A Proposed Solution to the Problem with Using Completely Random Data to Assess the Number of Factors with Parallel Analysis

Autor/inn/en	Green, Samuel B.; Levy, Roy; Thompson, Marilyn S.; Lu, Min; Lo, Wen-Juo
Titel	A Proposed Solution to the Problem with Using Completely Random Data to Assess the Number of Factors with Parallel Analysis
Quelle	In: Educational and Psychological Measurement, 72 (2012) 3, S.357-374 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164411422252
Schlagwörter	Monte Carlo Methods; Factor Structure; Data Analysis; Psychometrics; Evaluation Research; Evaluation Methods; Comparative Analysis; Observation; Correlation; Factor Analysis + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Faktorenstruktur; Auswertung; Psychometry; Psychometrie; Evaluationsforschung; Beobachtung; Korrelation; Faktorenanalyse
Abstract	A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the study: number of observations, number of factors, number of measured variables, size of the factor loadings, and degree of correlation between factors. Based on the results, the revised parallel analysis method, using principal axis factoring and the 95th percentile eigenvalue rule, offers promise. (Contains 3 figures and 4 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10