Linganisha mbinu

	Uchanganuzi wa Kipengele cha Kuthibitisha wa Polytomous	Uchambuzi wa Kichunguza Vigezo vingi vya Polytomous
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	1984	1978
Mwanzilishi	Bengt Muthen	Bengt Muthén
Aina	Latent variable / confirmatory measurement model	Latent variable / dimension reduction
Chanzo asilia	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.	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	CFA for ordered categories, ordinal CFA, categorical CFA, WLSMV-CFA	EFA for ordered-categorical data, polychoric EFA, ordinal exploratory factor analysis, polytomous factor analysis
Zinazohusiana	5	4
Muhtasari	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.	Polytomous exploratory factor analysis extends standard EFA to ordered categorical (Likert-type) response data by replacing the Pearson correlation matrix with a polychoric correlation matrix. It recovers the latent continuous variable that each polytomous item is assumed to reflect, yielding more accurate factor loadings and better-defined factor structures than treating ordinal scores as if they were continuous.