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| 계층적 모형 검증 연구× | 다수준 모형× | |
|---|---|---|
| 분야≠ | 연구설계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994) | 1992 |
| 창시자≠ | Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen | Anthony Bryk and Stephen Raudenbush |
| 유형≠ | Quantitative confirmatory research design | Method |
| 원전≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 별칭 | multilevel model testing, hierarchical SEM, nested model testing, HLM model testing | HLM, mixed-effects models, random effects models, MLM |
| 관련≠ | 5 | 3 |
| 요약≠ | Hierarchical model testing research is a quantitative design that evaluates theoretically derived models using data with a nested or clustered structure — for example, students within classrooms, employees within organisations, or patients within hospitals. It applies hierarchical linear models (HLM) or multilevel structural equation models (ML-SEM) to test whether a proposed set of relationships holds after properly accounting for the non-independence introduced by grouping. | 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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