Effects of cross-loadings on determining the number of factors to retain
Publication in refereed journal
已正式接受出版

香港中文大學研究人員

引用次數
Scopus ( 22/11/2020)
替代計量分析
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其它資訊
摘要In 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.
出版社接受日期15.05.2020
著者Yujun Li, Zhonglin Wen, Kit-Tai Hau, Ke-Hai Yuan, Yafeng Peng
期刊名稱Structural Equation Modeling: A Multidisciplinary Journal
出版年份2020
出版社Taylor & Francis
國際標準期刊號1070-5511
電子國際標準期刊號1532-8007
語言英式英語
關鍵詞Exploratory factor analysis, cross-loading, number of factors, non-normality

上次更新時間 2020-22-11 於 23:51