เปรียบเทียบวิธี

	การสร้างแบบจำลองสมการโครงสร้าง (SEM)	การวิเคราะห์ปัจจัยยืนยัน (Confirmatory Factor Analysis: CFA)	Multilevel Modeling
สาขาวิชา	สถิติศาสตร์	การวัดทางจิตวิทยา	สถิติการวิจัย
ตระกูล	Latent structure	Latent structure	Process / pipeline
ปีกำเนิด	1970	1969	1992
ผู้ริเริ่ม	Karl Jöreskog (LISREL framework, 1970s)	Karl Gustav Jöreskog	Anthony Bryk and Stephen Raudenbush
ประเภท	Latent variable / causal modeling	Hypothesis-testing latent variable model	Method
แหล่งต้นตำรับ	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
ชื่อเรียกอื่น	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling	CFA, confirmatory FA, measurement model, restricted factor analysis	HLM, mixed-effects models, random effects models, MLM
ที่เกี่ยวข้อง	5	4	3
สรุป	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.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	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.