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Explanatory Item Response Models pp 213–240Cite as

Person-by-item predictors

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Part of the book series: Statistics for Social Science and Public Policy ((SSBS))

Abstract

In this chapter we consider the inclusion of person-by-item predictors into the model. Unlike person predictors or item predictors, person-by-item predictors vary both within and between persons. The inclusion of person-by-item predictors besides person predictors or item predictors is relevant for modeling various phenomena such as differential item functioning (DIF) and local item dependencies (LID) (see Zwinderman, 1997). To describe models with person-by-item predictors we will distinguish between static and dynamic interaction models. We concentrate here on models for DIF and LID, but the interaction concept is of course more general.

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References

  • Arnold, B.C. & Press, S.J. (1989). Compatible conditional distributions. Journal of the American Statistical Association, 84, 152–156.

    Article  MathSciNet  MATH  Google Scholar 

  • Diggle, P.J., Heagerty, P., Liang, K. & Zeger, S.L. (2002). Analysis of Longitudinal Data (2nd ed.). New York: Oxford University Press.

    Google Scholar 

  • Dorans, N.J. & Holland, P.W. (1993). DIF detection and description: Mantel-Haenszel and standardization. In P.W. Holland & W. Howard (Eds), Differential Item Functioning (pp. 35–66). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Dorans, N.J. & Kulick, E. (1986). Demonstrating the utility of the standardization approach to assessing unexpected differential item performance on the scholastic aptitude test. Journal of Educational Measurement, 23, 355–368.

    Article  Google Scholar 

  • Engelhard, G. (1992). The measurement of writing ability with a many-faceted Rasch model. Applied Measurement in Education, 5, 171–191.

    Article  Google Scholar 

  • Fahrmeir, L. & Tutz, G. (2001). Multivariate Statistical Modeling Based on Generalized Linear Models (2nd ed.). New York: Springer.

    Book  Google Scholar 

  • Gelman, A. & Speed, T.P. (1993). Characterizing a joint probability distribution by conditionals. Journal of the Royal Statistical Society. Series B, 55, 185–188.

    MathSciNet  MATH  Google Scholar 

  • Holland, P.W. & Thayer, D.T. (1988). Differential item functioning and the Mantel-Haenszel procedure. In H. Wainer & H.I. Braun (Eds), Test Validity (pp. 129–145). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Holland, P.W. & Wainer, H. (1993). Differential Item Functioning. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Hoskens, M. & De Boeck, P. (1995). Componential IRT models for polytomous items. Journal of Educational Measurement, 32, 364–384.

    Article  Google Scholar 

  • Hoskens, M. & De Boeck, P. (1997). A parametric model for local dependence among test items. Psychological Methods, 2, 261–277.

    Article  Google Scholar 

  • Hunt, L.A. & Jorgensen, M.A. (1999). Mixture model clustering: A brief introduction to the MULTIMIX program. Australian and New Zealand Journal of Statistics, 41, 153–171.

    MATH  Google Scholar 

  • Ip, E.H., Wang, J.W., De Boeck, P. & Meulders, M. (2003). Locally dependent latent trait models for polytomous responses. Psychometrika. Manuscript accepted for publication.

    Google Scholar 

  • Jannarone, R.J. (1986). Conjunctive item response theory kernels. Psychometrika, 51, 357–373.

    Article  MATH  Google Scholar 

  • Kelderman, H. (1984). Loglinear Rasch model tests. Psychometrika, 49, 223–245.

    Article  MATH  Google Scholar 

  • Kelderman, H. (1989). Item bias detection using loglinear IRT. Psychometrika, 54, 681–697.

    Article  MathSciNet  Google Scholar 

  • Klauer, K.C. & Sydow, H. (2001). Dynamic IRT models. In A. Boomsma, M.A.J. van Duijn & T.A.B. Snijders (Eds), Essays on Item Response Theory (pp. 69–87). New York: Springer.

    Chapter  Google Scholar 

  • Li, H. & Stout, W. (1996). A new procedure for detection of crossing DIF. Psychometrika, 61, 647–677.

    Article  MATH  Google Scholar 

  • Lord, F.M. (1980). Applications of Item Response Theory to Practical Testing Problems. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Mellenbergh, G.J. (1982). Contingency table models for assessing item bias. Journal of Educational Statistics, 7, 105–118.

