Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Ricerca gerarchica trasversale× | Modellazione multilivello× | |
|---|---|---|
| Campo≠ | Disegno della ricerca | Statistica per la ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1980s–1990s (formalized with HLM software and methodology) | 1992 |
| Ideatore≠ | Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition) | Anthony Bryk and Stephen Raudenbush |
| Tipo≠ | Quantitative observational design | Method |
| Fonte seminale≠ | Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage. ISBN: 978-1849202015 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Alias | multilevel cross-sectional design, nested cross-sectional study, clustered cross-sectional research, HCS design | HLM, mixed-effects models, random effects models, MLM |
| Correlati≠ | 2 | 3 |
| Sintesi≠ | Hierarchical cross-sectional research is a quantitative observational design that collects data from individuals nested within higher-level units — such as students within schools, patients within hospitals, or employees within organizations — at a single point in time. By accounting for the non-independence of clustered observations through multilevel modeling, it enables researchers to simultaneously examine individual-level and group-level predictors of an outcome without violating the independence assumption of ordinary regression. | 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. |
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