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| Ricerca Quantitativa Osservazionale Bayesiana× | Modellazione multilivello× | |
|---|---|---|
| Campo≠ | Disegno della ricerca | Statistica per la ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (systematic application to observational research) | 1992 |
| Ideatore≠ | Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s) | Anthony Bryk and Stephen Raudenbush |
| Tipo≠ | Quantitative non-experimental research design with Bayesian inference | Method |
| Fonte seminale≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Alias | Bayesian observational study, Bayesian non-experimental quantitative design, Bayesian causal observational analysis, BOQR | HLM, mixed-effects models, random effects models, MLM |
| Correlati≠ | 4 | 3 |
| Sintesi≠ | Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about parameters as probability distributions, updates them with observed data via Bayes' theorem, and reports conclusions as posterior probability statements. The approach is especially valued in epidemiology, social science, and health services research where randomisation is impossible or unethical. | 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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