Salīdzināt metodes

	Strukturālā vienādojumu modelēšana (SEM)	Eksploratīvā faktoru analīze (EFA)	Daudzlīmeņu modelēšana
Nozare	Statistika	Statistika	Pētniecības statistika
Saime	Latent structure	Latent structure	Process / pipeline
Izcelsmes gads	1970	—	1992
Autors	Karl Jöreskog (LISREL framework, 1970s)	—	Anthony Bryk and Stephen Raudenbush
Tips	Latent variable / causal modeling	Latent variable / dimension reduction	Method
Pirmavots	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Citi nosaukumi	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling	common factor analysis, açımlayıcı faktör analizi, factor analysis	HLM, mixed-effects models, random effects models, MLM
Saistītās	5	4	3
Kopsavilkums	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.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.