Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Hierarhiskā lineārā modelēšana (HLM / daudzlīmeņu modelēšana)× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|
| Nozare≠ | Statistika | Ekonometrija |
| Saime≠ | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 1986 | 2019 |
| Autors≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Parametric nested-data regression | Linear regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | HLM, MLM, multilevel modeling, multilevel analysis | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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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