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| 계층적 선형 모형 (HLM / 다층 모형)× | 패널 데이터 고정 효과 모형× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열≠ | Hypothesis test | Regression model |
| 기원 연도≠ | 1986 | 2014 |
| 창시자≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Hsiao (textbook treatment); within transformation of panel data |
| 유형≠ | Parametric nested-data regression | Panel data regression |
| 원전≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| 별칭≠ | HLM, MLM, multilevel modeling, multilevel analysis | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
| ScholarGate데이터셋 ↗ |
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