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ERIC Number: EJ1086573
Record Type: Journal
Publication Date: 2016-Feb
Pages: 17
Abstractor: As Provided
ISSN: ISSN-0013-1644
Accuracy of Revised and Traditional Parallel Analyses for Assessing Dimensionality with Binary Data
Green, Samuel B.; Redell, Nickalus; Thompson, Marilyn S.; Levy, Roy
Educational and Psychological Measurement, v76 n1 p5-21 Feb 2016
Parallel analysis (PA) is a useful empirical tool for assessing the number of factors in exploratory factor analysis. On conceptual and empirical grounds, we argue for a revision to PA that makes it more consistent with hypothesis testing. Using Monte Carlo methods, we evaluated the relative accuracy of the revised PA (R-PA) and traditional PA (T-PA) methods for factor analysis of tetrachoric correlations between items with binary responses. We manipulated five data generation factors: number of observations, type of factor model, factor loadings, correlation between factors, and distribution of thresholds. The R-PA method tended to be more accurate than T-PA, although not uniformly across conditions. R-PA tended to perform better relative to T-PA if the underlying model (a) was unidimensional but had some unique items, (b) had highly correlated factors, or (c) had a general factor as well as a group factor. In addition, R-PA tended to outperform T-PA if items had higher factor loadings and sample size was large. A major disadvantage of the T-PA method was that it frequently yielded inflated Type I error rates.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A