Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Latent Growth Curve Model (LGC)× | Jaukto efektu modelis× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime≠ | Latent structure | Regression model |
| Izcelsmes gads≠ | 1990 | 1982 |
| Autors≠ | Meredith & Tisak | Laird & Ware |
| Tips≠ | Latent variable / longitudinal growth model | Mixed effects regression |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli | LME, LMM, mixed model, random effects model |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. |
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