方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 混合效应模型× | 结构方程模型 (SEM)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族≠ | Regression model | Latent structure |
| 起源年份≠ | 1982 | 1970 |
| 提出者≠ | Laird & Ware | Karl Jöreskog (LISREL framework, 1970s) |
| 类型≠ | Mixed effects regression | Latent variable / causal modeling |
| 开创性文献≠ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. 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 | LME, LMM, mixed model, random effects model | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
|
|