Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Desenho Solomon de Quatro Grupos Randomizado por Cluster× | Modelagem Multinível× | |
|---|---|---|
| Área≠ | Delineamento experimental | Estatística para pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1949 (Solomon design); cluster extension formalized in 1990s | 1992 |
| Autor original≠ | Richard L. Solomon (four-group logic, 1949); cluster randomization methods developed by Murray and colleagues in the 1990s | Anthony Bryk and Stephen Raudenbush |
| Tipo≠ | Experimental design | Method |
| Fonte seminal≠ | Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Outros nomes | CR-S4GD, cluster-randomized four-group design, group-randomized Solomon design, Solomon four-group cluster trial | HLM, mixed-effects models, random effects models, MLM |
| Relacionados≠ | 6 | 3 |
| Resumo≠ | The cluster randomized Solomon four-group design combines cluster randomization — assigning intact groups such as schools, clinics, or communities to conditions — with the Solomon four-group structure that isolates the effect of pretesting. Four clusters (or sets of clusters) are created: two receive the treatment and two serve as controls, with only one treatment cluster and one control cluster receiving a pretest, while the others go straight to the posttest. This structure simultaneously controls for pretest sensitization and the logistical constraint that individual randomization is infeasible. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
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