方法对比
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| 生长混合模型 (GMM)× | 结构方程模型 (SEM)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1999 | 1970 |
| 提出者≠ | Bengt O. Muthén & Kerby Shedden | Karl Jöreskog (LISREL framework, 1970s) |
| 类型≠ | Latent class / longitudinal growth model | Latent variable / causal modeling |
| 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 ↗ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 |
| 别名 | Büyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve model | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| 相关 | 5 | 5 |
| 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. | 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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