Chan W, Yung Y F, Bentler P M
Multivariate Behav Res. 1995 Oct 1;30(4):453-9. doi: 10.1207/s15327906mbr3004_1.
In covariance structure analysis, the asymptotically distribution-free (ADF) method fails to work satisfactorily unless the sample is extremely large. Simulation studies report that the ADF test statistics observed arc usually too large and correct models arc then over-rejected. It is known that the accuracy of the ADF test statistic depends on the estimation of the weight matrix. In existing literature and computer software, a biased estimator W is used as an estimate of the unknown weight matrix. In this article. we suggest that W, an unbiased estimate of the weight matrix, may eliminate the small or intermediate sample size bias of the ADF test statistic. Results show that the test statistics based on W and W arc highly similar. The poor performance of the ADF method was not caused by the use of a biased weight matrix in the model studied in this article.
在协方差结构分析中,渐近无分布(ADF)方法除非样本极大,否则无法令人满意地发挥作用。模拟研究报告称,观察到的ADF检验统计量通常过大,正确的模型随后被过度拒绝。已知ADF检验统计量的准确性取决于权重矩阵的估计。在现有文献和计算机软件中,使用有偏估计量W作为未知权重矩阵的估计。在本文中,我们建议,权重矩阵的无偏估计量W可能会消除ADF检验统计量在小样本或中等样本量时的偏差。结果表明,基于W和W的检验统计量高度相似。本文所研究模型中ADF方法的不佳表现并非由使用有偏权重矩阵所致。