Multivariate Analysis of Variance for Multilevel Data: A Simulation Study Comparing Methods

Autor/in	Finch, W. Holmes
Titel	Multivariate Analysis of Variance for Multilevel Data: A Simulation Study Comparing Methods
Quelle	In: Journal of Experimental Education, 90 (2022) 1, S.173-190 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2020.1718058
Schlagwörter	Multivariate Analysis; Hypothesis Testing; Structural Equation Models; Hierarchical Linear Modeling; Statistical Analysis + Suchen Sie Ihr Suchwort? Multivariate Analyse; Hypothesenprüfung; Hypothesentest; Statistische Analyse
Abstract	Multivariate analysis of variance (MANOVA) is widely used to test the null hypothesis of equal multivariate means across 2 or more groups. MANOVA rests upon an assumption that error terms are independent of one another, which can be violated if individuals are clustered or nested within groups, such as schools. Ignoring such nesting can result in Type I error inflation, biased parameter estimates, and underestimated standard errors. This simulation study compared two approaches for testing the MANOVA null hypothesis of no group mean differences, when data come from a multilevel structure, under a variety of conditions. Results indicated that the multilevel MANOVA method of Snijders and Bosker, as well as an approach based on multilevel structural equation modeling (SEM) controlled Type I error under most conditions. In addition, the SEM based method yielded slightly higher power than did the multilevel MANOVA, under some conditions. Implications for practice are discussed. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01