Power Approximations for Overall Average Effects in Meta-Analysis with Dependent Effect Sizes

Autor/inn/en	Vembye, Mikkel Helding; Pustejovsky, James Eric; Pigott, Therese Deocampo
Titel	Power Approximations for Overall Average Effects in Meta-Analysis with Dependent Effect Sizes
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 1, S.70-102 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Vembye, Mikkel Helding) ORCID (Pustejovsky, James Eric) ORCID (Pigott, Therese Deocampo)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221127379
Schlagwörter	Meta Analysis; Robustness (Statistics); Statistical Analysis; Models; Effect Size; Hypothesis Testing; Monte Carlo Methods; Research Methodology + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Widerstandsfähigkeit; Statistische Analyse; Analogiemodell; Hypothesenprüfung; Hypothesentest; Monte-Carlo-Methode; Research method; Forschungsmethode
Abstract	Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01