Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo Linear Hierárquico (HLM)× | Modelo Linear Generalizado (GLM)× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1992 | 1972 |
| Autor original≠ | Bryk & Raudenbush | John A. Nelder & Robert W. M. Wedderburn |
| Tipo≠ | Multilevel linear regression | Regression framework |
| Fonte seminal≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ |
| Outros nomes | HLM, multilevel linear model, nested data model, random coefficient model | GLM, generalized regression, exponential family regression, link-function model |
| Relacionados≠ | 4 | 6 |
| Resumo≠ | The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data. | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. |
| ScholarGateConjunto de dados ↗ |
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