Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise Fatorial Confirmatória Multi-Grupo (MG-CFA)× | Análise Fatorial Exploratória (AFE)× | |
|---|---|---|
| Área≠ | Psicometria | Estatística |
| Família | Latent structure | Latent structure |
| Ano de origem≠ | 1971 | — |
| Autor original≠ | Karl Jöreskog | — |
| Tipo≠ | Measurement model / invariance test | Latent variable / dimension reduction |
| Fonte seminal≠ | 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 ↗ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ |
| Outros nomes≠ | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified. | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. |
| ScholarGateConjunto de dados ↗ |
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