Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Багаторівневе диференційне функціонування елементів (Multilevel DIF)× | Multilevel Measurement Invariance× | |
|---|---|---|
| Галузь | Психометрія | Психометрія |
| Родина | Latent structure | Latent structure |
| Рік появи≠ | 2001 | 2000s |
| Автор методу≠ | Kamata (2001) and subsequent multilevel IRT/DIF literature | Muthén, Asparouhov, and colleagues |
| Тип≠ | Bias detection / multilevel measurement model | Measurement model evaluation |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | multilevel DIF, hierarchical DIF analysis, cross-level DIF, ML-DIF | MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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