Proportion of Indicator Common Variance Due to a Factor as an Effect Size Statistic in Revised Parallel Analysis

Autor/inn/en	Xia, Yan; Green, Samuel B.; Xu, Yuning; Thompson, Marilyn S.
Titel	Proportion of Indicator Common Variance Due to a Factor as an Effect Size Statistic in Revised Parallel Analysis
Quelle	In: Educational and Psychological Measurement, 79 (2019) 1, S.85-107 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164418754611
Schlagwörter	Effect Size; Factor Analysis; Hypothesis Testing; Psychometrics; Statistics; Statistical Bias + Suchen Sie Ihr Suchwort? Faktorenanalyse; Hypothesenprüfung; Hypothesentest; Psychometry; Psychometrie; Statistik
Abstract	Past research suggests revised parallel analysis (R-PA) tends to yield relatively accurate results in determining the number of factors in exploratory factor analysis. R-PA can be interpreted as a series of hypothesis tests. At each step in the series, a null hypothesis is tested that an additional factor accounts for zero common variance among measures in the population. Integration of an effect size statistic--the proportion of common variance (PCV)--into this testing process should allow for a more nuanced interpretation of R-PA results. In this article, we initially assessed the psychometric qualities of three PCV statistics that can be used in conjunction with principal axis factor analysis: the standard PCV statistic and two modifications of it. Based on analyses of generated data, the modification that considered only positive eigenvalues [(mathematical characters omitted)] overall yielded the best results. Next, we examined PCV using minimum rank factor analysis, a method that avoids the extraction of negative eigenvalues. PCV with minimum rank factor analysis generally did not perform as well as [mathematical characters omitted], even with a relatively large sample size of 5,000. Finally, we investigated the use of [mathematical characters omitted] in combination with R-PA and concluded that practitioners can gain additional information from [mathematical characters omitted] and make more nuanced decision about the number of factors when R-PA fails to retain the correct number of factors. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01