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| 階層線形モデリング（HLM / マルチレベルモデリング）× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 統計学 | 計量経済学 |
| 系統≠ | Hypothesis test | Regression model |
| 提唱年≠ | 1986 | 2019 |
| 提唱者≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Parametric nested-data regression | Linear regression |
| 原典≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名≠ | HLM, MLM, multilevel modeling, multilevel analysis | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連≠ | 4 | 5 |
| 概要≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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