Compara mètodes

	Proves de models longitudinals	Investigació de contrastació de models
Camp	Disseny de recerca	Disseny de recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)	1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s
Autor original	Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett)	Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition
Tipus	Quantitative, confirmatory, longitudinal design	Confirmatory quantitative research design
Font seminal	Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344
Àlies	longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling	model-based research, structural model testing, theory-testing research, MTR
Relacionats	6	5
Resum	Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables.	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.