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| Bayesian-Survey-Forschung× | Multilevel Modeling× | |
|---|---|---|
| Fachgebiet≠ | Forschungsdesign | Forschungsstatistik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1980s–2000s (modern applied development) | 1992 |
| Urheber≠ | Thomas Bayes (theorem, 1763); applied to survey methodology by Donald Rubin, Andrew Gelman, and others (1980s–2000s) | Anthony Bryk and Stephen Raudenbush |
| Typ≠ | Quantitative observational research design with Bayesian inference | Method |
| Wegweisende Quelle≠ | Gelman, A., & Carlin, J. B. (2007). Some issues on the foundations of statistics. In A. Gelman & J. B. Carlin (Eds.), Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Aliasnamen | Bayesian survey analysis, Bayesian survey methodology, Bayesian polling, Bayesian questionnaire analysis | HLM, mixed-effects models, random effects models, MLM |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Bayesian survey research applies Bayesian statistical inference to survey data, combining prior knowledge or beliefs about population parameters with observed questionnaire responses to produce posterior probability distributions. Unlike null-hypothesis significance testing, this approach quantifies uncertainty directly, incorporates prior evidence, and yields probabilistic statements about parameters of interest — making it especially powerful for small samples, sequential data collection, and contexts where substantive prior knowledge exists. | 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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