विधियों की तुलना करें

	मिश्रित प्रभाव मॉडल	संरचनात्मक समीकरण मॉडलिंग (SEM)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Latent structure
उद्भव वर्ष	1982	1970
प्रवर्तक	Laird & Ware	Karl Jöreskog (LISREL framework, 1970s)
प्रकार	Mixed effects regression	Latent variable / causal modeling
मौलिक स्रोत	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
उपनाम	LME, LMM, mixed model, random effects model	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
संबंधित	4	5
सारांश	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.	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.