Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa de Teste de Modelos Bayesianos× | Modelagem de Equações Estruturais× | |
|---|---|---|
| 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 | 1921 |
| Autor original≠ | Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery | Sewall Wright |
| 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 ↗ | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| Outros nomes | Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT | SEM, path analysis, latent variable modeling, causal modeling |
| 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. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
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