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。
| 探索性结构方程模型× | 偏最小二乘结构方程模型× | |
|---|---|---|
| 领域 | 心理测量学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2009 | 1985 |
| 提出者≠ | Tihomir Asparouhov, Bengt Muthén | Herman Wold |
| 类型≠ | Hybrid exploratory-confirmatory factor modeling | Component-based structural equation model |
| 开创性文献≠ | Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗ | Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445 |
| 别名≠ | ESEM | PLS-SEM, PLS path modeling |
| 相关 | 5 | 5 |
| 摘要≠ | Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories. | PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data. |
| ScholarGate数据集 ↗ |
|
|