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### Summary

### Summary

Categorical Statistics for Communication Research presents scholars with a discipline-specific guide to categorical data analysis. The text blends necessary background information and formulas for statistical procedures with data analyses illustrating techniques such as log- linear modeling and logistic regression analysis.

Provides techniques for analyzing categorical data from a communication studies perspective Provides an accessible presentation of techniques for analyzing categorical data for communication scholars and other social scientists working at the advanced undergraduate and graduate teaching levels Illustrated with examples from different types of communication research such as health, political and sports communication and entertainment Includes exercises at the end of each chapter and a companion website containing exercise answers and chapter-by-chapter PowerPoint slides### Author Notes

Bryan E. Denham is Professor of Communication at Clemson University. An expert on logistic regression and log-linear modeling, he has published articles on teaching empirical research method and applying categorical statistics to social data in outlets such as the Journal of Communication, Journalism Mass Communication Quarterly Journalism Mass Communication Educator, and the Journal of Risk Research. He has taught empirical methods at both the graduate and undergraduate levels and has frequently served as a resource for both faculty and students in the use of categorical statistics from areas such as health, political, spoils, and science communication.

### Table of Contents

Preface | p. xiii |

Acknowledgments | p. xix |

About the Companion Website | p. xx |

1 Introduction to Categorical Statistics | p. 1 |

Historical Overview | p. 2 |

Probability Distributions and Parameter Estimation | p. 5 |

Example of Maximum Likelihood Estimation | p. 6 |

A Note on Statistical Software | p. 7 |

Chapter Summary | p. 7 |

Chapter Exercises | p. 8 |

Notes | p. 9 |

References | p. 10 |

2 Univariate Goodness of Fit and Contingency Tables in Two Dimensions | p. 12 |

Chi-Square Test for Goodness of Fit | p. 12 |

Chi-Square Test of Independence in Contingency Tables | p. 14 |

Likelihood Ratio Statistic | p. 16 |

Exact Tests for Small Samples | p. 17 |

McNemar's Test for Correlated Samples | p. 18 |

Measures of Association | p. 19 |

Odds Ratio | p. 19 |

Relative Risk | p. 21 |

Phi Coefficient | p. 22 |

Cramer's V | p. 23 |

Pearson's Contingency Coefficient | p. 24 |

Kendall's Tan | p. 25 |

Goodman and Kruskal's Gamma | p. 28 |

Somers'd | p. 28 |

Points of Concern in Bivariate Analyses | p. 29 |

SPSS Analyses | p. 31 |

Testing Goodness of Tit in SPSS | p. 31 |

Testing Independence in SPSS | p. 32 |

A Note on Style | p. 34 |

Chapter Summary | p. 35 |

Chapter Exercises | p. 36 |

Notes | p. 37 |

References | p. 38 |

3 Contingency Tables in Three Dimensions | p. 41 |

Moving from Two to Three Dimensions | p. 41 |

Cochran-Mantel-Haenszel Test | p. 43 |

Breslow-Day Test | p. 45 |

An Example in Public Health | p. 48 |

An Example in Political Communication | p. 50 |

Chapter Summary | p. 54 |

Chapter Exercises | p. 54 |

Note | p. 56 |

References | p. 56 |

4 Log-linear Analysis | p. 58 |

Development of Log-linear Models | p. 59 |

Examples of Published Research | p. 59 |

Log-linear Analysis: Fundamentals | p. 60 |

Two-way Tables | p. 61 |

Three-way Models | p. 62 |

