Skip to main content
Log in

On the relationship between item response theory and factor analysis of discretized variables

  • Published:
Psychometrika Aims and scope Submit manuscript

Abstract

Equivalence of marginal likelihood of the two-parameter normal ogive model in item response theory (IRT) and factor analysis of dichotomized variables (FA) was formally proved. The basic result on the dichotomous variables was extended to multicategory cases, both ordered and unordered categorical data. Pair comparison data arising from multiple-judgment sampling were discussed as a special case of the unordered categorical data. A taxonomy of data for the IRT and FA models was also attempted.

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

  • Akaike, H. (1980). Likelihood and the Bayes procedure. In J. M. Bernardo et al. (Eds.),Bayesian statistics. Valencia University Press.

  • Andersen, E. B. (1980).Discrete statistical models with social science applications. Amsterdam: North-Holland.

    Google Scholar 

  • Basu, D. (1977). On elimination of nuisance parameters.Journal of the American Statistical Association, 22, 229–243.

    Google Scholar 

  • Bartholomew, D. J. (1983). Latent variable models for ordered categorical data.Journal of Econometrics.22, 229–243.

    Google Scholar 

  • Bartholomew, D. J. (1985, July). A unified view of factor analysis, latent structure analysis and scaling. Invited paper given at the 4-th European Meeting of the Psychometric Society and Classification Societies, Cambridge.

  • Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories.Psychometrika, 37, 29–51.

    Google Scholar 

  • Bock, R. D. (1975).Multivariate statistical methods in behavioral research. New York: McGraw Hill.

    Google Scholar 

  • Bock, R. D. (1984, June).Full information item factor analysis. Paper presented at the Joint Meeting of the Classification Society and the Psychometric Society, Sant Barbara.

  • Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm.Psychometrika, 46, 443–459.

    Google Scholar 

  • Bock, R. D., & Jones, L. V. (1968).The measurement and prediction of judgment and choice. San Francisco: Holden Day.

    Google Scholar 

  • Bock, R. D., & Lieberman, M. (1970). Fitting a response model forn dichotomously scored items.Psychometrika, 35, 283–319.

    Google Scholar 

  • Carroll, J. D., & Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling via anN-way generalization of “Eckart-Young” decomposition.Psychometrika, 35, 283–319.

    Google Scholar 

  • Christoffersson, A. (1975). Factor analysis of dichotomized variables.Psychometrika, 40, 5–32.

    Google Scholar 

  • Coombs, C. H. (1964).A theory of data. New York: Wiley.

    Google Scholar 

  • Cox, D. R. (1966). Some procedure connected with the logistic qualitative response curve. In F. N. David (Ed.),Research papers in statistics: Festschrift for J. Neyman (pp. 55–71). New York: Wiley.

    Google Scholar 

  • de Leeuw, J. (1983). Models and methods for the analysis of correlation coefficients.Journal of Econometrics, 22, 113–137.

    Google Scholar 

  • De Soete, G., & Carroll, J. D. (1983). A maximum likelihood method for fitting the wandering vector model.Psychometrika, 48, 553–566.

    Google Scholar 

  • De Soete, G., Carroll, J. D., & DeSarbo, W. S. (in press). The wandering ideal point model: A probabilistic multidimensional unfolding model for paired comparisons data.Journal of Mathematical Psychology.

  • Gifi, A. (1981).Non-linear multivariate analysis. Unpublished manuscript, University of Leiden, Department of Data Theory.

  • Greenacre, M. (1984).Theory and application of correspondence analysis. London: Academic Press.

    Google Scholar 

  • Heiser, W. J. (1981).Unfolding analysis of proximity data. Unpublished doctoral Dissertation, University of Leiden.

  • Heiser, W., & de Leeuw, J. (1981). Multidimensional mapping of preference data.Mathematique et Sciences Humaines, 19, 39–96.

    Google Scholar 

  • Johnson, N. L., & Kotz, S. (1974).Distribution in Statistics: Multivariate distributions. New York: Wiley.

