Comparar métodos

	Investigación Correlacional Multivariada	Análisis de senderos	Modelado de Ecuaciones Estructurales
Campo	Diseño de investigación	Estadística	Estadística para la investigación
Familia	Process / pipeline	Latent structure	Process / pipeline
Año de origen	1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s	1921	1921
Autor original	Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others	Sewall Wright	Sewall Wright
Tipo	Non-experimental quantitative research design	Causal / mediation model	Method
Fuente seminal	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.	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	multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research	PA, path coefficient analysis, observed-variable SEM, causal path modeling	SEM, path analysis, latent variable modeling, causal modeling
Relacionados	2	5	3
Resumen	Multivariate correlational research is a non-experimental quantitative design that examines the simultaneous associations among three or more variables. Rather than manipulating conditions, the researcher measures naturally occurring variables and uses techniques such as multiple regression, canonical correlation, or structural equation modeling to map the pattern and strength of their interrelationships. It is the dominant design when the goal is to understand how a set of predictors jointly relates to one or more outcome variables.	Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.	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.