Linganisha mbinu

	Uthibitisho wa Usawazishaji wa Ordinal	Uchanganuzi wa Kiwango cha Uthibitisho wa Ordinal
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	1959 (validity framework); ordinal adaptation 1990s–2000s	1984
Mwanzilishi	Polychoric/tetrachoric correlation tradition (Pearson, 1900s); validity framework formalized by Campbell & Fiske (1959)	Bengt O. Muthén
Aina	Validity assessment	Latent variable / structural
Chanzo asilia	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.
Majina mbadala	OCV, convergent validity for ordinal scales, polychoric convergent validity, ordinal AVE	CFA for ordinal data, polychoric CFA, WLSMV CFA, categorical CFA
Zinazohusiana	6	5
Muhtasari	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.