Skip to main content
Log in

Bayesian Irt Models Incorporating General and Specific Abilities

  • Published:
Behaviormetrika Aims and scope Submit manuscript

Abstract

IRT-based models with a general ability and several specific ability dimensions are useful. Studies have looked at item response models where the general and specific ability dimensions form a hierarchical structure. It is also possible that the general and specific abilities directly underlie all test items. A multidimensional IRT model with such an additive structure is proposed under the Bayesian framework. Simulation studies were conducted to evaluate parameter recovery as well as model comparisons. A real data example is also provided. The results suggest that the proposed additive model offers a better way to represent the test situations not realized in existing models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ackerman, T.A. (1993). Insuring the validity of the reported score scale by reporting multiple scores. Paper presented at the North American Meeting of the Psychometric Society, Berkeley, CA.

    Google Scholar 

  • Aitkin, M. (1991). Posterior Bayes factors (with discussion). Journal of the Royal Statistical Society, Series B, 53, 111–142.

    MATH  Google Scholar 

  • Albert, J.H. (1992). Bayesian estimation of normal ogive item response curves using Gibbs sampling. Journal of Educational Statistics, 17, 251–269.

    Article  Google Scholar 

  • Albert, J.H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669–679.

    Article  MathSciNet  Google Scholar 

  • Béguin, A.A., & Glas, C.A.W. (2001). MCMC estimation and some model-fit analysis of multidimensional IRT models. Psychometrika, 66, 541–562.

    Article  MathSciNet  Google Scholar 

  • Bradlow, E.T., Wainer, H., & Wang, X. (1999). A Bayesian random effects model for testlets. Psychometrika, 64, 153–168.

    Article  Google Scholar 

  • Box, G.E.P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799.

    Article  MathSciNet  Google Scholar 

  • Carlin, B.P., & Chib, S. (1993). Bayesian model choice via Markov chain Monte Carlo. Research Report 93–006. University of Minnesota, Division of Biostatistics.

    Google Scholar 

  • Carlin, B.P., & Louis, T.A. (2000). Bayes and empirical Bayes methods for data analysis. London: Chapman & Hall.

    Book  Google Scholar 

  • Chib, S. (1995). Marginal likelihood from the Gibbs output. Journal of the American Statistical Association, 90, 1313–1321.

    Article  MathSciNet  Google Scholar 

  • Chib, S., & Jeliazkov, I. (2001). Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association, 96, 1197–1208.

    Article  MathSciNet  Google Scholar 

  • Gelman, A., Carlin, J.B., Stern, H.S., & Rubin, D.B. (2004). Bayesian data analysis. Boca Raton: Chapman & Hall/CRC.

    MATH  Google Scholar 

  • Kass, R.E., & Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.

    Article  MathSciNet  Google Scholar 

  • Lee, H. (1995). Markov chain Monte Carlo methods for estimating multidimensional ability in item response analysis. Ph.D. Dissertation, University of Missouri, Columbia, MO.

    Google Scholar 

  • Meng, X.L., & Wong, W.H. (1996). Simulating ratios of normaliing constants via a simple identity: a theoretical exploration. Statistica Sinica, 6, 831–860.

    MathSciNet  MATH  Google Scholar 

  • Meng, X.L., & Schilling, S. (2002). Warp bridge sampling. Journal of Computational and Graphical Statistics, 11, 552–586.

    Article  MathSciNet  Google Scholar 

  • Newton, M.A., & Raftery, A.E. (1994). Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion). Journal of the Royal Statistical Society, Series B, 56, 3–48.

    MathSciNet  MATH  Google Scholar 

  • Osterlind, S. (1997). A national review of scholastic achievement in general education: How are we doing and why should we care? ASHE-ERIC Higher Education Report 25, No. 8. Washington, DC: The George Washington University, Graduate School of Education and Human Development.

    Google Scholar 

  • Reckase, M.D. (1997). The past and future of multidimensional item response theory. Applied Psychological Measurement, 21, 25–36.

    Article  Google Scholar 

  • Ripley, B.D. (1987). Stochastic Simulation. New York: Wiley.

    Book  Google Scholar 

  • Robert, C.P. (2001). The Bayesian Choice (2nd ed). New York: Springer.

    Google Scholar 

  • Schmid, J., & Leiman, J.M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53–61.

    Article  Google Scholar 

  • Segall, D.O. (2001). General ability measurement: An application of multidimensional item response theory. Psychometrika, 66, 79–97.

    Article  MathSciNet  Google Scholar 

  • Sheng, Y., & Wikle, C.K. (2007). Comparing multiunidimensional and unidimensional item response theory models. Educational & Psychological Measurement, 67, 899–919.

    Article  MathSciNet  Google Scholar 

  • Sheng, Y., & Wikle, C.K. (2008). Bayesian multidimensional IRT models with a hierarchical structure. Educational & Psychological Measurement, 68, 413–430.

    Article  MathSciNet  Google Scholar 

  • Sinharay, S., Johnson, M.S., and Stern, H.S. (2006). Posterior predictive assessment of item response theory models. Applied Psychological Measurement, 30, 298–321.

    Article  MathSciNet  Google Scholar 

  • Sinharay, S., & Stern, H.S. (2003). Posterior predictive model checking in hierarchical models. Journal of Statistical Planning and Inference, 111, 209–221.

    Article  MathSciNet  Google Scholar 

  • Spearman, C. (1904). General intelligence, objectively determined and measured. American journal of Psychology, 15, 201–293.

    Article  Google Scholar 

  • Spiegelhalter, D.J., Best, N., Carlin, B., & van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B, 64, 583–640.

    Article  MathSciNet  Google Scholar 

  • Tanner, M.A., and W.H. Wong (1987). The calculation of posterior distribution by data augmentation (with discussion). Journal of the American Statistical Association, 82, 528–550.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanyan Sheng.

About this article

Cite this article

Sheng, Y., Wikle, C.K. Bayesian Irt Models Incorporating General and Specific Abilities. Behaviormetrika 36, 27–48 (2009). https://doi.org/10.2333/bhmk.36.27

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2333/bhmk.36.27

Key Words and Phrases

Navigation