A latent Markov model for the analysis of longitudinal data collected in continuous time:: States, durations, and transitions
A latent Markov model for the analysis of longitudinal data collected in continuous time:: States, durations, and transitions
复制标题
DOI：
10.1037/1082-989x.10.1.65
复制
发表时间：
2005-03-01
影响因子：
7
通讯作者：
Böckenholt, U
中科院分区：
文献类型：
--
作者：
Böckenholt, U
马尔可夫模型为分析和解释心理学应用中的时间依赖提供了一个通用的框架。最近的工作将马尔可夫模型扩展到潜在状态的情况，因为心理状态通常不是直接可观察到的，并且容易受到测量误差的影响。本文提出了隐马尔可夫模型的进一步推广，以允许分析在任意时间点收集的评级数据。这一扩展通过明确地关注处于潜伏状态的持续时间，提供了研究变化过程的新方法。在一个经验抽样应用中，作者表明，这种持续时间分析可以提供关于情绪计时特征的有价值的见解。
Markov models provide a general framework for analyzing and interpreting time dependencies in psychological applications. Recent work extended Markov models to the case of latent states because frequently psychological states are not directly observable and subject to measurement error. This article presents a further generalization of latent Markov models to allow for the analysis of rating data that are collected at arbitrary points in time. This extension offers new ways of investigating change processes by focusing explicitly on the durations that are spent in latent states. In an experience sampling application the author shows that such duration analyses can provide valuable insights about chronometric features of emotions.