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

Author

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  • Guanhua Fang

    (Columbia University)

  • Zhiliang Ying

    (Columbia University)

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.

Suggested Citation

  • Guanhua Fang & Zhiliang Ying, 2020. "Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 775-811, September.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09725-2
    DOI: 10.1007/s11336-020-09725-2
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    1. Ishwaran H. & Rao J.S., 2003. "Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 438-455, January.
    2. Oecd, 2016. "PISA 2015 Results in Focus," PISA in Focus 67, OECD Publishing.
    3. Dunson, David B. & Xing, Chuanhua, 2009. "Nonparametric Bayes Modeling of Multivariate Categorical Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1042-1051.
    4. Ke Deng & Zhi Geng & Jun S. Liu, 2014. "Association pattern discovery via theme dictionary models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 319-347, March.
    5. Guanhua Fang & Jingchen Liu & Zhiliang Ying, 2019. "On the Identifiability of Diagnostic Classification Models," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 19-40, March.
    6. W. Gibson, 1959. "Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis," Psychometrika, Springer;The Psychometric Society, vol. 24(3), pages 229-252, September.
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