@article{ref1,
title="Beyond overall effects: a Bayesian approach to finding constraints in meta-analysis",
journal="Psychological methods",
year="2019",
author="Rouder, Jeffrey N. and Haaf, Julia M. and Davis-Stober, Clintin P. and Hilgard, Joseph",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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).<p /> <p>Language: en</p>",
language="en",
issn="1082-989X",
doi="10.1037/met0000216",
url="http://dx.doi.org/10.1037/met0000216"
}