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| 계층적 기술 연구× | 다수준 모형× | |
|---|---|---|
| 분야≠ | 연구설계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1980s–1990s (multilevel descriptive formalization) | 1992 |
| 창시자≠ | Formalized within survey and educational research traditions; associated with Hox, Raudenbush, Bryk, and Creswell | Anthony Bryk and Stephen Raudenbush |
| 유형≠ | Quantitative observational/descriptive design | Method |
| 원전≠ | Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728455 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 별칭 | multilevel descriptive design, nested descriptive study, hierarchical survey design, stratified descriptive research | HLM, mixed-effects models, random effects models, MLM |
| 관련≠ | 4 | 3 |
| 요약≠ | Hierarchical descriptive research is an observational design that documents the current state of a phenomenon across two or more nested levels — for example, students within classrooms within schools, or employees within teams within organizations. Rather than testing hypotheses or explaining causation, it describes distributions, frequencies, and relationships at each level, making explicit the structured, layered nature of the population being studied. | 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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