Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Multilevel Differential Item Functioning× | Daudzlīmeņu mērījumu ekvitāte× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 2001 | 2000s |
| Autors≠ | Kamata (2001) and subsequent multilevel IRT/DIF literature | Muthén, Asparouhov, and colleagues |
| Tips≠ | Bias detection / multilevel measurement model | Measurement model evaluation |
| Pirmavots≠ | French, B. F., & Finch, W. H. (2008). Multigroup confirmatory factor analysis: Locating the invariant referent sets. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 96–113. DOI ↗ | Muthén, B. O., & Asparouhov, T. (2009). Multilevel factor analysis of class and student achievement components. Journal of Educational and Behavioral Statistics, 34(2), 250–270. link ↗ |
| Citi nosaukumi | multilevel DIF, hierarchical DIF analysis, cross-level DIF, ML-DIF | MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | Multilevel DIF analysis detects whether individual test or survey items function differently across groups when respondents are clustered within higher-level units — such as students nested in schools, employees in organizations, or patients in clinics. By accounting for hierarchical data structure, it separates genuine item bias from artificial DIF signals caused by ignoring clustering. | Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organisational, educational, and cross-cultural research. |
| ScholarGateDatu kopa ↗ |
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