Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļu vienkāršā lineārā regresija× | Hierarhiskais lineārais modelis (HLM)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 1992 |
| Autors≠ | Hsiao (1986); Baltagi (seminal textbook treatments) | Bryk & Raudenbush |
| Tips≠ | Linear regression (panel data) | Multilevel linear regression |
| Pirmavots≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 |
| Citi nosaukumi | panel SLR, longitudinal simple regression, two-way panel simple regression, fixed-effects simple linear regression | HLM, multilevel linear model, nested data model, random coefficient model |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Panel simple linear regression models a continuous outcome as a linear function of a single predictor using data that track the same entities (individuals, firms, countries) across multiple time periods. It separates within-entity variation from between-entity variation, enabling control for unobserved time-invariant characteristics that would confound a plain cross-sectional regression. | 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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