Academic Journal
A Latent Hidden Markov Model for Process Data.
| Τίτλος: | A Latent Hidden Markov Model for Process Data. |
|---|---|
| Συγγραφείς: | Tang X; University of Arizona, 617 N. Santa Rita Ave., Tucson, AZ , 85721, USA. xytang@math.arizona.edu. |
| Πηγή: | Psychometrika [Psychometrika] 2024 Mar; Vol. 89 (1), pp. 205-240. Date of Electronic Publication: 2023 Nov 07. |
| Τύπος έκδοσης: | Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Cambridge University Press Country of Publication: England NLM ID: 0376503 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1860-0980 (Electronic) Linking ISSN: 00333123 NLM ISO Abbreviation: Psychometrika Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2025- : Cambridge : Cambridge University Press Original Publication: Research Triangle Park, VA : Psychometric Society |
| Ιατρικοί όροι (MeSH): | Psychometrics*/methods , Markov Chains* , Problem Solving* , Models, Statistical*, Humans ; Computer Simulation |
| Περίληψη: | Response process data from computer-based problem-solving items describe respondents' problem-solving processes as sequences of actions. Such data provide a valuable source for understanding respondents' problem-solving behaviors. Recently, data-driven feature extraction methods have been developed to compress the information in unstructured process data into relatively low-dimensional features. Although the extracted features can be used as covariates in regression or other models to understand respondents' response behaviors, the results are often not easy to interpret since the relationship between the extracted features, and the original response process is often not explicitly defined. In this paper, we propose a statistical model for describing response processes and how they vary across respondents. The proposed model assumes a response process follows a hidden Markov model given the respondent's latent traits. The structure of hidden Markov models resembles problem-solving processes, with the hidden states interpreted as problem-solving subtasks or stages. Incorporating the latent traits in hidden Markov models enables us to characterize the heterogeneity of response processes across respondents in a parsimonious and interpretable way. We demonstrate the performance of the proposed model through simulation experiments and case studies of PISA process data. (© 2023. The Author(s), under exclusive licence to The Psychometric Society.) |
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| Grant Information: | DMS-2310664 National Science Foundation |
| Contributed Indexing: | Keywords: hidden Markov models; latent variable; problem-solving behaviors; response process |
| Entry Date(s): | Date Created: 20231107 Date Completed: 20240501 Latest Revision: 20260304 |
| Update Code: | 20260305 |
| DOI: | 10.1007/s11336-023-09938-1 |
| PMID: | 37934358 |
| Βάση Δεδομένων: | MEDLINE |
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