The findings of a collection of studies addressing a common research question can be visualized in terms of a forest plot, showing the effect sizes of the individual studies together with a corresponding confidence interval. A four-sided polygon (sometimes called a summary ‘diamond’) is often added to such a plot to depict the results from a meta-analysis pooling together the effect sizes,...
Effect sizes are commonly used in meta-analysis, as they provide a tool to summarize the results from each primary study in a common metric. In psychology and related fields, meta-analyses often involve integrating continuous variables measured with different scales across studies, which leads to using standardized mean differences as the effect size index. One of these indices is the...
The standardized mean change is widely recognized as a key effect size index in pretest-posttest one-group designs with quantitative dependent variables. Different parametric versions of this index are available, depending on the standardizer used to scale the mean difference into standardized units. In addition, various estimators can be applied to each parameter. This study used a Monte...
In meta-analysis, the Q statistic is traditionally used for testing the hypothesis of homogeneity of the parametric effect sizes of the set of studies. Several critiques have been posed to that test, especially when applied to the standardized mean difference (g). Among them, that the weights are based on estimated, not true, variances, that the variances of the estimates correlate with the...
Underpowered studies are ubiquitous in psychology and related disciplines. Meta-analysis can help alleviate this problem, increasing the statistical power by combining the results of a set of primary studies. However, this is not necessarily true when we use a random-effects model, which is currently the predominant approach when carrying out meta-analyses. In this study, we examined the...
Recent research has identified several limitations in traditional methods for conducting meta-analyses of reliability generalization, such as the lack of equivalence between total and subscale reliability indices and the violation of error independence assumptions. In response, multivariate statistical techniques have been developed to offer more accurate estimations of measurement...
