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On the Relationships Between Jeffreys Modal and Weighted Likelihood Estimation of Ability Under Logistic IRT Models

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Abstract

This paper focuses on two estimators of ability with logistic item response theory models: the Bayesian modal (BM) estimator and the weighted likelihood (WL) estimator. For the BM estimator, Jeffreys’ prior distribution is considered, and the corresponding estimator is referred to as the Jeffreys modal (JM) estimator. It is established that under the three-parameter logistic model, the JM estimator returns larger estimates than the WL estimator. Several implications of this result are outlined.

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References

  • Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F.M. Lord & M.R. Novick (Eds.), Statistical theories of mental test scores. Reading: Addison-Wesley (Chaps. 17–20).

    Google Scholar 

  • Birnbaum, A. (1969). Statistical theory for logistic mental test models with a prior distribution of ability. Journal of Mathematical Psychology, 6, 258–276.

    Article  Google Scholar 

  • Hoijtink, H., & Boomsma, A. (1995). On person parameter estimation in the dichotomous Rasch model. In G.H. Fischer & I.W. Molenaar (Eds.), Rasch models. Foundations, recent developments, and applications (pp. 53–68). New York: Springer.

    Google Scholar 

  • Jeffreys, H. (1939). Theory of probability. Oxford: Oxford University Press.

    Google Scholar 

  • Jeffreys, H. (1946). An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 186, 453–461.

    Article  PubMed  Google Scholar 

  • Lord, F.M. (1980). Applications of item response theory to practical testing problems. Hillsdale: Lawrence Erlbaum.

    Google Scholar 

  • Lord, F.M. (1983). Unbiased estimators of ability parameters, of their variance, and of their parallel-forms reliability. Psychometrika, 48, 233–245.

    Article  Google Scholar 

  • Lord, F.M. (1984). Maximum likelihood and Bayesian parameter estimation in item response theory (Research Report No. RR-84-30-ONR). Princeton, NJ: Educational Testing Service.

  • Magis, D., & Raîche, G. (2010). An iterative maximum a posteriori estimation of proficiency level to detect multiple local likelihood maxima. Applied Psychological Measurement, 34, 75–90.

    Article  Google Scholar 

  • Meijer, R.R., & Nering, M.L. (1999). Computerized adaptive testing: Overview and introduction. Applied Psychological Measurement, 23, 187–194.

    Article  Google Scholar 

  • Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danish Institute for Educational Research.

  • Samejima, F. (1973). A comment on Birnbaum’s three-parameter logistic model in the latent trait theory. Psychometrika, 38, 221–223.

    Article  Google Scholar 

  • Warm, T.A. (1989). Weighted likelihood estimation of ability in item response models. Psychometrika, 54, 427–450.

    Article  Google Scholar 

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Correspondence to David Magis.

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Magis, D., Raîche, G. On the Relationships Between Jeffreys Modal and Weighted Likelihood Estimation of Ability Under Logistic IRT Models. Psychometrika 77, 163–169 (2012). https://doi.org/10.1007/s11336-011-9233-5

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  • DOI: https://doi.org/10.1007/s11336-011-9233-5

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