Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzlīmeņu eksploratīvā faktoru analīze (ML-EFA)× | Eksploratīvā faktoru analīze (EFA)× | |
|---|---|---|
| Nozare≠ | Psihometrija | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1994 | — |
| Autors≠ | Bengt O. Muthén | — |
| Tips≠ | Latent variable / multilevel dimension reduction | Latent variable / dimension reduction |
| Pirmavots≠ | Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. 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 ↗ |
| Citi nosaukumi≠ | ML-EFA, multilevel factor analysis, two-level exploratory factor analysis, hierarchical exploratory factor analysis | common factor analysis, açımlayıcı faktör analizi, 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. | 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. |
| ScholarGateDatu kopa ↗ |
|
|