Sammenlign metoder

	Multilevel Målingsinvarians	Strukturel Ligningsmodellering
Fagområde	Psykometri	Forskningsstatistik
Familie	Latent structure	Process / pipeline
Oprindelsesår	2000s	1921
Ophavsperson	Muthén, Asparouhov, and colleagues	Sewall Wright
Type	Measurement model evaluation	Method
Oprindelig kilde	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.
Aliasser	MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance	SEM, path analysis, latent variable modeling, causal modeling
Relaterede	3	3
Resumé	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.