Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzgrupas apstiprinošā faktoru analīze (MG-CFA)× | Apstiprinošā faktoru analīze (AFA)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1971 | 1969 |
| Autors≠ | Karl Jöreskog | Karl Gustav Jöreskog |
| Tips≠ | Measurement model / invariance test | Hypothesis-testing latent variable model |
| Pirmavots≠ | 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 ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Citi nosaukumi | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | 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. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
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