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Maximization of Some Types of Information for Unidentified Item Response Models with Guessing Parameters

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  • Haruhiko Ogasawara

    (Otaru University of Commerce)

Abstract

It is known that a family of fixed-effects item response models with equal discrimination and different guessing parameters has no model identifiability. For this family, some types of information including the Fisher information and a new one are maximized to have model identification. The conditions of monotonicity of these types of information with respect to a tuning parameter are given. In the case of the logistic model with guessing parameters, it is shown that maxima do not exist under some parametrization, where negative lower asymptote can be employed without changing the probabilities of correct responses by examinees.

Suggested Citation

  • Haruhiko Ogasawara, 2021. "Maximization of Some Types of Information for Unidentified Item Response Models with Guessing Parameters," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 544-563, June.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:2:d:10.1007_s11336-021-09763-4
    DOI: 10.1007/s11336-021-09763-4
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    References listed on IDEAS

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    1. Fumiko Samejima, 1973. "A comment on Birnbaum's three-parameter logistic model in the latent trait theory," Psychometrika, Springer;The Psychometric Society, vol. 38(2), pages 221-233, June.
    2. Egghe, L., 2012. "Averages of ratios compared to ratios of averages: Mathematical results," Journal of Informetrics, Elsevier, vol. 6(2), pages 307-317.
    3. Wim J. van der Linden & Michelle D. Barrett, 2017. "Erratum to: Linking Item Response Model Parameters," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 273-273, March.
    4. J. Keats, 1974. "Applications of projective transformations to test theory," Psychometrika, Springer;The Psychometric Society, vol. 39(3), pages 359-360, September.
    5. Hao Wu, 2016. "A Note on the Identifiability of Fixed-Effect 3PL Models," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1093-1097, December.
    6. Wim J. Linden & Michelle D. Barrett, 2016. "Linking Item Response Model Parameters," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 650-673, September.
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