Brief Research Report: Bayesian versus REML Estimations with Noninformative Priors in Multilevel Single-Case Data

Autor/inn/en	Baek, Eunkyeng; Beretvas, S. Natasha; Van den Noortgate, Wim; Ferron, John M.
Titel	Brief Research Report: Bayesian versus REML Estimations with Noninformative Priors in Multilevel Single-Case Data
Quelle	In: Journal of Experimental Education, 88 (2020) 4, S.698-710 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2018.1527280
Schlagwörter	Bayesian Statistics; Intervention; Case Studies; Monte Carlo Methods; Inferences; Comparative Analysis; Hierarchical Linear Modeling + Suchen Sie Ihr Suchwort? Case study; Fallstudie; Case Study; Monte-Carlo-Methode; Inference; Inferenz
Abstract	Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01