An overview of Markov chain methods for the study of stage-sequential developmental processes
An overview of Markov chain methods for the study of stage-sequential developmental processes
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DOI：
10.1037/0012-1649.44.2.457
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发表时间：
2008-03-01
影响因子：
4
通讯作者：
Kaplan, David
中科院分区：
文献类型：
--
作者：
Kaplan, David
本文提出了一个概述的定量方法的研究阶段顺序发展的基础上扩展的马尔可夫链模型。提出了四种方法来增强这种方法的灵活性：显式马尔可夫模型，隐马尔可夫模型，隐转移分析，和混合隐马尔可夫模型。混合隐马尔可夫模型的一个特殊情况下，所谓的移动-逗留模型，在这项研究中使用。无条件和有条件的模型估计的显式马尔可夫模型和潜在的马尔可夫模型，其中的条件模型包括贫困状况的措施。简要讨论了使用Mplus软件环境的模型规范、估计和测试问题，并提供了Mplus输入语法。作者将这4种方法应用于一个幼儿园队列儿童早期阅读能力阶段性发展的例子。
This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model. A special case of the mixture latent Markov model, the so-called mover-stayer model, is used in this study. Unconditional and conditional models are estimated for the manifest Markov model and the latent Markov model, where the conditional models include a measure of poverty status. Issues of model specification, estimation, and testing using the Mplus software environment are briefly discussed, and the Mplus input syntax is provided. The author applies these 4 methods to a single example of stage-sequential development in reading competency in the early school years, using data from the Early Childhood Longitudinal Study-Kindergarten Cohort.