Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Investigació de contrastació de models× | Investigació Longitudinal× | |
|---|---|---|
| Camp | Disseny de recerca | Disseny de recerca |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s | Late 19th–early 20th century; methodologically codified through the 20th century |
| Autor original≠ | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Tipus≠ | Confirmatory quantitative research design | Quantitative (or mixed) observational research design |
| Font seminal≠ | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Àlies | model-based research, structural model testing, theory-testing research, MTR | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Relacionats≠ | 5 | 4 |
| Resum≠ | 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. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
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