방법 비교

	Mixed Effects Model	일원 분산 분석	구조방정식 모형(SEM)
분야	통계학	통계학	통계학
계열	Regression model	Hypothesis test	Latent structure
기원 연도	1982	1925	1970
창시자	Laird & Ware	Ronald A. Fisher	Karl Jöreskog (LISREL framework, 1970s)
유형	Mixed effects regression	Parametric mean comparison	Latent variable / causal modeling
원전	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
별칭	LME, LMM, mixed model, random effects model	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
관련	4	4	5
요약	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.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.	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.