Recommended Readings

See the following texts for more information on categorical principal components analysis:

De Haas, M., J. A. Algera, H. F. J. M. Van Tuijl, and J. J. Meulman. 2000. Macro and micro goal setting: In search of coherence. Applied Psychology, 49, 579-595.

De Leeuw, J. 1982. Nonlinear principal components analysis. In: COMPSTAT Proceedings in Computational Statistics. Vienna: Physica Verlag.

Eckart, C., and G. Young. 1936. The approximation of one matrix by another one of lower rank. Psychometrika, 1, 211-218.

Gabriel, K. R. 1971. The biplot graphic display of matrices with application to principal components analysis. Biometrika, 58, 453-467.

Gifi, A. 1985. PRINCALS. Research Report UG-85-02. Leiden: Department of Data Theory, University of Leiden.

Gower, J. C., and J. J. Meulman. 1993. The treatment of categorical information in physical anthropology. International Journal of Anthropology, 8, 43-51.

Heiser, W. J., and J. J. Meulman. 1994. Homogeneity analysis: Exploring the distribution of variables and their nonlinear relationships. In: Correspondence Analysis in the Social Sciences: Recent Developments and Applications, M. Greenacre, and J. Blasius, eds. New York: Academic Press.

Kruskal, J. B. 1978. Factor analysis and principal components analysis: Bilinear methods. In: International Encyclopedia of Statistics, W. H. Kruskal, and J. M. Tanur, eds. New York: The Free Press.

Kruskal, J. B., and R. N. Shepard. 1974. A nonmetric variety of linear factor analysis. Psychometrika, 39, 123-157.

Meulman, J. J. 1993. Principal coordinates analysis with optimal transformations of the variables: Minimizing the sum of squares of the smallest eigenvalues. British Journal of Mathematical and Statistical Psychology, 46, 287-300.

Meulman, J. J., and P. Verboon. 1993. Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables. Psychometrika, 58, 7-35.

Meulman, J. J., A. J. Van der Kooij, and A. Babinec. 2000. New features of categorical principal components analysis for complicated data sets, including data mining. In: Classification, Automation and New Media, W. Gaul, and G. Ritter, eds. Berlin: Springer-Verlag.

Meulman, J. J., A. J. Van der Kooij, and W. J. Heiser. 2004. Principal components analysis with nonlinear optimal scaling transformations for ordinal and nominal data. In: Handbook of Quantitative Methodology for the Social Sciences, D. Kaplan, eds. Thousand Oaks, Calif.: Sage Publications, Inc..

Theunissen, N. C. M., J. J. Meulman, A. L. Den Ouden, H. M. Koopman, G. H. Verrips, S. P. Verloove-Vanhorick, and J. M. Wit. 2003. Changes can be studied when the measurement instrument is different at different time points. Health Services and Outcomes Research Methodology, 4, 109-126.

Tucker, L. R. 1960. Intra-individual and inter-individual multidimensionality. In: Psychological Scaling: Theory & Applications, H. Gulliksen, and S. Messick, eds. New York: John Wiley and Sons.

Vlek, C., and P. J. Stallen. 1981. Judging risks and benefits in the small and in the large. Organizational Behavior and Human Performance, 28, 235-271.

Wagenaar, W. A. 1988. Paradoxes of gambling behaviour. London: Lawrence Erlbaum Associates, Inc.

Young, F. W., Y. Takane, and J. De Leeuw. 1978. The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features. Psychometrika, 43, 279-281.

Zeijl, E., Y. te Poel, M. du Bois-Reymond, J. Ravesloot, and J. J. Meulman. 2000. The role of parents and peers in the leisure activities of young adolescents. Journal of Leisure Research, 32, 281-302.