    Google Scholar 

  • Jöreskog, K. G. (1970). A general method for analysis of covariance structures.Biometrika, 57, 239–251.

    Google Scholar 

  • Lazarsfeld, P. F., & Henry, N. (1968).Latent structure analysis. New York: Houghton Mifflin.

    Google Scholar 

  • Lord, F. M., & Novick, M. R. (1968).Statistical theories of mental test scores. Reading, MA: Addison Wesley.

    Google Scholar 

  • Luce, R. D. (1959).Individual choice behavior: A theoretical analysis. New York: Wiley.

    Google Scholar 

  • Mislevy, R. J. (in press). Recent developments in factor analysis of categorical variables.Journal of Educational Statistics.

  • Muthén, B. (1978). Contributions to factor analysis of dichotomous variables.Psychometrika, 43, 551–560.

    Google Scholar 

  • Muthén, B. (1979). A structural probit model with latent variables.Journal of the American Statistical Association, 24, 807–811.

    Google Scholar 

  • Muthén, B. (1983). Latent variable structural equation modeling with categorical data.Journal of Econometrics, 22, 43–65.

    Google Scholar 

  • Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators.Psychometrika, 49, 115–132.

    Google Scholar 

  • Muthén, B., & Christoffersson, A. (1981). Simultaneous factor analysis of dichotomous variables in several groups.Psychometrika, 46, 407–419.

    Google Scholar 

  • Ramsay, J. O. (1982). Some statistical approaches to multidimensional scaling.Journal of the Royal Statistical Society, Series A, 145, 285–312.

    Google Scholar 

  • Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores.Psychometrika Monograph No. 17, 34(4, Pt. 2).

  • Schönemann, P. H., & Wang, M. M. (1972). An individual difference model for the multidimensional analysis of preference data.Psychometrika, 37, 275–309.

    Google Scholar 

  • Sjoberg, L. (1967). Successive categories scaling of paired comparisons.Psychometrika, 32, 297–308.

    Google Scholar 

  • Takane, Y. (1980a). Analysis of categorizing behavior by a quantification method.Behaviormetrika, 8, 75–86.

    Google Scholar 

  • Takane, Y. (1980b). Maximum likelihood estimation in the generalized case of Thurstone's model of comparative judgment.Japanese Psychological Rsearch, 22, 188–196.

    Google Scholar 

  • Takane, Y. (1983a, July). Choice model analysis of the “pick any/n” type of binary data. Handout for the talk given at the European Psychometric and Classification Meeting, Jouy-en-Josas.

  • Takane, Y. (1983b, June). An item response model for multidimensional analysis of multiple-choice data. Handbook for the talk given at the Annual Meeting of the Psychometric Society, Los Angeles.

  • Takane, Y. (1985, June). Probabilistic multidimensional pair comparison models that take into account systematic individual differences. Transcript of the talk given at the 50-th Anniversary Meeting of the Psychometric Society, Nashville, TN.

  • Takane, Y., & Carroll, J. D. (1981). Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities.Psychometrika, 46, 389–405.

    Google Scholar 

  • Thissen, D. (1982). Marginal maximum likelihood estimation for the one-parameter logistic model.Psychometrika, 47, 175–186.

    Google Scholar 

  • Thurstone, L. L. (1927). A law of comparative judgments.Psychological Review, 34, 273–286.

    Google Scholar 

  • Thurstone, L. L. (1945). The prediction of choice.Psychometrika, 10, 237–253.

    Google Scholar 

  • Weinberg, S. L. Carroll, J. D., & Cohen, H. S. (1984). Confidence regions for INDSCAL using the jackknife and bootstrap techniques.Psychomertrika, 49, 475–491.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

The work reported in this paper has been supported by Grant A6394 to the first author from the Natural Sciences and Engineering Research Council of Canada.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Takane, Y., de Leeuw, J. On the relationship between item response theory and factor analysis of discretized variables. Psychometrika 52, 393–408 (1987). https://doi.org/10.1007/BF02294363

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02294363

Key words

Navigation