Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelagem Linear Hierárquica (HLM / Modelagem Multinível)× | Modelo de Efeitos Mistos× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família≠ | Hypothesis test | Regression model |
| Ano de origem≠ | 1986 | 1982 |
| Autor original≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Laird & Ware |
| Tipo≠ | Parametric nested-data regression | Mixed effects regression |
| Fonte seminal≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| Outros nomes≠ | HLM, MLM, multilevel modeling, multilevel analysis | LME, LMM, mixed model, random effects model |
| Relacionados | 4 | 4 |
| Resumo≠ | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. |
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