Effects of cross-loadings on determining the number of factors to retain
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AbstractIn exploratory factor analysis (EFA), cross-loadings frequently occur in empirical research, but its effects on determining the number of factors to retain are seldom known. In this paper, we analyzed whether and how cross-loadings affected the performance of the parallel analysis (PA), the empirical Kaiser criterion (EKC), the likelihood ratio test (LRT), the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA) in determining the number of factors to retain. A large-scale simulation study was also conducted. A few conclusions can be drawn: (1) overall, PA provides the most accurate performance, especially when data are non-normally distributed; (2) cross-loadings noticeably affect the performance of PA, CFI, and TLI with different patterns, and they virtually have no effect on EKC, LRT, and RMSEA; (3) no method is immune to the sizable detrimental effect of normality assumption violation. Several recommendations were provided.
Acceptance Date15/05/2020
All Author(s) ListYujun Li, Zhonglin Wen, Kit-Tai Hau, Ke-Hai Yuan, Yafeng Peng
Journal nameStructural Equation Modeling: A Multidisciplinary Journal
PublisherTaylor & Francis
LanguagesEnglish-United Kingdom
KeywordsExploratory factor analysis, cross-loading, number of factors, non-normality

Last updated on 2021-10-01 at 00:13