Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models

Autor/in	Xia, Yan
Titel	Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models
Quelle	In: Educational and Psychological Measurement, 81 (2021) 6, S.1143-1171 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Xia, Yan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164421992836
Schlagwörter	Statistical Analysis; Factor Analysis; Accuracy; Goodness of Fit + Suchen Sie Ihr Suchwort? Statistische Analyse; Faktorenanalyse
Abstract	Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser's rule, minimum average partial, sequential X[superscript 2], and sequential root mean square error of approximation, comparative fit index, and Tucker-Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing-fitting parsimonious model with "K" factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a "K"-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model-data misfit that can be tolerated. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01