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| Ricerca Confermativa Comparativa× | Ricerca di Test del Modello× | |
|---|---|---|
| Campo | Disegno della ricerca | Disegno della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1971 (Jöreskog); systematized in organizational research by 2000 | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s |
| Ideatore≠ | Karl Jöreskog (multigroup CFA foundation); Robert Vandenberg & Charles Lance (organizational application) | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition |
| Tipo≠ | Quantitative comparative research design | Confirmatory quantitative research design |
| Fonte seminale≠ | Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. DOI ↗ | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 |
| Alias | multigroup confirmatory research, cross-group confirmatory study, comparative hypothesis testing design, comparative model testing research | model-based research, structural model testing, theory-testing research, MTR |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | Comparative confirmatory research tests whether a pre-specified theoretical model or set of hypotheses holds equivalently across two or more distinct groups, time points, or contexts. It extends standard confirmatory analysis by explicitly imposing and evaluating equality constraints across groups, determining not only whether a model fits the data but whether its structure, factor loadings, and parameter estimates are comparable across populations. This design is foundational to cross-cultural, multi-site, and subgroup comparison studies. | 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. |
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