Robust oblique Target-rotation for small samples
小样本下的稳健斜交目标旋转AI 解读 ↓
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小样本下的稳健斜交目标旋转
- 研究问题
- 研究探索性因子分析中，逐项交叉载荷的目标旋转为何易受抽样误差影响，以及小样本下如何改善旋转结果。
- 方法与思路
- 作者对显著载荷区块计算平均交叉载荷，据此进行斜交目标旋转，再将所得变换矩阵应用于完整载荷矩阵。
- 材料陈述的贡献
- 摘要称模拟中区块平均旋转减少了因子相关的负偏差，尤其在小样本、多因子情形，实例也显示更高的子样本相似性。
- 阅读关注点
- 应核对载荷区块划分、目标矩阵构建、模拟因子结构及偏差指标，并检查样本相似性比较是否具有独立基准。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出偏差幅度，所述优势也未覆盖任意因子结构。
原文摘要
Introduction: Oblique Target-rotation in the context of exploratory factor analysis is a relevant method for the investigation of the oblique independent clusters model. It was argued that minimizing single cross-loadings by means of target rotation may lead to large effects of sampling error on the target rotated factor solutions. Method: In order to minimize effects of sampling error on results of Target-rotation we propose to compute the mean cross-loadings for each block of salient loadings of the independent clusters model and to perform target rotation for the block-wise mean cross-loadings. The resulting transformation-matrix is than applied to the complete unrotated loading matrix in order to produce mean Target-rotated factors. Results: A simulation study based on correlated independent factor models revealed that mean oblique Target-rotation resulted in smaller negative bias of factor inter-correlations than conventional Target-rotation based on single loadings, especially when sample size was small and when the number of factors was large. An empirical example revealed that the similarity of Target-rotated factors computed for small subsamples with Target-rotated factors of the total sample was more pronounced for mean Target-rotation than for conventional Target-rotation. Discussion: Mean Target-rotation can be recommended in the context of oblique independent factor models, especially for small samples. An R-script and an SPSS-script for this form of Target-rotation are provided in the Appendix.