Project IV

Categorical Data Analysis

Tahani Coolen-Maturi

Description

Categorical data are particularly common in social and medical applications. For example, categorical data occur in social science as measurements of attitudes and opinions, and in health sciences as measurements of responses such as whether we have full recovery of patients (yes, no) or the severity of disease (none, mild, moderate, severe). Many statistical methods are developed for analysing categorical data, to name a few:

  • Generalized linear models for binary and count data
  • Logistic regression 
  • Logit models
  • Loglinear models for contingency tables

    • There are many interesting theoretical and applied topics students may want to work on, where implementation can be done using the statistical package R.

    Prerequisites

    Statistical Methods III

    Corequisites

    Topics in Statistics (recommended)

    References

    • Agresti, A. An Introduction to Categorical Data Analysis, John Wiley & Sons, 2007.
    • Agresti, A. Analysis of Ordinal Categorical Data, Wiley, 2012.
    • Agresti, A. Categorical Data Analysis, Wiley-Blackwell, 2013.
    • Bilder, C.R. and Loughin, T.M. Analysis of Categorical Data with R, Chapman and Hall/CRC, 2014.
    • Kateri, M. Contingency Table Analysis: From Theory to Applications, Springer, 2014.

    email: Tahani Coolen-Maturi

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