Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis Under Conditions of Multivariate Nonnormality
Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis Under Conditions of Multivariate Nonnormality
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DOI：
10.1207/s15328007sem0703_2
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发表时间：
2000-01-01
影响因子：
6
通讯作者：
Fouladi, Rachel T.
中科院分区：
文献类型：
--
作者：
Fouladi, Rachel T.
假设结构模型是否是一组变量之间关联模式的适当表示的问题可以使用多种统计程序来解决。这些过程包括协方差结构分析技术和相关性结构分析技术，其中协方差结构过程基于协方差的分布理论，相关性结构过程基于相关性的分布理论。本文提供了一个概述的标准和修改的正常理论和渐近分布的自由协方差和相关性结构分析技术，还详细介绍了Monte Carlo模拟结果的I型和II型误差控制的结构模型类型，模型中的变量数量，样本量和分布的非正态性的函数。本Monte Carlo模拟清楚地表明，结构分析技术的鲁棒性和非鲁棒性作为模型结构和数据条件的函数而变化。这些结果的用户的结构分析技术的影响被认为是在当前的软件可用性的背景下。
Questions of whether hypothesized structure models are appropriate representations of the pattern of association among a group of variables can be addressed using a wide variety of statistical procedures. These procedures include covariance structure analysis techniques and correlation structure analysis techniques, in which covariance structure procedures are based on distribution theory for covariances, and correlation structure procedures are based on distribution theory for correlations. The present article provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and also details Monte Carlo simulation results on the Type I and Type II error control as a function of structure model type, number of variables in the model, sample size, and distributional nonnormality. The present Monte Carlo simulation demonstrates clearly that the robustness and nonrobustness of structure analysis techniques vary as a function of the structure of the model and the data conditions. Implications of these results for users of structure analysis techniques are considered in the context of current software availability.