Skip to main content
Log in

Stochastic Ordering Of the Latent Trait by the Sum Score Under Various Polytomous IRT Models

  • Published:
Psychometrika Aims and scope Submit manuscript

Abstract

The sum score is often used to order respondents on the latent trait measured by the test. Therefore, it is desirable that under the chosen model the sum score stochastically orders the latent trait. It is known that unlike dichotomous item response theory (IRT) models, most polytomous IRT models do not imply stochastic ordering. It is unknown, however, (1) whether stochastic ordering is often or rarely violated and (2) whether violations yield a serious problem for practical data analysis. These are the central issues of this paper. First, some unanswered questions that pertain to polytomous IRT models implying stochastic ordering were investigated. Second, simulation studies were conducted to evaluate stochastic ordering in practical situations. It was found that for most polytomous IRT models that do not imply stochastic ordering, the sum score can be used safely to order respondents on the latent trait.

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

  • Agresti A. (1990) Categorical Data Analysis.Wiley, New York

    Google Scholar 

  • Andrich D. (1978) A rating scale formulation for ordered response categories. Psychometrika 43:561–573

    Google Scholar 

  • Birnbaum A. (1968) Some latent trait models and their use in inferring an examinee’s ability. In: Lord F.M., Novick M.R. (eds) Statistical theories of mental test scores. Addison-Wesley, Reading, MA, pp 397–479

    Google Scholar 

  • Douglas R., Fienberg S.E., Lee M.T., Sampson A.R., Whitaker L.R. (1991) Positive dependence concepts for ordinal contingency tables. In Block H.W., Sampson A.R., Savits T.H. (eds) Topics in Statistical Dependence. IMS, Hayward, CA, pp 189–202

    Google Scholar 

  • Grayson D.A. (1988) Two group classification in latent trait theory: scores with monotone likelihood ratio. Psychometrika 53:383–392

    Google Scholar 

  • Hemker B.T., Sijtsma K., Molenaar I.W., Junker B.W. (1996) Polytomous IRT models and monotone likelihood ratio of the total score. Psychometrika 61:679–693

    Google Scholar 

  • Hemker B.T., Sijtsma K., Molenaar I.W., Junker B.W. (1997) Stochastic ordering using the latent and the sum score in polytomous IRT models. Psychometrika 62:331–347

    Google Scholar 

  • Hemker B.T., Van der Ark L.A., Sijtsma K. (2001) On measurement properties of continuation ratio models. Psychometrika 66:487-506

    Google Scholar 

  • Huynh H. (1994a) A new proof for monotone likelihood ratio for the sum of independent variables. Psychometrika 59:77–79

    MathSciNet  Google Scholar 

  • Huynh H. (1994b) On equivalence between a partial credit item and a set of independent Rasch binary items. Psychometrika 59:111–119

    MathSciNet  Google Scholar 

  • Huynh H. (1996) Decomposition of a Rasch partial credit item into independent and indecomposable trinary items. Psychometrika 61:31–39

    MathSciNet  Google Scholar 

  • Junker B.W. (1991) Essential independence and likelihood-based ability estimation for polytomous items. Psychometrika 56:255–278

    Google Scholar 

  • Junker B.W. (1998) Some remarks on Scheiblechner’s treatment of ISOP models. Psychometrika 63:73–82

    Google Scholar 

  • Lehmann E.L., Rojo R. (1992) Invariant directional orderings. The Annals of Statistics 20:2100–2110

    Google Scholar 

  • Masters G.N. (1982) A Rasch model for partial credit scoring. Psychometrika 47:149–174

    Google Scholar 

  • Mellenbergh G.J. (1995) Conceptual notes on models for discrete polytomous item responses. Applied Psychological Measurement 19:91–100

    Google Scholar 

  • Mokken R.J. (1971) A Theory and Procedure of Scale Analysis. De Gruyter, New York/Berlin

    Google Scholar 

  • Molenaar I.W. (1983) Item Steps (Heymans Bulletin HB-83-630-EX). Groningen, The Netherlands: University of Groningen.

  • Molenaar I.W. (1997) Nonparametric models for polytomous responses. In: van der Linden W.J., Hambleton R.K. (eds) Handbook of Modern Item Response Theory. Springer, New York, pp 369–380

    Google Scholar 

  • Molenaar I.W., Sijtsma K. (2000) MSP for Windows [Software manual]. iec ProGAMMA, Groningen, The Netherlands

    Google Scholar 

  • Muraki E. (1990) Fitting a polytomous item response model to Likert-type data. Applied Psychological Measurement 14:59–71

    Google Scholar 

  • Muraki E. (1992) A generalized partial credit model: Application of an EM-algorithm. Applied Psychological Measurement 16:159–176

    Google Scholar 

  • Rasch G. (1960) Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen

    Google Scholar 

  • Ross S.M. (1996) Stochastic processes, (2nd edn.). Wiley, New York

    Google Scholar 

  • Samejima F. (1969) Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph 17

  • Samejima F. (1995) Acceleration model in the heterogeneous case of the general graded response model. Psychometrika 60:549–572

    Google Scholar 

  • Samejima F. (2001) Substantiveness in psychometrics. Paper presented at the First International Meeting of the Psychometric Society, Osaka, Japan

  • Scheiblechner H. (1995) Isotonic ordinal probabilistic models (ISOP). Psychometrika 60:281–304

    Google Scholar 

  • Sijtsma K., Hemker B.T. (1998) Nonparametric polytomous IRT models for invariant item ordering, with results for parametric models. Psychometrika 63:183–200

    Google Scholar 

  • Sijtsma K., Hemker B.T. (2000) A taxonomy of IRT models for ordering persons and items using simple sum scores. Journal of Educational and Behavioral Statistics 25:391–415

    Google Scholar 

  • Sijtsma K., Van der Ark L.A. (2001) Progress in NIRT analysis of polytomous item scores: Dilemmas and practical solutions. In: Boomsma A., van Duijn M.A.J., Snijders T.A.B. (eds) Essays on Item Response Theory. Springer, New York, pp 297–318

    Google Scholar 

  • Tutz G. (1990) Sequential item response models with an ordered response. British Journal of Mathematical and Statistical Psychology 43:39–55

    MathSciNet  Google Scholar 

  • Van der Ark L.A. (2001) Relationships and properties of polytomous item response theory models. Applied Psychological Measurement 25:273–282

    Article  Google Scholar 

  • Van der Ark L.A., Hemker B.T., Sijtsma K. (2002) Hierarchically related nonparametric IRT models, and practical data analysis. In: Marcoulides G.A., Moustaki I. (eds) Latent Variable and Latent Structure Models. Erlbaum, Mahwah, NJ, pp 41–62

    Google Scholar 

  • Van Engelenburg G (1997) On Psychometric Models for Polytomous Items with Ordered Categories within the Framework of Item Response Theory, Unpublished doctoral dissertation, University of Amsterdam, Amsterdam.

  • Verhelst N.D. (1992) Het Eenparameter Logistisch Model [The one parameter logistic model (OPLM)] (OPD Memorandum 92-3). Arnhem, The Netherlands: CITO National Institute for Educational Measurement.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. Andries van der Ark.

Additional information

The author would like to thank Klaas Sijtsma for commenting on earlier drafts of this paper.

Rights and permissions

Reprints and permissions

About this article

Cite this article

van der Ark, L.A. Stochastic Ordering Of the Latent Trait by the Sum Score Under Various Polytomous IRT Models. Psychometrika 70, 283–304 (2005). https://doi.org/10.1007/s11336-000-0862-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11336-000-0862-3

Keywords

Navigation