CAN TEST STATISTICS IN COVARIANCE STRUCTURE-ANALYSIS BE TRUSTED
CAN TEST STATISTICS IN COVARIANCE STRUCTURE-ANALYSIS BE TRUSTED
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DOI：
10.1037/0033-2909.112.2.351
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发表时间：
1992-09-01
影响因子：
22.4
通讯作者：
KANO, Y
中科院分区：
文献类型：
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作者：
HU, LT;BENTLER, PM;KANO, Y
协方差结构分析使用卡方2拟合优度检验统计量，其充分性未知。当研究者违反样本大小、变量独立性和分布假设时，基于模型的科学结论可能会被扭曲。用蒙特卡罗验证性因子分析研究评价了6个检验统计量的行为。在6个样本量的7种分布条件下，测试的表现显著不同。两个正态理论测试在某些条件下运行良好，但在其他条件下完全失败。一种允许齐次非零峰的检验方法。允许异质边缘峰的测试表现更好。一个无分布检验在所有条件下都表现得非常糟糕，除了最大的样本量。Satorra-Bentler比例检验统计量总体表现最好。
Covariance structure analysis uses chi-2 goodness-of-fit test statistics whose adequacy is not known. Scientific conclusions based on models may be distorted when researchers violate sample size, variate independence, and distributional assumptions. The behavior of 6 test statistics is evaluated with a Monte Carlo confirmatory factor analysis study. The tests performed dramatically differently under 7 distributional conditions at 6 sample sizes. Two normal-theory tests worked well under some conditions but completely broke down under other conditions. A test that permits homogeneous nonzero kurtoses performed variably. A test that permits heterogeneous marginal kurtoses performed better. A distribution-free test performed spectacularly badly in all conditions at all but the largest sample sizes. The Satorra-Bentler scaled test statistic performed best overall.