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Joint consistency of nonparametric item characteristic curve and ability estimation

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Abstract

The simultaneous and nonparametric estimation of latent abilities and item characteristic curves is considered. The asymptotic properties of ordinal ability estimation and kernel smoothed nonparametric item characteristic curve estimation are investigated under very general assumptions on the underlying item response theory model as both the test length and the sample size increase. A large deviation probability inequality is stated for ordinal ability estimation. The mean squared error of kernel smoothed item characteristic curve estimates is studied and a strong consistency result is obtained showing that the worst case error in the item characteristic curve estimates over all items and ability levels converges to zero with probability equal to one.

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References

  • Billingsley, P. (1986).Probability and measure. New York: Wiley.

    Google Scholar 

  • Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm.Psychometrika, 46, 443–459.

    Google Scholar 

  • Devroye, L. P. (1978). The uniform convergence of the Nadaraya-Watson regression function Estimate.Canadian Journal of Statistics, 6, 179–191.

    Google Scholar 

  • Fan, J., & Truong, Y. K. (1993). Nonparametric regression with errors in variables.The Annals of Statistics, 21, 1900–1925.

    Google Scholar 

  • Haberman, S. J. (1977). Maximum likelihood estimates in exponential response models.Annals of Statistics, 5, 815–841.

    Google Scholar 

  • Härdle, W. (1990).Applied nonparametric regression. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hastie, T. J., & Tibshirani, R. J. (1990).Generalized additive models. London: Chapman Hall.

    Google Scholar 

  • Hoeffding, W. (1986). Probability inequalities for sums of bounded random variables.Encyclopedia of Statistical Sciences, 7, 222–225.

    Google Scholar 

  • Nadaraya, E. A. (1964). On estimating regression.Probability Theory and Its Applications, 9, 141–142.

    Google Scholar 

  • Neyman, J., & Scott, E. L. (1948). Consistent estimates based on partially consistent observations.Econometrika, 16, 1–32.

    Google Scholar 

  • Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation.Psychometrika, 56, 611–630.

    Google Scholar 

  • Ramsay, J. O. (1994).TESTGRAF: A program for the graphical analysis of multiple choice test and questionnaire data. Unpublished user's guide to TESTGRAF.

  • Ramsay, J. O., & Winsberg, S. (1991). Maximum marginal likelihood estimation for semiparametric item analysis.Psychometrika, 56, 365–379.

    Google Scholar 

  • Samejima, F. (1979).A new family of models for the multiple-choice item (Research report No. 79-4). Knoxville, TN: University of Tennessee, Department of Psychology.

    Google Scholar 

  • Samejima, F. (1981).Efficient methods of estimating the operating characteristic of item response categories and challenge to a new model for the multiple-choice item. Unpublished manuscript, Knoxville, TN: University of Tennessee, Department of Psychology.

    Google Scholar 

  • Samejima, F. (1984).Plausibility functions of the Iowa Vocabulary Test items estimated by the simple sum procedure of the conditional P.D.F.approach (Research report No. 84-1). Knoxville, TN: University of Tennessee, Department of Psychology.

    Google Scholar 

  • Samejima, F. (1988).Advancement of latent trait theory (Research Report No. 79-4). Knoxville, TN: University of Tennessee, Department of Psychology.

    Google Scholar 

  • Samejima, F. (1990).Differential weight procedure of the conditional p.d.f.approach for estimating the operating characteristic of discrete item responses (ONR research report No. 90-4). Knoxville, TN: University of Tennessee, Department of Psychology.

    Google Scholar 

  • Schuster, E. F. (1972). Joint asymptotic distribution of the estimated regression function at a finite number of distinct points.Annals of Mathematical Statistics, 43, 84–88.

    Google Scholar 

  • Serfling, R. J. (1980).Approximation theorems of mathematical statistics. New York: John Wiley.

    Google Scholar 

  • Watson, G. S. (1964). Smooth regression analysis.Sankhya, Series A, 26, 359–372.

    Google Scholar 

  • Wingersky, M. S., Patrick, R., & Lord, F. M. (1988).LOGIST User's Guide. Princeton, New Jersey: Educational Testing Service.

    Google Scholar 

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Douglas, J. Joint consistency of nonparametric item characteristic curve and ability estimation. Psychometrika 62, 7–28 (1997). https://doi.org/10.1007/BF02294778

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  • DOI: https://doi.org/10.1007/BF02294778

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