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Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR

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  • Magis, David
  • Raîche, Gilles

Abstract

This paper outlines a computerized adaptive testing (CAT) framework and presents an R package for the simulation of response patterns under CAT procedures. This package, called catR, requires a bank of items, previously calibrated according to the four-parameter logistic (4PL) model or any simpler logistic model. The package proposes several methods to select the early test items, several methods for next item selection, different estimators of ability (maximum likelihood, Bayes modal, expected a posteriori, weighted likelihood), and three stopping rules (based on the test length, the precision of ability estimates or the classification of the examinee). After a short description of the different steps of a CAT process, the commands and options of the catR package are presented and practically illustrated.

Suggested Citation

  • Magis, David & Raîche, Gilles, 2012. "Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i08).
    • Handle: RePEc:jss:jstsof:v:048:i08
    DOI: http://hdl.handle.net/10.18637/jss.v048.i08
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    References listed on IDEAS

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    1. Frederic Lord, 1983. "Unbiased estimators of ability parameters, of their variance, and of their parallel-forms reliability," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 233-245, June.
    2. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    3. Wim Linden, 1998. "Bayesian item selection criteria for adaptive testing," Psychometrika, Springer;The Psychometric Society, vol. 63(2), pages 201-216, June.
    4. R. Darrell Bock, 1972. "Estimating item parameters and latent ability when responses are scored in two or more nominal categories," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 29-51, March.
    5. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
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    Cited by:

    1. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    2. David Magis, 2015. "A Note on the Equivalence Between Observed and Expected Information Functions With Polytomous IRT Models," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 96-105, February.
    3. Magis, David & Barrada, Juan Ramon, 2017. "Computerized Adaptive Testing with R: Recent Updates of the Package catR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(c01).
    4. Wickelmaier, Florian & Strobl, Carolin & Zeileis, Achim, 2012. "Psychoco: Psychometric Computing in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i01).
    5. David Magis, 2016. "Efficient Standard Error Formulas of Ability Estimators with Dichotomous Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 184-200, March.
    6. Chalmers, R. Philip, 2016. "Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i05).

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