Linganisha mbinu

	Uthabiti wa Upimaji wa Polytomous	Uchanganuzi wa Uthibiti wa Vipengele vya Vikundi Nyingi (MG-CFA)
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	2000–2004	1971
Mwanzilishi	Roger E. Millsap, Robert J. Vandenberg	Karl Jöreskog
Aina	Multi-group confirmatory test	Measurement model / invariance test
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.	Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.
Majina mbadala	PMI, ordinal measurement invariance, polytomous factorial invariance, polytomous multi-group measurement invariance	MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA
Zinazohusiana	5	6
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.	Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified.