方法对比
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| 生长混合模型 (GMM)× | 分层线性模型 (HLM / 多层模型)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族≠ | Latent structure | Hypothesis test |
| 起源年份≠ | 1999 | 1986 |
| 提出者≠ | Bengt O. Muthén & Kerby Shedden | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| 类型≠ | Latent class / longitudinal growth model | Parametric nested-data regression |
| 开创性文献≠ | Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469. DOI ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| 别名≠ | Büyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve model | HLM, MLM, multilevel modeling, multilevel analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | The Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the standard Latent Growth Curve (LGC) model by allowing the sample to be composed of an unknown mixture of classes with different intercepts, slopes, and variance structures. | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. |
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