Bayesian analysis of mixtures in structural equation models with non-ignorable missing data
Bayesian analysis of mixtures in structural equation models with non-ignorable missing data
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DOI：
10.1348/000711009x475187
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发表时间：
2010-11-01
影响因子：
2.6
通讯作者：
Song, Xin-Yuan
中科院分区：
文献类型：
--
作者：
Cai, Jing-Heng;Song, Xin-Yuan
结构方程模型（sem）已被广泛用于确定社会、心理和行为科学中潜在变量和观察变量之间的相互关系。由于异构数据在这些领域的实际研究中非常普遍，因此混合模型的分析受到了文献的广泛关注。在混合sem分析中的一个重要问题是数据缺失的存在，特别是具有不可忽略机制的数据缺失。然而，在分析具有不可忽略的缺失数据的混合sme方面，只做了有限的工作。本文的主要目的是开发一种贝叶斯方法来分析具有未知数量的成分和不可忽略的缺失数据的混合sem。仿真研究表明，采用马尔可夫链蒙特卡罗方法得到的贝叶斯估计是准确的，通过路径采样过程计算出的贝叶斯因子可用于识别正确的组分数量、选择合适的缺失机制以及研究潜在变量对混合SEMs的各种影响。一个关于工作满意度研究的真实数据集被用来证明该方法。
Structural equation models (SEMs) have become widely used to determine the interrelationships between latent and observed variables in social, psychological, and behavioural sciences. As heterogeneous data are very common in practical research in these fields, the analysis of mixture models has received a lot of attention in the literature. An important issue in the analysis of mixture SEMs is the presence of missing data, in particular of data missing with a non-ignorable mechanism. However, only a limited amount of work has been done in analysing mixture SEMs with non-ignorable missing data. The main objective of this paper is to develop a Bayesian approach for analysing mixture SEMs with an unknown number of components and non-ignorable missing data. A simulation study shows that Bayesian estimates obtained by the proposed Markov chain Monte Carlo methods are accurate and the Bayes factor computed via a path sampling procedure is useful for identifying the correct number of components, selecting an appropriate missingness mechanism, and investigating various effects of latent variables in the mixture SEMs. A real data set on a study of job satisfaction is used to demonstrate the methodology.