The Consequences of Ignoring Multilevel Data Structures in Nonhierarchical Covariance Modeling
The Consequences of Ignoring Multilevel Data Structures in Nonhierarchical Covariance Modeling
复制标题
DOI：
10.1207/s15328007sem0803_1
复制
发表时间：
2001-01-01
影响因子：
6
通讯作者：
Julian, Marc W.
中科院分区：
文献类型：
--
作者：
Julian, Marc W.
本研究使用蒙特卡罗模拟检验了在非分层协方差建模中忽略多层数据结构的影响。根据3个设计因素生成多水平样本数据：(a)类内相关性，(b)组和成员配置，以及(c)与多水平数据相关的组间和组内方差成分基础的模型。然后将忽略多层结构的协方差模型拟合到数据中。结果表明，当变量表现出最小程度的类内相关性时，卡方模型/数据拟合统计量、参数估计量和标准误差估计量相对无偏。然而，随着类内相关水平的增加，卡方统计量、参数及其标准误差都出现估计问题。特定的组/成员配置以及潜在的组间和组内模型结构进一步加剧了在多层数据的非分层分析中遇到的估计问题。
This study examined the effects of ignoring multilevel data structures in nonhierarchical covariance modeling using a Monte Carlo simulation. Multilevel sample data were generated with respect to 3 design factors: (a) intraclass correlation, (b) group and member configuration, and (c) the models that underlie the between-group and within-group variance components associated with multilevel data. Covariance models that ignored the multilevel structure were then fit to the data. Results indicated that when variables exhibit minimal levels of intraclass correlation, the chi-square model/data fit statistic, the parameter estimators, and the standard error estimators are relatively unbiased. However, as the level of intraclass correlation increases, the chi-square statistic, the parameters, and their standard errors all exhibit estimation problems. The specific group/member configurations as well as the underlying between-group and within-group model structures further exacerbate the estimation problems encountered in the nonhierarchical analysis of multilevel data.