Visual Models for Categorical Data in Economic Research

  • Justyna Brzezińska
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This paper is concerned with the use of visualizing categorical data in qualitative data analysis (Friendly, Visualizing categorical data, SAS Press, 2000. ISBN 1-58025-660-0; Meyer et al., J. Stat. Softw., 2006; Meyer et al., vcd: Visualizing Categorical Data. R package version 1.0.9, 2008). Graphical methods for qualitative data and extension using a variety of R packages will be presented. This paper outlines a general framework for visual models for categorical data. These ideas are illustrated with a variety of graphical methods for categorical data for large, multi-way contingency tables. Graphical methods are available in R software in vcd and vcdExtra library including mosaic plot, association plot, sieve plot, double-decker plot or agreement plot. These R packages include methods for the exploration of categorical data, such as fitting and graphing, plots and tests for independence or visualization techniques for log-linear models. Some graphs, e.g. mosaic display plots are well-suited for detecting patterns of association in the process of model building, others are useful in model diagnosis and graphical presentation and summaries. The use of log-linear analysis, as well as visualizing categorical data in economic research, will be presented in this paper.


  1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrow & F. Czaki (Eds.), Proceedings of the 2nd international symposium on information. Budapest: Akademiai Kiado.Google Scholar
  2. Anderson, C. J., & Vermunt, J. K. (2000). Log-multiplicative association models as latent variable models for nominal and/or ordinal data. Sociological Methodology, 30(1), 81–121.CrossRefGoogle Scholar
  3. Bertin, J. (1983). Semiology of graphics. Madison: University of Wisconsin Press.Google Scholar
  4. Deming, W., & Stephan, F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Annals of Mathematical Statistics, 11(4), 427–444.MathSciNetCrossRefGoogle Scholar
  5. Friendly, M. (1992). Mosaic display for log-linear models. In ASA, Proceedings of the statistical graphics section (pp. 61–68), Alexandria, VA.Google Scholar
  6. Friendly, M. (1994). Mosaic displays for multi-way contingency tables. Journals of the American Statistical Association, 89, 190–200.CrossRefGoogle Scholar
  7. Friendly, M. (1995). Conceptual and visual models for categorical data. The Amercian Statistician, 49(2), 153–160.Google Scholar
  8. Friendly, M. (1999). Extending mosaic displays: Marginal, conditional, and partial views of categorical data. Journal of Computational and Graphical Statistics, 8(3), 373–395.Google Scholar
  9. Friendly, M. (2000). Visualizing categorical data. Cary, NC: SAS Press. ISBN 1-58025-660-0.Google Scholar
  10. Goodman, L. A. (1970). The multivariate analysis of qualitative data: Interaction among multiple classifications. Journal of the American Statistical Association, 65, 226–256.CrossRefGoogle Scholar
  11. Hartigan, J. A., & Kleiner, B. (1981). Mosaics for contingency tables. In Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface.Google Scholar
  12. Hartigan, J. A., & Kleiner, B. (1984). A mosaic of television ratings. The Amercian Statistician, 38(1), 32–35.Google Scholar
  13. Knoke, D., & Burke, P. J. (1980). Log-linear models. Beverly Hills: Sage.MATHGoogle Scholar
  14. Mayer, D., Hornik, K., & Zeileis, A. (2006). The strucplot framework: Visualizing multi-way contingency tables with vcd. Journal of Statistical Software, 17(3), 1–48.Google Scholar
  15. Meyer, D., Zeileis, A., & Hornik, K. (2008). VCD: Visualizing categorical data, R package.
  16. Raftery, A. E. (1986). Choosing models for cross-classification. American Sociological Review, 51, 145–146.CrossRefGoogle Scholar
  17. Van Rosmalen, J. M., Koning, A. J., & Groenen, P. J. F. (1999). Optimal scaling of interaction effects in generalized linear modelling. Multivariate Behavioral Research, 44, 59–81.CrossRefGoogle Scholar
  18. Theus, M., & Lauer, R. W. (1999). Technical report 12, visualizing loglinear models. Journal of Computational and Graphical Methods, 8(3), 396–412.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of Economics in KatowiceKatowicePoland

Personalised recommendations