So sánh phương pháp

	Phân tích nhân tố khẳng định (Confirmatory Factor Analysis - CFA)	Phân tích trung gian	Mô hình đa cấp
Lĩnh vực	Trắc lượng tâm lý	Thống kê	Thống kê nghiên cứu
Họ	Latent structure	Hypothesis test	Process / pipeline
Năm ra đời	1969	1986	1992
Người khởi xướng	Karl Gustav Jöreskog	Baron & Kenny	Anthony Bryk and Stephen Raudenbush
Loại	Hypothesis-testing latent variable model	Indirect effects / path test	Method
Công trình gốc	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.
Tên gọi khác	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
Liên quan	4	5	3
Tóm tắt	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.