方法对比
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| 潜增长曲线模型 (LGC)× | 混合效应模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族≠ | Latent structure | Regression model |
| 起源年份≠ | 1990 | 1982 |
| 提出者≠ | Meredith & Tisak | Laird & Ware |
| 类型≠ | Latent variable / longitudinal growth model | Mixed effects regression |
| 开创性文献≠ | 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 ↗ |
| 别名 | latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli | LME, LMM, mixed model, random effects model |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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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