Outlying Observation Diagnostics in Growth Curve Modeling
Outlying Observation Diagnostics in Growth Curve Modeling
复制标题
生长曲线建模中的外围观察诊断
DOI：
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复制
发表时间：
2017
影响因子：
3.8
通讯作者：
Z. Zhang
中科院分区：
文献类型：
--
作者：
Xin Tong;Z. Zhang
摘要 增长曲线模型广泛用于研究增长和变化现象。社会和行为科学领域的许多研究表明，没有任何异常观察的数据是一个例外，特别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不正确的统计推断。因此，在生长曲线建模中识别异常观测值至关重要。本研究通过对线性增长曲线模型进行蒙特卡罗模拟研究，通过改变样本大小、测量次数以及外围观测的比例、几何形状和类型等因素，对外围观测诊断的六种方法进行比较评估。有人建议，成功检测异常观测值的最大机会来自于使用多种方法、比较其结果并根据研究目的做出决定。还提供了一个真实的数据分析例子来说明六种外围观测诊断方法的应用。
ABSTRACT 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.
将数据拟合到模型：使用两个散点图进行结构方程建模诊断。
影响因子：
7
作者：
Yuan,Ke-Hai;Hayashi,Kentaro
通讯作者：
Hayashi,Kentaro
协方差结构分析中异常值对估计量和检验的影响。
DOI：
10.1348/000711001159366
发表时间：
2001
期刊：
The British journal of mathematical and statistical psychology
影响因子：
--
作者：
Yuan,KH;Bentler,PM
通讯作者：
Bentler,PM