A Semiparametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing

Chun Wang, Zhewen Fan, Hua-Hua Chang and Jeffrey A. Douglas
Additional contact information
Chun Wang: University of Minnesota
Zhewen Fan: Precision Therapeutics, Inc.
Hua-Hua Chang: University of Illinois at Urbana-Champaign

Journal of Educational and Behavioral Statistics, 2013, vol. 38, issue 4, 381-417

Abstract: The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees’ responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the advantage of conciseness, but may suffer from reduced flexibility to fit real data. We propose a semiparametric approach, specifically, the Cox proportional hazards model with a latent speed covariate to model the RTs, embedded within the hierarchical framework proposed by van der Linden to model the RTs and response accuracy simultaneously. This semiparametric approach combines the flexibility of nonparametric modeling and the brevity and interpretability of the parametric modeling. A Markov chain Monte Carlo method for parameter estimation is given and may be used with sparse data obtained by computerized adaptive testing. Both simulation studies and real data analysis are carried out to demonstrate the applicability of the new model.

Keywords: response time; Cox proportional hazards model; partial likelihood; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:38:y:2013:i:4:p:381-417

DOI: 10.3102/1076998612461831

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