Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model cu efecte mixte× | ANOVA cu măsuri repetate× | Modelarea ecuațiilor structurale (SEM)× | |
|---|---|---|---|
| Domeniu | Statistică | Statistică | Statistică |
| Familie≠ | Regression model | Hypothesis test | Latent structure |
| Anul apariției≠ | 1982 | 1992 | 1970 |
| Autorul original≠ | Laird & Ware | Girden (textbook treatment); Field (2013) | Karl Jöreskog (LISREL framework, 1970s) |
| Tip≠ | Mixed effects regression | Parametric within-subjects mean comparison | Latent variable / causal modeling |
| Sursa seminală≠ | 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 | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 |
| Denumiri alternative | LME, LMM, mixed model, random effects model | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| Înrudite≠ | 4 | 4 | 5 |
| Rezumat≠ | 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). | Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences. |
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