Skip to main content

Unidimensional Item Response Theory Models

  • Chapter
  • First Online:
Multidimensional Item Response Theory

Part of the book series: Statistics for Social and Behavioral Sciences ((SSBS))

  • 5528 Accesses

Abstract

In Chap. 3, the point will be made that multidimensional item response theory (MIRT) is an outgrowth of both factor analysis and unidimensional item response theory (UIRT). Although this is clearly true, the way that MIRT analysis results are interpreted is much more akin to UIRT. This chapter provides a brief introduction to UIRT with a special emphasis on the components that will be generalized when MIRT models are presented in Chap. 4. This chapter is not a thorough description of UIRT models and their applications. Other texts such as Lord (1980), Hambleton and Swaminathan (1985), Hulin et al. (1983), Fischer and Molenaar (1995), and van der Linden and Hambleton (1997) should be consulted for a more thorough development of UIRT models.

There are two purposes for describing UIRT models in this chapter. The first is to present basic concepts about the modeling of the interaction between persons and test items using simple models that allow a simpler explication of the concepts. The second purpose is to identify shortcomings of the UIRT models that motivated the development of more complex models. As with all scientific models of observed phenomena, the models are only useful to the extent that they provide reasonable approximations to real world relationships. Furthermore, the use of more complex models is only justified when they provide increased accuracy or new insights. One of the purposes of this book is to show that the use of the more complex MIRT models is justified because they meet these criteria.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Nonmonotonic IRT models have been proposed (e.g., Thissen and Steinberg 1984, Sympson 1983), but these have not yet been generalized to the multidimensional case so they are not considered here.

  2. 2.

    The symbols used for the presentation of the models follow Lord (1980) with item parameters represented by Roman letters. Other authors have used the statistical convention of representing parameters using Greek letters.

  3. 3.

    Chapter 6 presents a number of estimation procedures including maximum likelihood. A full discussion of estimation procedures is beyond the scope of this book. The reader should refer to a comprehensive mathematical statistics text for a detailed discussion of maximum-likelihood estimation and other techniques for estimating model parameters.

References

  • Abrahamowicz M, Ramsay JO (1992) Multicategorical spline model for item response theory. Psychometrika 57:5–27

    Article  Google Scholar 

  • Baker FB, Kim S-H (2004) Item response theory: Parameter estimation techniques (2nd edition, revised and expanded). Marcel Dekker, New York

    Google Scholar 

  • Birnbaum A (1968) Some latent trait models and their use in infering an examinee’s ability. In FM Lord MR Novick (eds.) Statistical theories of mental test scores. Addison-Wesley, Reading, MA

    Google Scholar 

  • Chang H-H, Ying Z (1996) A global information approach to computerized adaptive testing. Applied Psychological Measurement 20:213–230

    Article  Google Scholar 

  • Drasgow F, Parsons CK (1983) Applications of unidimensional item response theory to multidimensional data. Applied Psychological Measurement 7:189–199

    Article  Google Scholar 

  • Fischer GH (1995a) Derivations of the Rasch model. In Fischer GH and Molenaar IW (eds) Rasch models: Foundations, recent developments and applications. Springer-Verlag, New York

    Google Scholar 

  • Fischer GH, Molenaar IW (eds) (1995) Rasch models: Foundations, recent developments, and applications. Springer-Verlag, New York

    MATH  Google Scholar 

  • Fisher RA (1925) Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society 22:700–725

    Article  MATH  Google Scholar 

  • Hulin CL, Drasgow F, Parsons CK (1983) Item response theory: application to psychological measurement. Dow Jones-Irwin, Homewood, IL

    Google Scholar 

  • Kendall MG, Stuart A (1961) The advanced theory of statistics. Hafner, New York

    Google Scholar 

  • Lord FM (1980) Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates, Hillsdale, NJ

    Google Scholar 

  • Lord FM, Novick MR (1968) Statistical theories of mental test scores. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Miller MD, Linn RL (1988) Invariance of item characteristic functions with variations in instructional coverage. Journal of Educational Measurement 25:205–219

    Article  Google Scholar 

  • Rasch G (1960) Probabilistic models for some intelligence and attainment tests. Danmarks Paedagogiske Institut, Copenhagen

    Google Scholar 

  • Rosenbaum PR (1984) Testing the conditional independence and monotonicity assumptions of item response theory. Psychometrika 49:425–435

    Article  MATH  MathSciNet  Google Scholar 

  • Samejima F, Livingston P (1979) Method of moments as the least squares solution for fitting a polynomial (Research Report 79-2). Univerrsity of Tennessee, Knoxville, TN

    Google Scholar 

  • Savalei V (2006) Logistic approximation to the normal: the KL rationale. Psychometrika 71: 763–767

    Article  MathSciNet  Google Scholar 

  • Stevens SS (1946) On the theory of scales of measurement. Science 103:677–680

    Article  Google Scholar 

  • Stevens SS (1951) Mathematics, measurement, and psychophysics. In SS Stevens (ed) Handbook of experimental psychology (pp. 1–49). Wiley, New York

    Google Scholar 

  • Stout W (1987) A nonparametric approach for assessing latent trait dimensionality. Psychometrika 52:589–617

    Article  MATH  MathSciNet  Google Scholar 

  • Stout W, Douglas B, Junker B, Roussos L (1999) DIMTEST [Computer software]. The William Stout Institute for Measurement, Champaign, IL

    Google Scholar 

  • Tate R (2003) A comparison of selected empirical methods for assessing the structure of responses to test items. Applied Psychological Measurement 27:159–203

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • van der Linden WJ, Hambleton RK (eds.) (1997) Handbook of modern item response theory. Springer, New York

    MATH  Google Scholar 

  • Verhelst ND, Verstralen HHFM (1997) Modeling sums of binary responses by the partial credit model (Measurement and Research Department Reports 97-7). Cito, Arnhem, The Netherlands

    Google Scholar 

  • Yen WM (1984) Effects of local dependence on the fit and equating performance of the three-parameter logistic model. Applied Psychological Measurement 8:125–145

    Article  Google Scholar 

  • Andrich D (2004) Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care 42:I-7–I-16

    Article  Google Scholar 

  • Sympson JB (1978) A model for testing with multidimensional items. In Weiss DJ (ed) Proceedings of the 1977 Computerized Adaptive Testing Conference, University of Minnesota, Minneapolis

    Google Scholar 

  • McDonald RP (1967) Nonlinear factor analysis. Psychometric Monograph 15

    Google Scholar 

  • Samejima F (1969) Estimation of latent ability using a response pattern of graded scores. Psychometric Monograph Supplement, 34 (Monograph No. 17)

    Google Scholar 

  • Lord FM (1980) Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates, Hillsdale, NJ

    Google Scholar 

  • Masters GN, Wright BD (1997) The partial credit model. In WJ van der Linden RK Hambleton (eds.) Handbook of modern item response theory. Springer, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mark D. Reckase .

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Reckase, M.D. (2009). Unidimensional Item Response Theory Models. In: Multidimensional Item Response Theory. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-0-387-89976-3_2

Download citation

Publish with us

Policies and ethics