Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bejeziāņu konstruktās validitātes novērtēšana× | Apstiprinošā faktoru analīze (AFA)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1955 / 2012 | 1969 |
| Autors≠ | Cronbach & Meehl (validity framework); Muthén & Asparouhov (Bayesian SEM extension) | Karl Gustav Jöreskog |
| Tips≠ | Validity assessment / Bayesian inference | Hypothesis-testing latent variable model |
| Pirmavots≠ | Muthén, B. & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Citi nosaukumi | Bayesian validity analysis, Bayesian CFA-based validity, Bayesian structural validity, posterior construct validity | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Bayesian construct validity assessment uses Bayesian confirmatory factor analysis and related Bayesian structural equation models to evaluate whether a scale or test measures the intended latent construct. It yields full posterior distributions for factor loadings, structural coefficients, and model-fit indices rather than single point estimates, enabling more nuanced and uncertainty-aware validity conclusions. | 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. |
| ScholarGateDatu kopa ↗ |
|
|