Columbia University, New York, USA.
Psychometrika. 2020 Sep;85(3):775-811. doi: 10.1007/s11336-020-09725-2. Epub 2020 Sep 14.
Process data, which are temporally ordered sequences of categorical observations, are of recent interest due to its increasing abundance and the desire to extract useful information. A process is a collection of time-stamped events of different types, recording how an individual behaves in a given time period. The process data are too complex in terms of size and irregularity for the classical psychometric models to be directly applicable and, consequently, new ways for modeling and analysis are desired. We introduce herein a latent theme dictionary model for processes that identifies co-occurrent event patterns and individuals with similar behavioral patterns. Theoretical properties are established under certain regularity conditions for the likelihood-based estimation and inference. A nonparametric Bayes algorithm using the Markov Chain Monte Carlo method is proposed for computation. Simulation studies show that the proposed approach performs well in a range of situations. The proposed method is applied to an item in the 2012 Programme for International Student Assessment with interpretable findings.
由于其数量的增加以及提取有用信息的需求，过程数据（即按时间顺序排列的分类观测序列）最近受到了关注。过程是一组不同类型的时间戳事件的集合，记录了个体在给定时间段内的行为方式。从大小和不规则性方面来看，过程数据过于复杂，无法直接应用经典心理测量模型，因此需要寻找新的建模和分析方法。我们在此引入了一种用于过程的潜在主题字典模型，该模型可以识别并发事件模式和具有相似行为模式的个体。在某些正则条件下，我们建立了基于似然的估计和推断的理论性质。我们提出了一种基于马尔可夫链蒙特卡罗方法的非参数贝叶斯算法来进行计算。模拟研究表明，该方法在多种情况下表现良好。我们将该方法应用于 2012 年国际学生评估项目中的一个项目，并得出了可解释的结果。
