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Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data

Published online by Cambridge University Press:  01 January 2025

Guanhua Fang  and
Zhiliang Ying
Guanhua Fang*
Affiliation:
Columbia University
Zhiliang Ying
Affiliation:
Columbia University
*
Correspondence should be made to Guanhua Fang, Columbia University, New York, USA. Email: gf2340@columbia.edu
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Abstract

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.

Keywords

latent theme dictionary modelprocess dataco-occurrent patternidentifiability
Type
Application Reviews and Case Studies
Information
Psychometrika , Volume 85 , Issue 3 , September 2020 , pp. 775 - 811
Copyright
Copyright © 2020 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-020-09725-2) contains supplementary material, which is available to authorized users.

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