Skip to main content
Log in

Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features

  • Published:
Psychometrika Aims and scope Submit manuscript

Abstract

Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple or single. The single transformations consist of three types: nominal, ordinal, and numerical. The corresponding OVERALS computer program minimizes a least squares loss function by using an alternating least squares algorithm. Many existing linear and nonlinear multivariate analysis techniques are shown to be special cases of OVERALS. An application to data from an epidemiological survey is presented.

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.

Institutional subscriptions

Similar content being viewed by others

References

  • Benzécri, J. P. et al. (1973).L'Analyse des données [Data analysis] (2 vols.). Paris: Dunod.

    Google Scholar 

  • Benzécri, J. P. et al. (1980).Pratique de l'Analyse des données [Practice of data analysis] (3 vols). Paris: Dunod.

    Google Scholar 

  • Carroll, J. D. (1968). Generalization of canonical correlation analysis to three or more sets of variables.Proceedings of the 76th Annual Convention of the American Psychological Association, 5, 227–228.

    Google Scholar 

  • Cliff, N. (1966). Orthogonal rotation to congruence.Psychometrika, 31, 33–42.

    Google Scholar 

  • de Leeuw, J. (1983, July).Nonlinear joint bivariate analysis. Paper presented at the meeting of the Psychometric Society, Jouy-en-Josas, France.

  • de Leeuw, J. (1984a). The Gifi-system of nonlinear multivariate analysis. In E. Diday, M. Jambu, L. Lebart, J. Pagès, & R. Thomassone (Eds.),Data Analysis and Informatics III (pp. 415–424). Amsterdam: North Holland.

    Google Scholar 

  • de Leeuw, J. (1984b).Canonical analysis of categorical data. (Doctoral dissertation, University of Leiden, 1973). Leiden: DSWO-Press.

    Google Scholar 

  • de Leeuw, J. & van der Burg, E. (1986). The Permutational limit distribution of generalized canonical correlations. In E. Diday, Y. Escoufier, L. Lebart, J. P. Pagès, Y. Schektman, & R. Thomassone (Eds.),Data analysis and informatics IV (pp. 509–521). Amsterdam: North Holland.

    Google Scholar 

  • de Leeuw, J., van Rijckevorsel, J., & van der Wouden, H. (1981). Nonlinear principal component analysis using B-splines.Methods of operations research, 23, 211–234.

    Google Scholar 

  • Gifi, A. (1981).Nonlinear multivariate analysis. Department of data theory, University of Leiden. (In Press, Leiden: DSWO-Press).

    Google Scholar 

  • Gifi, A. (1985).PRINCALS. (User's Guide UG-85-03). Department of Data Theory, University of Leiden.

  • Greenacre, M. J. (1984).Theory and applications of correspondence analysis. New York: Academic Press.

    Google Scholar 

  • Guttman, L. (1941). The quantification of a class of attributes: A theory and method of scale construction. In P. Horst (Ed.),The prediction of personal adjustment. New York: Social Science Research Council.

    Google Scholar 

  • Horst, P. (1961). Relations amongm sets of measures.Psychometrika, 26, 129–149.

    Google Scholar 

  • Kettenring, J. R. (1971). Canonical analysis of several sets of variables.Biometrika, 56, 433–451.

    Google Scholar 

  • Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness-of-fit to a nonmetric hypothesis.Psychometrika, 29, 1–28.

    Google Scholar 

  • Kuhfeld, W. F. (1985).Principal components of ordered categorical data. Unpublished doctoral dissertation, University of North Carolina.

  • Kuhfeld, W. F., Sarle, W. S., & Young, F. W. (1985). Methods in generating model estimates in the PRINQUAL macro.SUGI-Proceedings (pp. 962–971), Cary, NC: SAS-Institute.

    Google Scholar 

  • Lebart, L., Morineau, A., & Warwick, K. M. (1984).Multivariate descriptive analysis. New York: Wiley.

