a University of Virginia.
b University of Notre Dame.
Multivariate Behav Res. 2017 Nov-Dec;52(6):768-788. doi: 10.1080/00273171.2017.1374824.
Growth curve models are widely used for investigating growth and change phenomena. Many studies in social and behavioral sciences have demonstrated that data without any outlying observation are rather an exception, especially for data collected longitudinally. Ignoring the existence of outlying observations may lead to inaccurate or even incorrect statistical inferences. Therefore, it is crucial to identify outlying observations in growth curve modeling. This study comparatively evaluates six methods in outlying observation diagnostics through a Monte Carlo simulation study on a linear growth curve model, by varying factors of sample size, number of measurement occasions, as well as proportion, geometry, and type of outlying observations. It is suggested that the greatest chance of success in detecting outlying observations comes from use of multiple methods, comparing their results and making a decision based on research purposes. A real data analysis example is also provided to illustrate the application of the six outlying observation diagnostic methods.
生长曲线模型广泛用于研究生长和变化现象。许多社会和行为科学的研究表明，没有异常观测值的数据是相当罕见的，尤其是对于纵向收集的数据。忽略异常观测值的存在可能会导致不准确甚至错误的统计推断。因此，在生长曲线建模中识别异常观测值至关重要。本研究通过对线性生长曲线模型进行蒙特卡罗模拟研究，比较了六种异常观测值诊断方法，考虑了样本量、测量次数以及异常观测值的比例、几何形状和类型等因素。研究结果表明，成功检测异常观测值的最大机会来自于使用多种方法，比较它们的结果，并根据研究目的做出决策。还提供了一个实际数据分析的例子来说明这六种异常观测值诊断方法的应用。
