So sánh phương pháp

	Hồi quy Logistic	Mô hình đa cấp	Ghép cặp điểm xu hướng
Lĩnh vực	Thống kê nghiên cứu	Thống kê nghiên cứu	Thống kê nghiên cứu
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	1958	1992	1983
Người khởi xướng	David Roxbee Cox	Anthony Bryk and Stephen Raudenbush	Paul Rosenbaum and Donald Rubin
Loại	Method	Method	Method
Công trình gốc	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Tên gọi khác	logit model, binomial logistic regression, LR	HLM, mixed-effects models, random effects models, MLM	PSM, propensity score weighting, covariate balance
Liên quan	3	3	3
Tóm tắt	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.