Linganisha mbinu

	Uthabiti wa Upimaji wa Polytomous	Uchanganuzi wa Kipengele cha Kuthibitisha wa Polytomous
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	2000–2004	1984
Mwanzilishi	Roger E. Millsap, Robert J. Vandenberg	Bengt Muthen
Aina	Multi-group confirmatory test	Latent variable / confirmatory measurement model
Chanzo asilia	Millsap, R. E. & Kwok, O.-M. (2004). Evaluating the impact of partial factor loading and intercept invariance on selection utility. Psychological Methods, 9(2), 200–215.	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	PMI, ordinal measurement invariance, polytomous factorial invariance, polytomous multi-group measurement invariance	CFA for ordered categories, ordinal CFA, categorical CFA, WLSMV-CFA
Zinazohusiana	5	5
Muhtasari	Polytomous measurement invariance testing evaluates whether a scale with ordered categorical (polytomous) response options — such as Likert-type items — measures the same latent construct in the same way across two or more groups. It extends classical multi-group CFA invariance testing to properly account for the ordinal nature of item responses, ensuring that group comparisons of latent means or factor structures are substantively valid.	Polytomous confirmatory factor analysis (CFA) tests a pre-specified factor structure when items have three or more ordered response categories (e.g., Likert scales). By working with polychoric correlations and robust estimators such as WLSMV, it avoids the distortions that arise when ordered categorical data are treated as continuous.