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| Multilevel Generaliserbarhedsteori× | Konstruktiv faktoranalyse (CFA)× | |
|---|---|---|
| Fagområde | Psykometri | Psykometri |
| Familie | Latent structure | Latent structure |
| Oprindelsesår≠ | 1990s–2000s | 1969 |
| Ophavsperson≠ | Brennan, R. L. and Shavelson, R. J. (extensions of Cronbach et al. G-theory to multilevel designs) | Karl Gustav Jöreskog |
| Type≠ | Measurement / variance decomposition | Hypothesis-testing latent variable model |
| Oprindelig kilde≠ | Briggs, D. C. & Wilson, M. (2003). An introduction to multidimensional measurement using Rasch models and generalizability theory. Journal of Applied Measurement, 4(1), 1–19. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Aliasser | multilevel G-theory, ML-GT, hierarchical generalizability theory, multilevel G-study | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Relaterede | 4 | 4 |
| Resumé≠ | Multilevel generalizability theory extends classical G-theory to measurement designs where observations are nested within higher-level units — for example, items nested within raters, or students nested within classrooms. It decomposes score variance into components attributable to persons, facets, and their interactions across hierarchical levels, enabling precise estimation of measurement precision in complex, real-world assessment settings. | 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. |
| ScholarGateDatasæt ↗ |
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