Linganisha mbinu

	Uchanganuzi wa Kipengele cha Uhakika (CFA)	Uchambuzi wa Utendaji Tofauti wa Kipengee (DIF)	Uundaji wa Milongozo ya Kimuundo (SEM)
Nyanja	Takwimu	Saikometriki	Takwimu
Familia	Latent structure	Latent structure	Latent structure
Mwaka wa asili	1969	1988	1970
Mwanzilishi	Karl Jöreskog	Paul W. Holland & Dorothy T. Thayer (Mantel-Haenszel approach, 1988)	Karl Jöreskog (LISREL framework, 1970s)
Aina	Confirmatory latent variable model	Item-level fairness / measurement equivalence analysis	Latent variable / causal modeling
Chanzo asilia	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	Holland, P. W. & Thayer, D. T. (1988). Differential Item Performance and the Mantel-Haenszel Procedure. ETS Research Report Series.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Majina mbadala	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	Madde Yanlılık Analizi (DIF — Differential Item Functioning), item bias analysis, Mantel-Haenszel DIF, Lord chi-square DIF	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Zinazohusiana	4	4	5
Muhtasari	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	Differential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability respond differently to a test item. First formalised by Holland and Thayer in 1988 via the Mantel-Haenszel procedure, it is the principal tool in modern test development for detecting and removing item bias.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.