Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa de Teste de Modelos Bayesianos× | Modelagem Multinível× | |
|---|---|---|
| Área≠ | Delineamento de pesquisa | Estatística para pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward | 1992 |
| Autor original≠ | Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery | Anthony Bryk and Stephen Raudenbush |
| Tipo≠ | Quantitative inferential research design | Method |
| Fonte seminal≠ | Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Outros nomes | Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT | HLM, mixed-effects models, random effects models, MLM |
| Relacionados≠ | 4 | 3 |
| Resumo≠ | Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unlike null-hypothesis significance testing, Bayesian model testing yields direct evidence for or against specific hypotheses, incorporates prior knowledge, and can support a null hypothesis rather than merely failing to reject it. | 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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