The effect of varying degrees of nonnormality in structural equation modeling
The effect of varying degrees of nonnormality in structural equation modeling
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DOI：
10.1207/s15328007sem1201_1
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发表时间：
2005-01-01
影响因子：
6
通讯作者：
Lomax, RG
中科院分区：
文献类型：
--
作者：
Lei, M;Lomax, RG
本仿真研究考察了结构方程模型在广义最小二乘和极大似然两种估计方法和4种样本量下对不同程度非正态性的鲁棒性。One hundred.。25,500和1000。每一个轻微和严重的异常度都由纯偏态、纯峰度和偏态和峰度组成。分析了参数估计的偏差和标准误差。此外。进行方差分析，考察3个因素对多个拟合优度指标的影响。研究发现，参数估计的标准误差不受估计方法和非正态性条件的显著影响。正如预期的那样，在较大的样本量下，标准误差减小。参数估计对非正态性比样本大小和估计方法更敏感。与规范拟合指数相比，卡方是最不稳健的模型拟合指数。非归一化拟合指数和比较拟合指数。为获得准确的参数估计，建议样本量为100或更多。
This simulation study investigated the robustness of structural equation modeling to different degrees of nonnormality under 2 estimation methods, generalized least squares and maximum likelihood, and 4 sample sizes. 100. 250, 500, and 1,000. Each of the slight and severe nonnormality degrees was comprised of pure skewness, pure kurtosis, and both skewness and kurtosis. Bias and standard errors of parameter estimates were analyzed. In addition. an analysis of variance was conducted to investigate the effects of the 3 factors on several goodness-of-fit indexes. The study found that standard errors of parameter estimates were not significantly affected by estimation methods and nonnormality conditions. As expected, standard errors decreased at larger sample sizes. Parameter estimates were more sensitive to nonnormality than to sample size and estimation method. Chi-square was the least robust model fit index compared with Normed Fit Index. Nonnormed Fit Index, and Comparative Fit Index. Sample sizes of 100 or more are recommended for accurate parameter estimates.