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。
| 多层测量不变性× | 结构方程模型× | |
|---|---|---|
| 领域≠ | 心理测量学 | 研究统计学 |
| 方法族≠ | Latent structure | Process / pipeline |
| 起源年份≠ | 2000s | 1921 |
| 提出者≠ | Muthén, Asparouhov, and colleagues | Sewall Wright |
| 类型≠ | Measurement model evaluation | Method |
| 开创性文献≠ | Muthén, B. O., & Asparouhov, T. (2009). Multilevel factor analysis of class and student achievement components. Journal of Educational and Behavioral Statistics, 34(2), 250–270. link ↗ | 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 | MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance | SEM, path analysis, latent variable modeling, causal modeling |
| 相关 | 3 | 3 |
| 摘要≠ | Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organisational, educational, and cross-cultural research. | 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 ↗ |
|
|