เปรียบเทียบวิธี

	การวิเคราะห์ปัจจัยยืนยัน (Confirmatory Factor Analysis: CFA)	การวิเคราะห์การส่งผ่าน	Multilevel Modeling
สาขาวิชา	การวัดทางจิตวิทยา	สถิติศาสตร์	สถิติการวิจัย
ตระกูล	Latent structure	Hypothesis test	Process / pipeline
ปีกำเนิด	1969	1986	1992
ผู้ริเริ่ม	Karl Gustav Jöreskog	Baron & Kenny	Anthony Bryk and Stephen Raudenbush
ประเภท	Hypothesis-testing latent variable model	Indirect effects / path test	Method
แหล่งต้นตำรับ	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
ชื่อเรียกอื่น	CFA, confirmatory FA, measurement model, restricted factor analysis	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)	HLM, mixed-effects models, random effects models, MLM
ที่เกี่ยวข้อง	4	5	3
สรุป	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.	Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism.	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.