The Analysis of Cross-Classified Categorical Data

1. Front Matter

   Pages i-xiii

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2. Introduction

   Pages 1-7

3. Two-Dimensional Tables

   Pages 8-26

4. Three-Dimensional Tables

   Pages 27-55

5. Selection of a Model

   Pages 56-70

6. Four- and Higher-Dimensional Contingency Tables

   Pages 71-94

7. Fixed Margins and Logit Models

   Pages 95-119

8. Causal Analysis Involving Logit and Loglinear Models

   Pages 120-139

9. Fixed and Random Zeros

   Pages 140-163

10. Back Matter

    Pages 165-198

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A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation.

• Logit
• categorical data analysis
• contingency tables
• log-linear models
• logistic regression models

From the reviews of the second edition:

"This is a great book on the analysis of cross-classified categorical data, particularly for non-statistical readers. The need for analyzing cross-classified categorical data has been growing rapidly … . The loglinear models and maximum-likelihood estimates discussed in this book are very helpful for practitioners to get hands on categorical data and perform sound analyses. This book is comprised of 8 chapters. … is a useful book and I recommend it to data analysts in practical and academic fields." (Zhen Mei, Zentralblatt MATH, Vol. 1134 (12), 2008)

"This Springer edition is a reprint of the first edition published by MIT Press in 1977. … this a useful resource book for applied statisticians with good theoretical background." (Technometrics, Vol. 50 (4), November, 2008)

• Carnegie-Mellon University, Pittsburgh

  Stephen E. Fienberg

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