Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

Autor/inn/en	Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato
Titel	Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs
Quelle	In: Educational and Psychological Measurement, 67 (2007) 3, S.410-432 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164406294777
Schlagwörter	Sample Size; Robustness (Statistics); Monte Carlo Methods; Multivariate Analysis; Comparative Analysis; Correlation; Heterogeneous Grouping; Homogeneous Grouping + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Monte-Carlo-Methode; Multivariate Analyse; Korrelation; Homogene Gruppierung; Niveaugruppierung; Streaming
Abstract	This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the Kenward-Roger solution available in SAS PROC MIXED. The authors found that, overall, the MBF procedure appeared to be the least sensitive to the factors examined in the present study; however, this is not necessarily the case for all data sets. As the results show, for tests of the between-subjects main effect, the MBF approach outperformed the mixed-model method when fitting either a patterned or nonpatterned covariance structure. But for tests of within-subjects effects, its Type I error control advantages decrease. Sample SAS Commands are appended. (Contains 5 tables.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10