnormal distributions. The results show good performance for 2 indices, one based on the probit transformation and the other based on the logistic distribution. In the last 20 years, meta-analysis has become a very popular and useful research methodology to in-tegrate the results of a set of empirical studies about a given topic. To carry out a meta-analysis an effect-size index has to be selected to translate the results of every study into a common metric. When the focus of a study is to compare the per-formance of two groups (e.g., treated vs. control, male vs. female, trained vs. nontrained) on a continuous dependent variable, the effect-size index most usually applied is the standardized mean difference, d, de-fined as the difference between the means of the two groups divided by a within-group standard deviation estimate. Substantial meta-analytic literature has been devoted to showing the properties of this index, its sampling variance, how to obtain confidence intervals on the weighted mean effect size obtained from it, its statistical significance, homogeneity tests, and how to search for variables that moderate it (Cooper, 1998