Beyond overall effects: A Bayesian approach to finding constraints in meta-analysis.

Abstract

Most meta-analyses focus on the behavior of meta-analytic means. In many cases, however, this mean is difficult to defend as a construct because the underlying distribution of studies reflects many factors, including how we as researchers choose to design studies. We present an alternative goal for meta-analysis. The analyst may ask about relations that are stable across all the studies. In a typical meta-analysis, there is a hypothesized direction (e.g., that violent video games increase, rather than decrease, aggressive behavior). We ask whether all studies in a meta-analysis have true effects in the hypothesized direction. If so, this is an example of a stable relation across all the studies. We propose 4 models: (a) all studies are truly null; (b) all studies share a single true nonzero effect; (c) studies differ, but all true effects are in the same direction; and (d) some study effects are truly positive, whereas others are truly negative. We develop Bayes factor model comparison for these models and apply them to 4 extant meta-analyses to show their usefulness. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 21 citations exist upstream.