University of Notre Dame.
Renmin University of China.
Multivariate Behav Res. 2019 Nov-Dec;54(6):840-855. doi: 10.1080/00273171.2019.1596060. Epub 2019 Apr 8.
Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. However, conventional SEM methods are not crafted to handle data with a large number of variables (). A large can cause , the most widely used likelihood ratio statistic, to depart drastically from the assumed chi-square distribution even with normally distributed data and a relatively large sample size . A key element affecting this behavior of is its mean bias. The focus of this article is to determine the cause of the bias. To this end, empirical means of via Monte Carlo simulation are used to obtain the empirical bias. The most effective predictors of the mean bias are subsequently identified and their predictive utility examined. The results are further used to predict type I errors of . The article also illustrates how to use the obtained results to determine the required sample size for to behave reasonably well. A real data example is presented to show the effect of the mean bias on model inference as well as how to correct the bias in practice.
调查数据通常包含许多变量。结构方程建模 (SEM) 常用于分析此类数据。然而，传统的 SEM 方法并不是为处理大量变量的数据而设计的（）。大量的变量（）会导致最广泛使用的似然比统计量，即使在正态分布数据和相对较大的样本量下，也会严重偏离假设的卡方分布。影响这种（）行为的一个关键因素是其均值偏差。本文的重点是确定偏差的原因。为此，通过蒙特卡罗模拟获得经验均值偏差（）。随后确定了导致偏差的最有效预测因素，并检验了它们的预测效用。结果进一步用于预测（）的Ⅰ类错误。本文还说明了如何使用获得的结果来确定 的样本量，以使其表现良好。通过一个真实的数据示例，展示了均值偏差对模型推断的影响，以及如何在实践中纠正偏差。
