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。
| 动态因果建模× | 结构方程模型× | |
|---|---|---|
| 领域≠ | 神经影像 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2003 | 1921 |
| 提出者≠ | Karl J. Friston | Sewall Wright |
| 类型≠ | Causal modeling pipeline for neuroimaging | Method |
| 开创性文献≠ | Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗ | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| 别名≠ | DCM, Dynamic Causal Model | SEM, path analysis, latent variable modeling, causal modeling |
| 相关≠ | 2 | 3 |
| 摘要≠ | Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
| ScholarGate数据集 ↗ |
|
|