Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель латентної кривої зростання (LGC)× | Змішана модель ефектів× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина≠ | Latent structure | Regression model |
| Рік появи≠ | 1990 | 1982 |
| Автор методу≠ | Meredith & Tisak | Laird & Ware |
| Тип≠ | Latent variable / longitudinal growth model | Mixed effects regression |
| Основоположне джерело≠ | Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122. DOI ↗ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| Інші назви | latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli | LME, LMM, mixed model, random effects model |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories. | 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. |
| ScholarGateНабір даних ↗ |
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