Advertisement

A framework for item response models

  • Paul De Boeck
  • Mark Wilson
Part of the Statistics for Social Science and Public Policy book series (SSBS)

Abstract

This volume has been written with the view that there are several larger perspectives that can be used (a) to throw light on the sometimes confusing array of models and data that one can find in the area of item response modeling, (b) to explore different contexts of data analysis than the ‘test data’ context to which item response models are traditionally applied, and (c) to place these models in a larger statistical framework that will enable the reader to use a generalized statistical approach and also to take advantage of the flexibility of statistical computing packages that are now available.

Keywords

Linear Mixed Model Item Response Item Response Theory Generalize Linear Mixed Model Verbal Aggression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agresti, A., Booth, J., Hobert, J.P., & Caffo, B. (2000). Random-effects modeling of categorical data. Sociological Methodology, 30, 27–80.CrossRefGoogle Scholar
  2. Baker, F.B. (1992) Item Response Theory: Parameter Estimation Techniques. New York: Marcel Dekker.zbMATHGoogle Scholar
  3. 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 (pp. 395–479). Reading, MA: Addison-Wesley.Google Scholar
  4. Bock, R.D. (1997). A brief history of item response theory. Educational Measurement: Issues and Practice, 16, 21–33.CrossRefGoogle Scholar
  5. Bond, T., & Fox, C. (2001). Applying the Rasch Model: Fundamental Measurement in Human Sciences. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  6. Boomsma, A., van Dijn, M.A.J., & Snijders, T.A.B. (Eds) (2001). Essays and Item Response Theory. New York: Springer.Google Scholar
  7. Breslow, N.E., & Clayton, D.G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 9–25.zbMATHGoogle Scholar
  8. Camilli, G. (1994). Origin of the scaling constant d = 1.7 in item response theory. Journal of Educational and Behavioral Statistics, 19, 293–295.Google Scholar
  9. Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/correlation Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  10. Cronbach, L.J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 672–684.CrossRefGoogle Scholar
  11. Davidian, M., & Giltinan, D.M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall.Google Scholar
  12. Davis, C.S. (2002). Statistical Methods for the Analysis of Repeated Measurements. New York: Springer.zbMATHGoogle Scholar
  13. Embretson, S.E. (1983). Construct validity: Construct representation versus nomothetic span. Psychological Bulletin, 93, 179–197.CrossRefGoogle Scholar
  14. Embretson, S.E. (Ed.) (1985). Test Design: Developments in Psychology and Psychometrics. New York: Academic Press.Google Scholar
  15. Embretson, S.E., & Reise, S. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  16. Fahrmeir, L., & Tutz, G. (2001). Multivariate Statistical Modeling Based on Generalized Linear Models (2nd ed.). New York: Springer.CrossRefGoogle Scholar
  17. Fischer, G.H., & Molenaar, I. (Eds) (1995). Rasch Models Foundations, Recent Developments and Applications. New York: Springer.zbMATHGoogle Scholar
  18. Goldstein, H. (2003). Multilevel Statistical Models (3rd ed.). London: Arnold.zbMATHGoogle Scholar
  19. Hambleton, R.K., Swaminathan, H., & Rogers, H.J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage.Google Scholar
  20. Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational and Behavioral Statistics, 38, 79–93.Google Scholar
  21. Kirk, R.E. (1995). Experimental Design. Procedures for the Behavioral Sciences (3rd ed.). Pacific Grove, CA: Brooks/Cole.zbMATHGoogle Scholar
  22. Kreft, I., & de Leeuw, J. (1998). Introducing Multilevel Modeling. London: Sage.Google Scholar
  23. Longford, N.T. (1993). Random Coefficient Models. London: Oxford University Press.zbMATHGoogle Scholar
  24. Lord, F.M., & Novick, M. (1968). Statistical Theories of Mental Test Scores. Reading, MA: Addison Wesley.zbMATHGoogle Scholar
  25. McCullagh, P., & Neider, J.A. (1989). Generalized Linear Models (2nd ed.). London: Chapman & Hall.zbMATHGoogle Scholar
  26. McCulloch, C.E., & Searle, S.R. (2001). Generalized, Linear, and Mixed Models. New York: Wiley.zbMATHGoogle Scholar
  27. McDonald, R.P. (1999). Test Theory. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  28. Mellenbergh, G. (1994). Generalized linear item response theory. Psychological Bulletin, 115, 300–307.CrossRefGoogle Scholar
  29. Moustaki, I., & Knott, M. (2000). Generalized latent trait models. Psy-chometrika, 65, 391–441.MathSciNetGoogle Scholar
  30. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage.Google Scholar
  31. Rijmen, F., Tuerlinkx, F., De Boeck, P., & Kuppens (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8, 185–205.CrossRefGoogle Scholar
  32. SAS Institute (1999). SAS Online Doc (Version 8) (software manual on CD-Rom). Cary, NC: SAS Institute Inc.Google Scholar
  33. Snijders, T., & Bosker, R. (1999). Multilevel Analysis. London: Sage.zbMATHGoogle Scholar
  34. Spiegelhalter, D., Thomas, A., Best, N. & Lunn, D. (2003). BUGS: Bayesian inference using Gibbs sampling. MRC Biostatistics Unit, Cambridge, England,www.mrc-bsu.cam.ac.uk/bugs/Google Scholar
  35. Spielberger, C.D. (1988). State-Trait Anger Expression Inventory Research Edition. Professional Manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
  36. Spielberger, C.D., & Sydeman, S.J. (1994). State-trait anxiety inventory and state-trait anger expression inventory. In M.E. Maruish (Ed.), The Use of Psychological Tests for Treatment Planning and Outcome Assessment (pp. 292–321). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  37. Sternberg, R.J. (1977). Component processes in analogical reasoning. Psychological Review, 84, 353–378.CrossRefGoogle Scholar
  38. Sternberg, R.J. (1980). Representation and process in linear syllogistic reasoning. Journal of Experimental Psychology: General, 109, 119–159.CrossRefGoogle Scholar
  39. Thissen, D., & Orlando, M. (2001). Item response theory for items scored in two categories. In D. Thissen & H. Wainer (Eds), Test Scoring (pp. 73–140). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  40. Thissen, D., & Wainer, H. (Eds) (2001). Test Scoring. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  41. van der Linden, W.J., & Hambleton, R.K. (Eds) (1997). Handbook of Modern Item Response Theory. New York: Springer.zbMATHGoogle Scholar
  42. Vansteelandt, K. (2000). Formal models for contextualized personality psychology. Unpublished doctoral dissertation, K.U.Leuven, Belgium.Google Scholar
  43. Verbeke, G., & Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.zbMATHGoogle Scholar
  44. Vonesh, E.F., & Chinchilli, V.M. (1997). Linear and Nonlinear Models for the Analysis of Repeated Measurements. New York: Dekker.zbMATHGoogle Scholar
  45. Wallenstein, S. (1982). Regression models for repeated measurements. Biometrics, 38, 849–853.CrossRefGoogle Scholar
  46. Wilson, M. (2005). Constructing Measures: An Item Response Modeling Approach. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  47. Wilson, M., & Adams, R.J. (1992). A multilevel perspective on the ‘two scientific disciplines of psychology’. Paper presented in a Symposium on the Two Scientific Disciplines of Psychology at the XXV International Congress of Psychology, Brussels.Google Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Paul De Boeck
  • Mark Wilson

There are no affiliations available

Personalised recommendations