Accounting for heterogeneity via random-effects models and moderator analyses in meta-analysis

To conduct a meta-analysis, one needs to express the results from a set of related studies in terms of an outcome measure, such as a standardized mean difference, correlation coefficient, or odds ratio. The observed outcome from a single study will differ from the true value of the outcome measure because of sampling variability. The observed outcomes from a set of related studies measuring the same outcome will, therefore, not coincide. However, one often finds that the observed outcomes differ more from each other than would be expected based on sampling variability alone. A likely explanation for this phenomenon is that the true values of the outcome measure are heterogeneous. One way to account for the heterogeneity is to assume that the heterogeneity is entirely random. Another approach is to examine whether the heterogeneity in the outcomes can be accounted for, at least in part, by a set of study-level variables describing the methods, procedures, and samples used in the different studies. The purpose of the present paper is to discuss these different approaches with particular emphasis on the interpretation of the results and practical issues. © 2007 Hogrefe & Huber Publishers.status: submitte