Meta-analysis is highly relevant across all fields of empirical science, which invariably depend on the accumulation of empirical evidence over time, in order to support or to reject hypotheses and theories.
Its principal aim is to critically assess and to summarize the available data answering to a specific research hypothesis. Meta-analysis is the statistical approach for quantitatively combining and synthesizing the results of two or more empirical studies with identical or comparable research questions. They are especially suited for the illustration of data comparisons, patterns, trends, and relationships. Graphical displays allow to present complex statistical information in a comprehensive way. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.ĭata visualization is essential for the exploration of any empirical data and for the communication of statistical results in science in general.
This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. The majority of graphs (130, 62.5%) possessed a unique combination of graph features. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). One half of these have accrued within the past 10 years alone. We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic.
We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. Data-visualization methods are essential to explore and communicate meta-analytic data and results.