Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Penalizada SCAD× | Modelagem Exploratória de Equações Estruturais× | |
|---|---|---|
| Área | Psicometria | Psicometria |
| Família | Latent structure | Latent structure |
| Ano de origem≠ | 2001 | 2009 |
| Autor original≠ | Jianqing Fan, Runze Li | Tihomir Asparouhov, Bengt Muthén |
| Tipo≠ | Penalized regression with non-concave penalty | Hybrid exploratory-confirmatory factor modeling |
| Fonte seminal≠ | Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360. DOI ↗ | Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗ |
| Outros nomes | SCAD | ESEM |
| Relacionados | 5 | 5 |
| Resumo≠ | SCAD (Smoothly Clipped Absolute Deviation) is a variable selection and regularization method developed by Fan and Li (2001) that addresses limitations of L1 penalization (lasso). SCAD uses a non-concave penalty that automatically performs variable selection while maintaining oracle properties: it recovers the true underlying model as if the true predictors were known in advance. | Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories. |
| ScholarGateConjunto de dados ↗ |
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