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Autor/inn/enPreacher, Kristopher J.; Zhang, Guangjian; Kim, Cheongtag; Mels, Gerhard
TitelChoosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective
QuelleIn: Multivariate Behavioral Research, 48 (2013) 1, S.28-56 (29 Seiten)
PDF als Volltext Verfügbarkeit 
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0027-3171
DOI10.1080/00273171.2012.710386
SchlagwörterFactor Analysis; Models; Selection; Goodness of Fit; Generalizability Theory; Selection Criteria; Behavior Problems; Questionnaires; High School Students; Colorado
AbstractA central problem in the application of exploratory factor analysis is deciding how many factors to retain ("m"). Although this is inherently a model selection problem, a model selection perspective is rarely adopted for this task. We suggest that Cudeck and Henly's (1991) framework can be applied to guide the selection process. Researchers must first identify the analytic goal: identifying the (approximately) correct "m" or identifying the most replicable "m". Second, researchers must choose fit indices that are most congruent with their goal. Consistent with theory, a simulation study showed that different fit indices are best suited to different goals. Moreover, model selection with one goal in mind (e.g., identifying the approximately correct "m") will not necessarily lead to the same number of factors as model selection with the other goal in mind (e.g., identifying the most replicable "m"). We recommend that researchers more thoroughly consider what they mean by "the right number of factors" before they choose fit indices. (Contains 2 tables, 5 figures, and 5 footnotes.) (As Provided).
AnmerkungenPsychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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