方法对比

	多层模型	方差分析 (ANOVA)	逻辑回归
领域	研究统计学	研究统计学	研究统计学
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1992	1925	1958
提出者	Anthony Bryk and Stephen Raudenbush	Ronald A. Fisher	David Roxbee Cox
类型	Method	Method	Method
开创性文献	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
别名	HLM, mixed-effects models, random effects models, MLM	ANOVA, F-test	logit model, binomial logistic regression, LR
相关	3	4	3
摘要	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.	ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.	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.