Goodness of Fit and Model Selection | p. 64 |

Descriptive Statistics and Residuals for the Fitted Model | p. 65 |

Parameter Estimation | p. 67 |

Ordinal Log-linear Analysis | p. 70 |

Three Ordinal Measures | p. 72 |

More Complex Models | p. 75 |

Visual Displays | p. 78 |

Chapter Summary | p. 83 |

Chapter Exercises | p. 83 |

Notes | p. 85 |

References | p. 86 |

5 Logit Log-linear Analysis | p. 90 |

Examples of Published Research | p. 91 |

Logit Log-linear Analysis: Fundamental Components | p. 92 |

Logit Model with One Response Measure | p. 93 |

Logit Model with Two Response Measures | p. 98 |

SPSS Example | p. 106 |

Correspondence Analysis | p. 113 |

Chapter Summary | p. 114 |

Chapter Exercises | p. 114 |

References | p. 116 |

6 Binary Logistic Regression | p. 119 |

Examples of Published Research | p. 120 |

Binary Logistic Regression: Fundamentals | p. 121 |

Simple Logistic Regression Analysis | p. 123 |

Multiple Logistic Regression Analysis | p. 124 |

Interactions | p. 127 |

Model Assessment | p. 128 |

Additional Statistics | p. 128 |

Diagnostic Considerations | p. 129 |

Binary Logistic Regression in SPSS | p. 130 |

Chapter Summary | p. 144 |

Chapter Exercises | p. 144 |

Notes | p. 146 |

References | p. 146 |

7 Multinomial Logistic Regression | p. 153 |

Examples of Published Research | p. 154 |

Multinomial Logistic Regression: Fundamentals | p. 154 |

Simple Multinomial Logistic Regression Analysis | p. 155 |

Multiple Multinomial Logistic Regression Analysis | p. 157 |

Conditional Logit Modeling | p. 159 |

Multinomial Logistic Regression in SPSS | p. 160 |

Chapter Summary | p. 165 |

Chapter Exercises | p. 165 |

Notes | p. 168 |

References | p. 168 |

8 Ordinal Logistic Regression | p. 171 |

Examples of Published Research | p. 172 |

Ordinal Logistic Regression: Fundamentals | p. 172 |

Simple Ordinal Logistic Regression Analysis | p. 175 |

Multiple Ordinal Logistic Regression Analysis | p. 176 |

Interactions | p. 180 |

Ordinal Logistic Regression in SPSS | p. 182 |

Chapter Summary | p. 184 |

Chapter Exercises | p. 186 |

Notes | p. 193 |

References | p. 193 |

9 Probit Analysis | p. 198 |

Examples of Published Research | p. 199 |

Probit Analysis: Fundamentals | p. 200 |

Binary Prohit Analysis | p. 201 |

Ordinal Probit Analysis | p. 206 |

Multinomial Probit Analysis | p. 208 |

Interactions | p. 208 |

Chapter Summary | p. 211 |

Chapter Exercises | p. 211 |

Notes | p. 212 |

References | p. 213 |

10 Poisson and Negative Binomial Regression | p. 216 |

Examples of Published Research | p. 217 |

Poisson Regression: Fundamentals | p. 218 |

Negative Binomial Regression: Fundamentals | p. 220 |

Additional Techniques | p. 222 |

SPSS Analyses | p. 223 |

Chapter Summary | p. 227 |

Chapter Exercises | p. 227 |

Notes | p. 229 |

References | p. 229 |

11 Interrater Agreement Measures for Nominal and Ordinal Data | p. 232 |

Analysis of Nominal Data with Two Raters | p. 233 |

Analysis of Nominal Data with Multiple Raters | p. 238 |

Analysis of Ordinal Data with Two Raters | p. 241 |

Analysis of Ordinal Data with Multiple Raters | p. 245 |

Kappa Coefficient in SPSS | p. 247 |

Intraclass Correlation Coefficients in SPSS | p. 249 |

Chapter Summary | p. 250 |

Chapter Exercises | p. 250 |

Notes | p. 251 |

References | p. 252 |

12 Concluding Communication | p. 255 |

References | p. 257 |

Appendix A Chi-Square Table | p. 259 |

Appendix B SPSS Code for Selected Procedures | p. 261 |

Index | p. 266 |