Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa de Teste de Modelos Multivariados× | Pesquisa de Teste de Modelo× | |
|---|---|---|
| Área | Delineamento de pesquisa | Delineamento de pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 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 |
| Tipo≠ | Quantitative confirmatory research design | Confirmatory quantitative research design |
| Fonte 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 |
| Outros nomes | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | model-based research, structural model testing, theory-testing research, MTR |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. |
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