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Related Experiment Videos

Exploring contingency tables with correspondence analysis.

E B Moser1

  • 1Department of Experimental Statistics, Louisiana State University Agricultural Center, Baton Rouge 70803.

Computer Applications in the Biosciences : CABIOS
|July 1, 1989
PubMed
Summary
This summary is machine-generated.

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Correspondence analysis offers a graphical method to supplement log-linear models for analyzing contingency tables. This technique aids in identifying and interpreting models, particularly for biological and survey data.

Area of Science:

  • Statistics
  • Computational Biology
  • Data Analysis

Background:

  • Log-linear models are standard for analyzing contingency tables.
  • Interpreting complex relationships within multi-dimensional tables can be challenging.
  • Visualizing these relationships aids in model identification and interpretation.

Purpose of the Study:

  • To describe and implement a correspondence analysis algorithm.
  • To demonstrate its utility in supplementing log-linear model analysis for contingency tables.
  • To showcase its application across various biological and survey datasets.

Main Methods:

  • Implementation of a correspondence analysis algorithm in SAS/IML.
  • Application to two-way contingency tables (e.g., tumor data).

Related Experiment Videos

  • Extension to three-way contingency tables (e.g., ulcer patient survey data) with fixed margins.
  • Simultaneous display of related tables (e.g., bird diets and foraging behaviors).

    • Main Results:

    • Correspondence analysis effectively supplements log-linear models in both model identification and interpretation.
    • Graphical outputs highlight relationships between log-linear model parameters and table structures.
    • The technique is versatile, applicable to simple and higher-dimensional contingency tables.
    • Simultaneous displays reveal inter-table relationships, enhancing ecological data interpretation.

    Conclusions:

    • Correspondence analysis provides a valuable graphical complement to traditional log-linear modeling for contingency tables.
    • Its application enhances the understanding of complex associations in biological and social science data.
    • The SAS/IML implementation offers a practical tool for researchers in these fields.