Compara mètodes

	Investigació de proves de models multivariants	Investigació de contrastació de models
Camp	Disseny de recerca	Disseny de recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	1970s–1980s (multivariate model testing as a distinct approach)	1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s
Autor original	Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)	Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition
Tipus	Quantitative confirmatory research design	Confirmatory quantitative research design
Font seminal	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344
Àlies	multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research	model-based research, structural model testing, theory-testing research, MTR
Relacionats	5	5
Resum	Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis.	Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence.