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| Model Lineal Jeràrquic (HLM)× | Regressió per Mínims Quadrats Ordinàris (MQO)× | |
|---|---|---|
| Camp≠ | Estadística | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1992 | 2019 |
| Autor original≠ | Bryk & Raudenbush | Wooldridge (textbook treatment); classical least squares |
| Tipus≠ | Multilevel linear regression | Linear regression |
| Font seminal≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Àlies | HLM, multilevel linear model, nested data model, random coefficient model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionats≠ | 4 | 5 |
| Resum≠ | The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data. | 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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