方法对比

	多元解释研究	结构方程模型
领域	研究设计	研究统计学
方法族	Process / pipeline	Process / pipeline
起源年份	Mid-to-late 20th century (consolidated ~1960s–1980s)	1921
提出者	Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others	Sewall Wright
类型	Quantitative research design	Method
开创性文献	Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
别名	multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER	SEM, path analysis, latent variable modeling, causal modeling
相关	4	3
摘要	Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.