Weighted Histogram for a Meta-Analysis (WHaM): Descriptive graphics using R Shiny
Year: 2022
Type: preprint
Abstract: For over three decades in psychology, meta-analysis has been a popular methodological tool for summarizing effect sizes within a given research domain. Statistical meta-analytic summaries typically reflect a mean and associated variance (heterogeneity) estimate, and visual summaries of constituent effect sizes typically use forest and funnel plots. Although these plots are useful, they do not reveal the shape of the distribution of effect sizes directly. To remedy this gap, we offer a weighted histogram that is more interpretable and useful when depicting the distribution of effect sizes and their associated sampling error variance. In support of the weighted histogram, we reviewed a hundred of the most recent meta-analyses published in American Psychological Association (APA) journals and in Journal of Applied Psychology, many popular books in psychology on meta-analysis, and several software programs and packages for conducting meta-analysis—all of which suggest a strong need for a more succinct and effective way to visualize the distribution and accuracy of effects included in a meta-analysis (i.e., a weighted histogram). Importantly, we also offer a user-friendly interactive online app (developed using R Shiny) that allows meta-analytic researchers to easily create their own publication-ready weighted histograms.
Source: PsyArXiv (OSF Preprints)
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4283385010