方法对比

	序数收敛效度	序数确认因子分析 (序数 CFA)
领域	心理测量学	心理测量学
方法族	Latent structure	Latent structure
起源年份	1959 (validity framework); ordinal adaptation 1990s–2000s	1984
提出者	Polychoric/tetrachoric correlation tradition (Pearson, 1900s); validity framework formalized by Campbell & Fiske (1959)	Bengt O. Muthén
类型	Validity assessment	Latent variable / structural
开创性文献	Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373.	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.
别名	OCV, convergent validity for ordinal scales, polychoric convergent validity, ordinal AVE	CFA for ordinal data, polychoric CFA, WLSMV CFA, categorical CFA
相关	6	5
摘要	Ordinal convergent validity assesses the degree to which indicators of the same latent construct correlate strongly with each other when those indicators are measured on ordinal (e.g., Likert-type) scales. It adapts standard convergent validity procedures — factor loadings, average variance extracted, and HTMT ratios — to account for the discrete, bounded nature of ordinal response categories using polychoric correlations and ordinal-appropriate estimation methods.	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.