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| Mixed Effects Model× | ANOVA mit wiederholten Messungen× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie≠ | Regression model | Hypothesis test |
| Entstehungsjahr≠ | 1982 | 1992 |
| Urheber≠ | Laird & Ware | Girden (textbook treatment); Field (2013) |
| Typ≠ | Mixed effects regression | Parametric within-subjects mean comparison |
| Wegweisende Quelle≠ | 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 |
| Aliasnamen | LME, LMM, mixed model, random effects model | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | 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). |
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