The Misspecification of the Covariance Structures in Multilevel Models for Single-Case Data: A Monte Carlo Simulation Study

Autor/inn/en	Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim
Titel	The Misspecification of the Covariance Structures in Multilevel Models for Single-Case Data: A Monte Carlo Simulation Study
Quelle	In: Journal of Experimental Education, 84 (2016) 3, S.473-509 (37 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2015.1065216
Schlagwörter	Quantitative Daten; Hierarchical Linear Modeling; Monte Carlo Methods; Computation; Statistical Bias; Statistical Analysis; Simulation; Error of Measurement; Meta Analysis + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Statistische Analyse; Simulation program; Simulationsprogramm; Messfehler; Meta-analysis; Metaanalyse
Abstract	The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest lies in the between-study variance estimate, including at least 30 studies is warranted. Modeling covariance does not result in less biased between-study variance estimates as the between-study covariance estimate is biased. When the research interest lies in the between-case covariance, the model including covariance results in unbiased between-case variance estimates. The three-level model appears to be less appropriate for estimating between-study variance if fewer than 30 studies are included. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01