Detecting Individual Differences in Change: Methods and Comparisons
Detecting Individual Differences in Change: Methods and Comparisons
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DOI：
10.1080/10705511.2014.936096
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发表时间：
2015-07-03
影响因子：
6
通讯作者：
Wang, Lijuan (Peggy)
中科院分区：
文献类型：
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作者：
Ke, Zijun;Wang, Lijuan (Peggy)
本研究检验并比较了检测个体差异变化的各种统计方法。考虑到3个问题，包括检验形式（特定与广义）、估计程序（约束与非约束）和非正态性，我们评估了4个方差检验，包括特定Wald方差检验、广义Wald方差检验、特定似然比（LR）方差检验和广义LR方差检验，在正态和非正态数据的约束和无约束估计下。对于约束估计过程，评价了混合分布法和alpha校正法处理边界问题的性能。为了处理非正态性问题，我们对Wald测试使用了夹心标准误差（SE）估计器，对LR测试使用了Satorra-Bentler缩放校正。仿真结果表明，检验方差参数和相关协方差（广义）比单独检验方差（特定）更有效，除非真正的协方差为零。此外，约束估计下的方差检验在更高的经验功率和更好的控制I型错误率方面优于无约束估计下的方差检验。在所有研究的检验中，对于正态和非正态数据，具有约束估计程序的稳健广义LR和Wald方差检验通常比其他检验更强大，并且在检验方差成分时具有更好的I型错误率。讨论了具体方差检验与广义方差检验、约束估计与无约束估计的比较结果。
This study examined and compared various statistical methods for detecting individual differences in change. Considering 3 issues including test forms (specific vs. generalized), estimation procedures (constrained vs. unconstrained), and nonnormality, we evaluated 4 variance tests including the specific Wald variance test, the generalized Wald variance test, the specific likelihood ratio (LR) variance test, and the generalized LR variance test under both constrained and unconstrained estimation for both normal and nonnormal data. For the constrained estimation procedure, both the mixture distribution approach and the alpha correction approach were evaluated for their performance in dealing with the boundary problem. To deal with the nonnormality issue, we used the sandwich standard error (SE) estimator for the Wald tests and the Satorra-Bentler scaling correction for the LR tests. Simulation results revealed that testing a variance parameter and the associated covariances (generalized) had higher power than testing the variance solely (specific), unless the true covariances were zero. In addition, the variance tests under constrained estimation outperformed those under unconstrained estimation in terms of higher empirical power and better control of Type I error rates. Among all the studied tests, for both normal and nonnormal data, the robust generalized LR and Wald variance tests with the constrained estimation procedure were generally more powerful and had better Type I error rates for testing variance components than the other tests. Results from the comparisons between specific and generalized variance tests and between constrained and unconstrained estimation were discussed.