Application of structural equation models to quality of life
Application of structural equation models to quality of life
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DOI：
10.1207/s15328007sem1203_5
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发表时间：
2005-01-01
影响因子：
6
通讯作者：
Hao, YT
中科院分区：
文献类型：
--
作者：
Lee, SY;Song, XY;Hao, YT
生活质量（QOL）已成为医疗保健的重要概念。由于生活质量是一个多维度的概念，最好的评价是由一些潜在的结构，它是公认的，潜变量模型，如探索性因素分析（EFA）和验证性因素分析（CFA）是分析生活质量数据的有用工具。近年来，QOL研究者们逐渐认识到结构方程模型（SEM）的潜力，它是EFA和CFA的推广，在模型中建立回归型方程，用于研究潜在结构对QOL或健康相关QOL的影响。然而，由于QOL问卷中的项目通常是在有序分类量表上测量的，因此基于正态分布的SEM标准方法可能会产生误导性结果。在这篇文章中，我们提出了一种方法，使用阈值规范来处理有序分类变量。然后，在有序分类数据的基础上，提出了一种分析CFA和SEM的最大似然（ML）方法。这种方法产生的NIL估计的参数，估计的潜在结构的分数，和贝叶斯信息标准的模型比较。这些方法用从WHOQOL组获得的数据集来说明。
Quality of life (QOL) has become an important concept for health care. As QOL is a multidimensional concept that is best evaluated by a number of latent constructs, it is well recognized that latent variable models, such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are useful tools for analyzing QOL data. Recently, QOL researchers have realized the potential of structural equation modeling (SEM), which is a generalization of EFA and CFA in formulating a regression type equation in the model for studying the effects of the latent constructs to the QOL or health-related QOL. However, as the items in a QOL questionnaire are usually measured on an ordinal categorical scale, standard methods in SEM that are based on the normal distribution may produce misleading results. In this article, we propose an approach that uses a threshold specification to handle the ordinal categorical variables. Then, on the basis of observed ordinal categorical data, a maximum likelihood (ML) approach for analyzing CFA and SEM is introduced. This approach produces the NIL estimates of the parameters, estimates of the scores of latent constructs, and the Bayesian information criterion for model comparison. The methodologies are illustrated with a dataset that was obtained from the WHOQOL group.