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序数确认因子分析 (序数 CFA)×探索性因子分析(EFA)×
领域心理测量学统计学
方法族Latent structureLatent structure
起源年份1984
提出者Bengt O. Muthén
类型Latent variable / structuralLatent variable / dimension reduction
开创性文献Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. DOI ↗Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗
别名CFA for ordinal data, polychoric CFA, WLSMV CFA, categorical CFAcommon factor analysis, açımlayıcı faktör analizi, factor analysis
相关54
摘要Ordinal confirmatory factor analysis (Ordinal CFA) tests a pre-specified factor structure when the observed indicators are ordinal — typically Likert-type survey items. By using polychoric correlations and robust estimators such as WLSMV, it avoids the bias that arises from treating categorical responses as continuous.Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.
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ScholarGate方法对比: Ordinal CFA · EFA. 于 2026-06-17 检索自 https://scholargate.app/zh/compare