Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzlīmeņu eksploratīvā faktoru analīze (ML-EFA)× | Apstiprinošā faktoru analīze (AFA)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1994 | 1969 |
| Autors≠ | Bengt O. Muthén | Karl Gustav Jöreskog |
| Tips≠ | Latent variable / multilevel dimension reduction | Hypothesis-testing latent variable model |
| Pirmavots≠ | Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Citi nosaukumi | ML-EFA, multilevel factor analysis, two-level exploratory factor analysis, hierarchical exploratory factor analysis | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | Multilevel exploratory factor analysis uncovers latent factor structures simultaneously at two or more levels of a data hierarchy — for example, both within individuals and between groups — without imposing a fixed structure in advance. It is essential whenever survey or test items are collected from respondents nested inside classrooms, organisations, or clinics. | 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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