Hidden Markov item response theory models for responses and response times

Dylan Molenaar, D.L. Oberski, J.K. Vermunt, Paul De Boeck

Research output: Contribution to journalArticleScientificpeer-review

46 Citations (Scopus)


Current approaches to model responses and response times to psychometric tests solely focus on between-subject differences in speed and ability. Within subjects, speed and ability are assumed to be constants. Violations of this assumption are generally absorbed in the residual of the model. As a result, within-subject departures from the between-subject speed and ability level remain undetected. These departures may be of interest to the researcher as they reflect differences in the response processes adopted on the items of a test. In this article, we propose a dynamic approach for responses and response times based on hidden Markov modeling to account for within-subject differences in responses and response times. A simulation study is conducted to demonstrate acceptable parameter recovery and acceptable performance of various fit indices in distinguishing between different models. In addition, both a confirmatory and an exploratory application are presented to demonstrate the practical value of the modeling approach.
KEYWORDS: Conditional independence, dynamic modeling, hidden Markov modeling, item response theory, latent class models, response time modeling
Original languageEnglish
Pages (from-to)606-626
JournalMultivariate Behavioral Research
Issue number5
Publication statusPublished - 2 Sept 2016


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