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A Lognormal Model for Response Times on Test Items

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  • Wim J. van der Linden

Abstract

A lognormal model for the response times of a person on a set of test items is investigated. The model has a parameter structure analogous to the two-parameter logistic response models in item response theory, with a parameter for the speed of each person as well as parameters for the time intensity and discriminating power of each item. It is shown how these parameters can be estimated by a Markov chain Monte Carlo method (Gibbs sampler). The method was used to analyze response times for the adaptive version of a test from the Armed Services Vocational Aptitude Battery. The same data set was used to test the validity of the model against a normal model using posterior predictive checks on the response times. The lognormal model showed an excellent fit to the data, whereas the normal model seemed unable to allow for a characteristic skewness of the response time distributions. The addition of an equality constraint on the discrimination parameters led only to a slight loss of fit. The potential use of the model for improving the daily practice of testing is indicated.

Suggested Citation

  • Wim J. van der Linden, 2006. "A Lognormal Model for Response Times on Test Items," Journal of Educational and Behavioral Statistics, , vol. 31(2), pages 181-204, June.
    • Handle: RePEc:sae:jedbes:v:31:y:2006:i:2:p:181-204
    DOI: 10.3102/10769986031002181
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    Citations

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    Cited by:

    1. Chun Wang & Gongjun Xu & Zhuoran Shang, 2018. "A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 223-254, March.
    2. Edison M. Choe & Jinming Zhang & Hua-Hua Chang, 2018. "Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 650-673, September.
    3. Maria Bolsinova & Jesper Tijmstra, 2019. "Modeling Differences Between Response Times of Correct and Incorrect Responses," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 1018-1046, December.
    4. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.

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