Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzlīmeņu skalas izstrāde× | Apstiprinošā faktoru analīze (AFA)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1990s–2000s | 1969 |
| Autors≠ | Raudenbush, Bryk, Hox and colleagues | Karl Gustav Jöreskog |
| Tips≠ | Hierarchical measurement / scale construction | Hypothesis-testing latent variable model |
| Pirmavots≠ | Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728462 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Citi nosaukumi | multilevel measurement modeling, hierarchical scale development, MLSEM scale construction, nested data scale development | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Multilevel scale development constructs and validates measurement instruments for data collected from individuals nested within higher-level units such as classrooms, organizations, or clinics. It partitions item variance into within-group and between-group components, ensuring that reliability and factor structure are evaluated at both levels simultaneously. | 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 ↗ |
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