Using Monte Carlo Experiments to Select Meta-Analytic Estimators

Autor/inn/en	Hong, Sanghyun; Reed, W. Robert
Titel	Using Monte Carlo Experiments to Select Meta-Analytic Estimators
Quelle	In: Research Synthesis Methods, 12 (2021) 2, S.192-215 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hong, Sanghyun) ORCID (Reed, W. Robert)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1467
Schlagwörter	Monte Carlo Methods; Meta Analysis; Research Methodology; Experiments; Sample Size; Effect Size; Medicine; Psychology; Social Sciences; Error of Measurement; Research Design; Publications; Bias + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Meta-analysis; Metaanalyse; Research method; Forschungsmethode; Erprobung; Medizin; Psychologie; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Messfehler; Forschungsdesign
Abstract	The purpose of this study is to show how Monte Carlo analysis of meta-analytic estimators can be used to select estimators for specific research situations. Our analysis conducts 1620 individual experiments, where each experiment is defined by a unique combination of sample size, effect size, effect size heterogeneity, publication selection mechanism, and other research characteristics. We compare 11 estimators commonly used in medicine, psychology, and the social sciences. These are evaluated on the basis of bias, mean squared error (MSE), and coverage rates. For our experimental design, we reproduce simulation environments from four recent studies. We demonstrate that relative estimator performance differs across performance measures. Estimator performance is a complex interaction of performance indicator and aspects of the application. An estimator that may be especially good with respect to MSE may perform relatively poorly with respect to coverage rates. We also show that the size of the meta-analyst's sample and effect heterogeneity are important determinants of relative estimator performance. We use these results to demonstrate how these observable characteristics can guide the meta-analyst to choose the most appropriate estimator for their research circumstances. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01