Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Modello a Effetti Misti× | ANOVA per misure ripetute× | |
|---|---|---|
| Campo | Statistica | Statistica |
| Famiglia≠ | Regression model | Hypothesis test |
| Anno di origine≠ | 1982 | 1992 |
| Ideatore≠ | Laird & Ware | Girden (textbook treatment); Field (2013) |
| Tipo≠ | Mixed effects regression | Parametric within-subjects mean comparison |
| Fonte seminale≠ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| Alias | LME, LMM, mixed model, random effects model | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| Correlati | 4 | 4 |
| Sintesi≠ | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. | 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). |
| ScholarGateInsieme di dati ↗ |
|
|