Linganisha mbinu

	Upimishaji wa uhakikisho wa kipimo cha ordinal	Uchanganuzi wa Kiwango cha Uthibitisho wa Ordinal
Nyanja	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	1984–2011	1984
Mwanzilishi	Roger Millsap; Bengt Muthén	Bengt O. Muthén
Aina	Multi-group model comparison	Latent variable / structural
Chanzo asilia	Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge. ISBN: 978-1848728936	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	ordinal MI, measurement invariance for ordinal data, ordinal CFA invariance, categorical measurement invariance	CFA for ordinal data, polychoric CFA, WLSMV CFA, categorical CFA
Zinazohusiana	6	5
Muhtasari	Ordinal measurement invariance testing evaluates whether a multi-group confirmatory factor model holds equivalent measurement properties across groups when scale items are ordinal — such as Likert-type response scales. It uses polychoric correlations and categorical estimators (WLSMV/DWLS) rather than Pearson-based methods, correcting the systematic bias that arises when ordinal data are treated as continuous.	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.