เปรียบเทียบวิธี

	Mixed Effects Model	การสร้างแบบจำลองสมการโครงสร้าง (SEM)
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Regression model	Latent structure
ปีกำเนิด	1982	1970
ผู้ริเริ่ม	Laird & Ware	Karl Jöreskog (LISREL framework, 1970s)
ประเภท	Mixed effects regression	Latent variable / causal modeling
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	LME, LMM, mixed model, random effects model	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
ที่เกี่ยวข้อง	4	5
สรุป	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.