, Volume 52, Issue 4, pp 493–513 | Cite as

Analysis of contingency tables by ideal point discriminant analysis

  • Yoshio Takane


Cross-classified data are frequently encountered in behavioral and social science research. The loglinear model and dual scaling (correspondence analysis) are two representative methods of analyzing such data. An alternative method, based on ideal point discriminant analysis (DA), is proposed for analysis of contingency tables, which in a certain sense encompasses the two existing methods. A variety of interesting structures can be imposed on rows and columns of the tables through manipulations of predictor variables and/or as direct constraints on model parameters. This, along with maximum likelihood estimation of the model parameters, allows interesting model comparisons. This is illustrated by the analysis of several data sets.

Key words

Multidimensional scaling model evaluation AIC the loglinear model dual scaling (correspondence analysis) canonical analysis the RC association model 


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Copyright information

© The Psychometric Society 1987

Authors and Affiliations

  • Yoshio Takane
    • 1
  1. 1.Department of PsychologyMcGill UniversityMontrealCanada

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