    Article  Google Scholar 

  • Millsap, R.E. & Everson, H.T. (1993). Methodology review: Statistical approaches for assessing measurement bias. Applied Psychological Measurement, 17, 297–334.

    Article  Google Scholar 

  • Moore, S.M. (1996). Estimating differential item functioning in the polytomous case with the RCML model. In G. Engelhard & M. Wilson (Eds), Objective Measurement: Theory into Practice (Vol.3) (pp. 219–240). Norwoord, NJ: Ablex.

    Google Scholar 

  • Muraki, E. (1999). Stepwise analysis of differential item functioning based on multiple-group partial credit model. Journal of Educational Measurement, 36, 217–232.

    Article  Google Scholar 

  • Raju, N.S. (1988). The area between two item characteristic curves. Psychometrika, 53, 495–502.

    Article  MathSciNet  MATH  Google Scholar 

  • Rosenbaum, P.R. (1988). Item bundles. Psychometrika, 53, 349–359.

    Article  MathSciNet  MATH  Google Scholar 

  • Scheuneman, J.D. (1979). A method of assessing bias in test items. Journal of Educational Measurement, 16, 143–152.

    Article  Google Scholar 

  • Shealy, R. & Stout, W. (1993a). An item response theory model for test bias and differential item functioning. In P.W. Holland & W. Howard (Eds), Differential Item Functioning (pp. 197–239). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Shealy, R. & Stout, W. (1993b). A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF. Psychometrika, 58, 159–194.

    Article  MATH  Google Scholar 

  • Swaminathan, H. & Rogers, H.J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27, 361–370.

    Article  Google Scholar 

  • Thissen, D., Steinberg, L. & Wainer, H. (1988). Use of item-response theory in the study of group differences in trace lines. In H. Wainer & H.I. Braun (Eds), Test Validity (pp. 147–169). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Verguts, T. & De Boeck, P. (2000). A Rasch model for learning while solving an intelligence test. Applied Psychological Measurement, 24, 151–162.

    Article  Google Scholar 

  • Verhelst, N.D. & Glas, C.A.W. (1993). A dynamic generalization of the Rasch model. Psychometrika, 58, 395–415.

    Article  MATH  Google Scholar 

  • Verhelst, N.D. & Glas, C.A.W. (1995). Dynamic generalizations of the Rasch model. In G.H. Fischer & I.W. Molenaar (Eds), Rasch Models: Foundations, Recent Developments, and Applications (pp. 181–201). New York: Springer.

    Google Scholar 

  • Wainer, H., Sireci, S.G., & Thissen, D. (1991). Differential test let functioning: Definitions and detection. Journal of Educational Measurement, 28, 197–219.

    Article  Google Scholar 

  • Wainer, H. & Kiely, G. (1987). Item clusters and computerized adaptive testing: A case for testlets. Journal of Educational Measurement, 24, 185–201.

    Article  Google Scholar 

  • Wilson, M. & Adams, R.A. (1995). Rasch models for item bundles. Psychometrika, 60, 181–198.

    Article  MATH  Google Scholar 

  • Zimowski, F., Muraki, E., Mislevy, R.J. & Bock, R.D. (1994). BIMAIN 2: Multiple group IRT analysis and test maintenance for binary items. Chicago: Scientific Software International.

    Google Scholar 

  • Zwinderman, A.H. (1997). Response models with manifest predictors. In W.J. van der Linden & R.K. Hambleton (Eds), Handbook of Modern Item Response Theory (pp. 245–256). New York: Springer.

    Chapter  Google Scholar 

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Authors
  1. Michel Meulders
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  2. Yiyu Xie
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Editor information

Editors and Affiliations

  1. Department of Psychology, K.U. Leuven, Tiensestraat 102, B-3000, Leuven, Belgium

    Paul De Boeck

  2. Graduate School of Education, University of California, Berkeley, 94720-1670, Berkeley, CA, USA

    Mark Wilson

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© 2004 Springer Science+Business Media New York

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Meulders, M., Xie, Y. (2004). Person-by-item predictors. In: De Boeck, P., Wilson, M. (eds) Explanatory Item Response Models. Statistics for Social Science and Public Policy. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3990-9_7

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  • DOI: https://doi.org/10.1007/978-1-4757-3990-9_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-2323-3

  • Online ISBN: 978-1-4757-3990-9

  • eBook Packages: Springer Book Archive

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