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The Annual Meeting of the Psychometric Society

The Annual Meeting of the Psychometric Society

IMPS 2017, IMPS 2018: Quantitative Psychology pp 25–37Cite as

NUTS for Mixture IRT Models

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 265))

Abstract

The No-U-Turn Sampler (NUTS) is a relatively new Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior that common MCMC algorithms such as Gibbs sampling or Metropolis Hastings usually exhibit. Given the fact that NUTS can efficiently explore the entire space of the target distribution, the sampler converges to high-dimensional target distributions more quickly than other MCMC algorithms and is hence less computational expensive. The focus of this study is on applying NUTS to one of the complex IRT models, specifically the two-parameter mixture IRT (Mix2PL) model, and further to examine its performance in estimating model parameters when sample size, test length, and number of latent classes are manipulated. The results indicate that overall, NUTS performs well in recovering model parameters. However, the recovery of the class membership of individual persons is not satisfactory for the three-class conditions. Findings from this investigation provide empirical evidence on the performance of NUTS in fitting Mix2PL models and suggest that researchers and practitioners in educational and psychological measurement should benefit from using NUTS in estimating parameters of complex IRT models.

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Author information

Authors and Affiliations

  1. Southern Illinois University, 62901, Carbondale, IL, USA

    Rehab Al Hakmani & Yanyan Sheng

Authors
  1. Rehab Al Hakmani
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  2. Yanyan Sheng
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Corresponding author

Correspondence to Rehab Al Hakmani .

Editor information

Editors and Affiliations

  1. Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden

    Marie Wiberg

  2. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Steven Culpepper

  3. Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium

    Rianne Janssen

  4. Facultad de Matematicas, Pontificia Universidad Catolica de Chile, Santiago, Chile

    Jorge González

  5. Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands

    Dylan Molenaar

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Al Hakmani, R., Sheng, Y. (2019). NUTS for Mixture IRT Models. In: Wiberg, M., Culpepper, S., Janssen, R., González, J., Molenaar, D. (eds) Quantitative Psychology. IMPS IMPS 2017 2018. Springer Proceedings in Mathematics & Statistics, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-030-01310-3_3

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