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| Disegno sperimentale Split-Plot× | Modellazione Lineare Gerarchica (HLM / Modellazione Multilivello)× | |
|---|---|---|
| Campo≠ | Disegno sperimentale | Statistica |
| Famiglia | Hypothesis test | Hypothesis test |
| Anno di origine≠ | 1935 | 1986 |
| Ideatore≠ | Frank Yates | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Tipo≠ | Parametric mixed-model ANOVA | Parametric nested-data regression |
| Fonte seminale≠ | Yates, F. (1935). Complex Experiments. Supplement to the Journal of the Royal Statistical Society, 2(2), 181–247. DOI ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| Alias≠ | split-plot ANOVA, whole-plot sub-plot design, Bölünmüş Parsel Deseni (Split-Plot) | HLM, MLM, multilevel modeling, multilevel analysis |
| Correlati≠ | 6 | 4 |
| Sintesi≠ | The split-plot design is a parametric experimental design that applies one factor to large whole plots and a second factor to subdivisions (sub-plots) within each whole plot. It was introduced by Frank Yates in 1935 to handle agricultural experiments where one factor — such as irrigation or tillage method — is difficult or impractical to change frequently, while a second factor can be varied more easily within the same plot. | 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. |
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