Pairwise Target Rotation for Factor Models
因子模型的成对目标旋转AI 解读 ↓
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因子模型的成对目标旋转
- 研究问题
- 探索性因子分析存在旋转不确定性时，如何把题项语义等先验信息与载荷结构的吻合程度转化为可操作的解释性指标？
- 方法与思路
- 作者构造评估先验信息与载荷一致性的解释性指数，并提出最大化该指数的成对目标旋转，通过模拟和经验数据检验其表现。
- 材料陈述的贡献
- 摘要称，该指数随较低噪声显示更好拟合，新旋转在所考察条件下比经典正交旋转更好恢复潜在结构，并呈现语义与构念的联系。
- 阅读关注点
- 应核对先验语义如何转化为目标、解释性指标与统计拟合的关系，以及模拟比较的旋转约束和经验构念验证是否充分。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明指数定义和模拟条件，所述优势不能外推为所有因子模型均适用。
原文摘要
Factor analysis is used to characterize latent variables by examining relationships among manifest variables. In exploratory factor analysis (EFA), various factor models are considered to uncover the latent structure. Now, the success of EFA lies with the model's interpretability, as the objective is to build a factor model that is not only supported by data, but is also meaningful. Achieving such, however, is challenging, as gauging interpretability is difficult and subjective, owing to rotational indeterminacy. Thus, we propose a new index that measures the interpretability of a factor model. The index does this by evaluating the agreement between a priori information, such as semantic information from items, and the loadings. We also introduce pairwise target rotation, a rotation method that maximizes interpretability based on the index. In general, this method allows for an intuitive yet flexible way of incorporating a priori information, such as semantics, in factor rotations, which can help in performing EFA more effectively. Based on simulations, the index correctly indicates better fit when noise levels are lower and the new rotation outperforms classical orthogonal rotations across diverse conditions in terms of recovering the latent structure. Finally, we applied the method to empirical datasets, providing evidence linking item semantics and latent constructs, consistent with recent findings.