Bootstrap-corrected ADF test statistics in covariance structure analysis.
Bootstrap-corrected ADF test statistics in covariance structure analysis.
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DOI：
10.1111/j.2044-8317.1994.tb01025.x
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发表时间：
1994-05
期刊：
影响因子：
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通讯作者：
Y. Yung;P. Bentler
中科院分区：
文献类型：
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作者：
Y. Yung;P. Bentler
协方差结构分析（CSA）的渐近无分布（ADF）检验统计量在模拟研究中表现很差，即它导致关于心理过程模型是否充分的不准确决定。在本研究中，ADF检验统计量的不良性能是由于权重矩阵（W = gamma-1）的估计不足，这是ADF理论中的一个关键量。基于霍尔的偏见减少的角度的Bootstrap程序，提出了校正ADF检验统计量。结果表明，自举校正的ADF检验统计量的添加剂偏差产生所需的尾部行为的样本量达到500的15个变量的3个因素的验证性因子分析模型，即使观察到的变量的分布是不多元正态和潜在的因素是依赖的。这些结果有助于恢复ADF理论在CSA。
The asymptotically distribution-free (ADF) test statistic for covariance structure analysis (CSA) has been reported to perform very poorly in simulation studies, i.e. it leads to inaccurate decisions regarding the adequacy of models of psychological processes. It is shown in the present study that the poor performance of the ADF test statistic is due to inadequate estimation of the weight matrix (W = gamma -1), which is a critical quantity in the ADF theory. Bootstrap procedures based on Hall's bias reduction perspective are proposed to correct the ADF test statistic. It is shown that the bootstrap correction of additive bias on the ADF test statistic yields the desired tail behaviour as the sample size reaches 500 for a 15-variable-3-factor confirmatory factor-analytic model, even if the distribution of the observed variables is not multivariate normal and the latent factors are dependent. These results help to revive the ADF theory in CSA.