Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Hierarchical Linear Modeling (HLM / Multilevel Modeling)× | Korduvate mõõtmiste ANOVA× | |
|---|---|---|
| Valdkond | Statistika | Statistika |
| Perekond | Hypothesis test | Hypothesis test |
| Tekkeaasta≠ | 1986 | 1992 |
| Looja≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Girden (textbook treatment); Field (2013) |
| Tüüp≠ | Parametric nested-data regression | Parametric within-subjects mean comparison |
| Algallikas≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| Rööpnimetused≠ | HLM, MLM, multilevel modeling, multilevel analysis | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| Seotud | 4 | 4 |
| Kokkuvõte≠ | 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. | Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013). |
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