Evaluating Models for Partially Clustered Designs

Autor/inn/en	Baldwin, Scott A.; Bauer, Daniel J.; Stice, Eric; Rohde, Paul
Titel	Evaluating Models for Partially Clustered Designs
Quelle	In: Psychological Methods, 16 (2011) 2, S.149-165 (17 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
DOI	10.1037/a0023464
Schlagwörter	Multivariate Analysis; Error of Measurement; Statistical Analysis; Statistical Bias; Models + Suchen Sie Ihr Suchwort? Multivariate Analyse; Messfehler; Statistische Analyse; Analogiemodell
Abstract	Partially clustered designs, where clustering occurs in some conditions and not others, are common in psychology, particularly in prevention and intervention trials. This article reports results from a simulation comparing 5 approaches to analyzing partially clustered data, including Type I errors, parameter bias, efficiency, and power. Results indicate that multilevel models adapted for partially clustered data are relatively unbiased and efficient and consistently maintain the nominal Type I error rate when using appropriate degrees of freedom. To attain sufficient power in partially clustered designs, researchers should attend primarily to the number of clusters in the study. An illustration using data from a partially clustered eating disorder prevention trial is provided. (Contains 6 figures, 3 tables and 2 footnotes.) (As Provided).
Anmerkungen	American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10