Сравнение методов

	Множественный регрессионный анализ	Структурное моделирование (Structural Equation Modeling)
Область	Статистика исследований	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1801	1921
Автор метода	Carl Friedrich Gauss	Sewall Wright
Тип	Method	Method
Основополагающий источник	Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons.	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.
Другие названия	MLR, multivariate regression, linear regression	SEM, path analysis, latent variable modeling, causal modeling
Связанные	4	3
Сводка	Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.	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.