Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa de Teste de Modelos Hierárquicos× | Modelagem de Equações Estruturais× | |
|---|---|---|
| Área≠ | Delineamento de pesquisa | Estatística para pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994) | 1921 |
| Autor original≠ | Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen | Sewall Wright |
| Tipo≠ | Quantitative confirmatory research design | Method |
| Fonte seminal≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | 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 | multilevel model testing, hierarchical SEM, nested model testing, HLM model testing | SEM, path analysis, latent variable modeling, causal modeling |
| Relacionados≠ | 5 | 3 |
| Resumo≠ | Hierarchical model testing research is a quantitative design that evaluates theoretically derived models using data with a nested or clustered structure — for example, students within classrooms, employees within organisations, or patients within hospitals. It applies hierarchical linear models (HLM) or multilevel structural equation models (ML-SEM) to test whether a proposed set of relationships holds after properly accounting for the non-independence introduced by grouping. | 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. |
| ScholarGateConjunto de dados ↗ |
|
|