Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Багаторівневий конфірматорний факторний аналіз (MCFA)× | Багаторівневе моделювання× | |
|---|---|---|
| Галузь≠ | Психометрія | Статистика досліджень |
| Родина≠ | Latent structure | Process / pipeline |
| Рік появи≠ | 1994 | 1992 |
| Автор методу≠ | Bengt O. Muthen | Anthony Bryk and Stephen Raudenbush |
| Тип≠ | Latent variable model / measurement model | Method |
| Основоположне джерело≠ | Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Інші назви | MCFA, multilevel measurement model, two-level CFA, hierarchical CFA | HLM, mixed-effects models, random effects models, MLM |
| Пов'язані≠ | 6 | 3 |
| Підсумок≠ | Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
| ScholarGateНабір даних ↗ |
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