Contingency Tables

  • J. D. Jobson
Part of the Springer Texts in Statistics book series (STS)

Abstract

This chapter begins with an introduction for Volume II and then presents a survey of the techniques available for analyzing contingency tables. The introduction consists of a discussion of data matrices measurement scales and an outline of techniques presented in Volume II. The discussion of contingency tables begins in the second section with a review of bivariate analysis for two categorical random variables and includes a discussion of inference techniques for two-dimensional tables. The discussion of two-dimensional tables also includes an introduction to the use of loglinear models. The third section presents a discussion of the application of loglinear models to multidimensional tables based on the maximum likelihood approach to estimation. The logit model is also introduced as a special case of the loglinear model. The last section of the chapter outlines the weighted least squares approach to modeling categorical data. The weighted least squares approach affords a greater variety of models than the maximum likelihood method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Cited Literature and References

  1. 1.
    Agresti, Alan (1984).Analysis of Ordinal Categorical Data. NewYork: John Wiley and Sons.MATHGoogle Scholar
  2. 2.
    Andersen, Erling B. (1980).Discrete Statistical Models With Social Science Applications.Amsterdam: North-Holland Publishing Company.Google Scholar
  3. 3.
    Andersen, Erling B. (1990).The Statistical Analysis of Categorical Data.Berlin: Springer-Verlag.MATHCrossRefGoogle Scholar
  4. 4.
    Bishop, Yvonne M.M., Fienberg, Stephen E., and Holland, Paul W. (1975).Discrete Multivariate Analysis: Theory and Practice.Cambridge, Ma.: MIT Press.Google Scholar
  5. 5.
    Christensen, Ronald (1991).Log-Linear Models.New York: Springer-Verlag.Google Scholar
  6. 6.
    Everitt, B.S. (1977).The Analysis of Contingency Tables.London: Chapman and Hall.Google Scholar
  7. 7.
    Fienberg, Stephen E. (1980).The Analysis of Cross-Classified Categorical DataSecond Edition. Cambridge, Ma.: MIT Press.MATHGoogle Scholar
  8. 8.
    Forthofer, Ron N. and Lehnen, Robert G. (1981).Public Program Analysis.Belmont, Ca.: Lifetime Learning Publications.CrossRefGoogle Scholar
  9. 9.
    Freeman, Daniel H. Jr. (1987).Applied Categorical Data Analysis.New York: Marcel Dekker.MATHGoogle Scholar
  10. 10.
    Grizzle, James E., Starmer, C. and Koch, Gary G. (1969). “Analysis of Categorical Data by Linear Models,”Biometrics25, 489–504.MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Koch, G.G., Landis, J.R., Freeman, J.L., Freeman, D.H., and Lehnen, R.G. (1977). “A General Methodology for the Analysis of Experiments with Repeated Measurement of Categorical Data,”Biometrics33, 133–158.MATHCrossRefGoogle Scholar
  12. 12.
    Reynolds, H.T. (1977).The Analysis of Cross-Classifications.New York: The Free Press.Google Scholar
  13. 13.
    Santner, Thomas J. and Duffy, Dianne E. (1989).The Statistical Analysis of Discrete Data.New York: Springer-Verlag.MATHCrossRefGoogle Scholar
  14. 14.
    Upton, Graham J.G. (1978).The Analysis of Cross-Tabulated Data.New York: John Wiley and Sons.Google Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • J. D. Jobson
    • 1
  1. 1.Faculty of BusinessUniversity of AlbertaEdmontonCanada

Personalised recommendations