Võrdle meetodeid
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| Kinnitav faktorianalüüs (CFA)× | Cronbachi alfa (usaldusväärsuse analüüs)× | Eksploratiivne faktorianalüüs (EFA)× | |
|---|---|---|---|
| Valdkond≠ | Psühhomeetria | Statistika | Statistika |
| Perekond | Latent structure | Latent structure | Latent structure |
| Tekkeaasta≠ | 1969 | 1951 | — |
| Looja≠ | Karl Gustav Jöreskog | Lee J. Cronbach | — |
| Tüüp≠ | Hypothesis-testing latent variable model | Reliability / internal consistency coefficient | Latent variable / dimension reduction |
| Algallikas≠ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. 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 ↗ |
| Rööpnimetused≠ | CFA, confirmatory FA, measurement model, restricted factor analysis | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Seotud | 4 | 4 | 4 |
| Kokkuvõte≠ | 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. | Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research. | 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. |
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