Salīdzināt metodes

	Daudzlīmeņu mērījumu ekvitāte	Modelēšana ar strukturālām vienādojumiem
Nozare	Psihometrija	Pētniecības statistika
Saime	Latent structure	Process / pipeline
Izcelsmes gads	2000s	1921
Autors	Muthén, Asparouhov, and colleagues	Sewall Wright
Tips	Measurement model evaluation	Method
Pirmavots	Muthén, B. O., & Asparouhov, T. (2009). Multilevel factor analysis of class and student achievement components. Journal of Educational and Behavioral Statistics, 34(2), 250–270.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Citi nosaukumi	MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance	SEM, path analysis, latent variable modeling, causal modeling
Saistītās	3	3
Kopsavilkums	Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organisational, educational, and cross-cultural research.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.