Comparar métodos

	Análisis de Regresión Múltiple	Modelado de Ecuaciones Estructurales
Campo	Estadística para la investigación	Estadística para la investigación
Familia	Process / pipeline	Process / pipeline
Año de origen	1801	1921
Autor original	Carl Friedrich Gauss	Sewall Wright
Tipo	Method	Method
Fuente seminal	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.
Alias	MLR, multivariate regression, linear regression	SEM, path analysis, latent variable modeling, causal modeling
Relacionados	4	3
Resumen	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.