Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Design Multinível de Métodos Mistos Orientado para a Avaliação× | Modelagem Multinível× | |
|---|---|---|
| Área≠ | Delineamento de pesquisa | Estatística para pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 2000s–2010s | 1992 |
| Autor original≠ | Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark (systematization) | Anthony Bryk and Stephen Raudenbush |
| Tipo≠ | Mixed methods evaluation design | Method |
| Fonte seminal≠ | Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage. ISBN: 978-1412975551 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Outros nomes | multilevel mixed methods evaluation, hierarchical mixed methods evaluation, MLM mixed methods, nested mixed methods evaluation | HLM, mixed-effects models, random effects models, MLM |
| Relacionados≠ | 2 | 3 |
| Resumo≠ | Evaluation-oriented multilevel mixed methods is a research design that combines quantitative and qualitative data across hierarchically nested levels of an organization or system — such as students within classrooms within schools — to evaluate a program, policy, or intervention. By capturing outcomes, processes, and contextual factors simultaneously at each level, this design produces richer evaluative inferences than either purely statistical multilevel models or single-level qualitative evaluations alone. | 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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