方法对比

	分层因果-比较研究	事后研究设计
领域	研究设计	研究设计
方法族	Process / pipeline	Process / pipeline
起源年份	1960s (causal-comparative); 1980s–2002 (hierarchical/multilevel extension)	1960s (systematic codification); concept used in social science from early 20th century
提出者	Kerlinger (causal-comparative logic); Raudenbush & Bryk (hierarchical extension)	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
类型	Non-experimental quantitative research design	Non-experimental quantitative research design
开创性文献	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
别名	multilevel causal-comparative design, nested causal-comparative research, HLM causal-comparative study, hierarchical ex post facto comparison	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
相关	4	3
摘要	Hierarchical causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on an outcome variable while explicitly modeling the nested structure of the data. Participants are clustered within higher-level units — students within classrooms, employees within organizations — and the design uses multilevel analytical techniques to distinguish group differences at each level. The cause-and-effect inference is strengthened by accounting for variance attributable to the hierarchy rather than misattributing it to individual-level group membership.	Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention.