title: Hidden Markov Item Response Theory Models for Responses and Response Times.
abstract: 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.
authors.affiliation: a University of Amsterdam.
authors.forename: Dylan
authors.initials: D
authors.lastname: Molenaar
authors.affiliation: b Tilburg University.
authors.forename: Daniel
authors.initials: D
authors.lastname: Oberski
authors.affiliation: b Tilburg University.
authors.forename: Jeroen
authors.initials: J
authors.lastname: Vermunt
authors.affiliation: c Ohio State University.
authors.forename: Paul
authors.initials: P
authors.lastname: De Boeck
mesh_terms: D000465:Algorithms; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011582:Psychological Theory; D011594:Psychometrics; D011930:Reaction Time
keywords: Conditional independence; dynamic modeling; hidden Markov modeling; item response theory; latent class models; response time modeling