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Model Selection for Monotonic Polynomial Item Response Models

  • Carl F. FalkEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 265)

Abstract

One flexible approach for item response modeling involves use of a monotonic polynomial in place of the linear predictor for commonly used parametric item response models. Since polynomial order may vary across items, model selection can be difficult. For polynomial orders greater than one, the number of possible order combinations increases exponentially with test length. I reframe this issue as a combinatorial optimization problem and apply an algorithm known as simulated annealing to aid in finding a suitable model. Simulated annealing resembles Metropolis-Hastings: A random perturbation of polynomial order for some item is generated and acceptance depends on the change in model fit and the current algorithm state. Simulations suggest that this approach is often a feasible way to select a better fitting model.

Keywords

Combinatorial optimization Nonparametric item response theory Monotonic polynomial Balanced incomplete block design 

Notes

Acknowledgements

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [funding reference number RGPIN-2018-05357]. Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), [numéro de référence RGPIN-2018-05357].

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PsychologyMcGill College, McGill UniversityMontrealCanada

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