Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustā hierarhiskā lineārā modelēšana× | Hierarhiskais lineārais modelis (HLM)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2004 | 1992 |
| Autors≠ | Maas & Hox (2004); Goldstein et al. (2018) | Bryk & Raudenbush |
| Tips≠ | Robust multilevel regression | Multilevel linear regression |
| Pirmavots≠ | Maas, C. J. M., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58(2), 127–137. DOI ↗ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 |
| Citi nosaukumi | robust HLM, robust multilevel model, robust mixed-effects linear model, robust nested regression | HLM, multilevel linear model, nested data model, random coefficient model |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Robust Hierarchical Linear Model (Robust HLM) extends standard HLM by replacing or protecting its standard errors against violations of distributional assumptions — chiefly non-normal residuals, heteroscedasticity, and influential clusters. It retains the nested, two-level (or higher) structure while producing more trustworthy inference under real-world data conditions. | 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. |
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