Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa Longitudinal Multivariada× | Modelagem de Equações Estruturais× | |
|---|---|---|
| Área≠ | Delineamento de pesquisa | Estatística para pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1970s–1980s (formalized in behavioral sciences literature) | 1921 |
| Autor original≠ | Nesselroade, Baltes, and the developmental/behavioral sciences tradition | Sewall Wright |
| Tipo≠ | Quantitative observational research design | Method |
| Fonte seminal≠ | Nesselroade, J. R., & Baltes, P. B. (Eds.). (1979). Longitudinal Research in the Study of Behavior and Development. Academic Press. ISBN: 978-0125154505 | 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 | longitudinal multivariate design, MLR, multivariate panel study, multivariate repeated-measures design | SEM, path analysis, latent variable modeling, causal modeling |
| Relacionados≠ | 4 | 3 |
| Resumo≠ | Multivariate longitudinal research is a quantitative observational design that follows the same units — individuals, groups, or organizations — across two or more time points while measuring several outcome and predictor variables simultaneously. By combining the temporal dimension of longitudinal tracking with multivariate statistical analysis, it allows researchers to examine how a system of variables co-evolves, how early measures predict later outcomes across multiple domains, and whether relationships among variables are stable or change over time. | 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 ↗ |
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