    Google Scholar 

  • Meulman, J. (1982).Homogeneity analysis of incomplete data. Leiden: DSWO-Press.

    Google Scholar 

  • Nishisato S. (1980).Analysis of categorical data: Dual scaling and its applications. Toronoto: University of Toronto Press.

    Google Scholar 

  • Rutishauser, H. (1969). Computational aspects of F. L. Bauer's simultaneous iteration method.Numerische Mathematik, 13, 4–13.

    Google Scholar 

  • Segijn, R. (1984). Lokale minima in PRINCALS [Local minima in PRINCALS]. Unpublished master's Thesis, Department of Data Theory, University of Leiden.

  • Takane, Y., Young, F. W., & de Leeuw, J. (1980). An individual differences additive model: An alternating least squares method with optimal scaling features.Psychometrika, 45, 183–209.

    Google Scholar 

  • Tenenhaus, M. & Young, F. W. (1985). An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis, and other methods for quantifying categorical multivariate data.Psychometrika, 50, 91–120.

    Google Scholar 

  • Thorndike, R. M. (1977). Canonical analysis and predictor selection.Multivariate Behavioral Research, 12, 75–87.

    Google Scholar 

  • van de Geer, J. P. (1985).HOMALS. (User's guide UG-85-02). Department of data theory, University of Leiden.

  • van de Geer, J. P. (1986).Introduction to multivariate data analysis (2 vols.). Leiden: DSWO-Press.

    Google Scholar 

  • van der Burg, E. (1983).CANALS (User's guide UG-85-05). Department of Data Theory, University of Leiden.

  • van der Burg, E., & de Leeuw, J. (1983). Non-linear canonical correlation.British Journal of Mathematical and Statistical Psychology, 36, 54–80.

    Google Scholar 

  • van der Burg, E., & de Leeuw, J. (1985). Use of the multinomial jackknife in generalized canonical correlation analysis. Paper presented at the Multidimensional Data Analysis Workshop, Cambridge, G.B.

  • van der Burg, E., de Leeuw, J., & Verdegaal, R. (1984).Nonlinear canonical correlation with m sets of variables (Research Report RR-84-12) Leiden: University of Leiden, Department of Data Theory.

    Google Scholar 

  • van der Lende, R., Kok, T. J., Peset Reig, R., Quanjer, Ph. H., Schouten, J. P., & Orie, N. G. M. (1981). Decreases in VC and FEV with time: Indicators for effects of smoking and air pollution.Bulletin Européen de Psychopathologie Respiratoire, 17, 775–792.

    Google Scholar 

  • van Pelt, W. J., Quanjer, Ph. W., Wise, M. E., van der Burg, E., & van der Lende, R. (1985). Analysis of maximum expiratory flow-volume curves using canonical correlation analysis.Methods of Information in Medicine, 24, 91–100.

    Google Scholar 

  • Verdegaal, R. (1985).Meer-sets analyse voor kwalitatieve gegevens [Multi-set analysis of qualitative data] (Research Report RR-85-14). Department of Data Theory, University of Leiden.

  • Verdegaal, R. (1986).OVERALS (User's guide UG-86-01). Department of Data Theory, University of Leiden.

  • Young, F. W. (1981). Quantitative analysis of qualitative data.Psychometrika, 46, 347–388.

    Google Scholar 

  • Young, F. W., de Leeuw, J., & Takane, Y. (1976). Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features.Psychometrika, 41, 505–529.

    Google Scholar 

  • Young, F. W., Takane, Y., & de Leeuw, J. (1978). The principal components of mixed measurement multivariate data: An alternating least squares method with optimal scaling features.Psychometrika, 43, 279–281.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

This research was partly supported by SWOV (Institute for Road Safety Research) in Leidschendam, The Netherlands.

Rights and permissions

Reprints and permissions

About this article

Cite this article

van der Burg, E., de Leeuw, J. & Verdegaal, R. Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features. Psychometrika 53, 177–197 (1988). https://doi.org/10.1007/BF02294131

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02294131

Key words

Navigation