A Course in Categorical Data Analysis

Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students, A Course in Categorical Data Analysis presents the easiest, most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet, readers do not need full knowledge of a statistical software package.

Categorical data-comprising counts of individuals, objects, or entities in different categories-emerge frequently from many areas of study, including medicine, sociology, geology, and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore, the ability to manipulate, understand, and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines. Although t-tests, linear regression, and analysis of variance are useful, valid methods for analysis of measurement data, categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students, A Course in Categorical Data Analysis presents the easiest, most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet, readers do not need full knowledge of a statistical software package.In this unique text, the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data, but on using different models that may lead to meaningful conclusions. The book offers some simple, innovative techniques not highighted in other texts that help make the book accessible to a broad, interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific, medical, or real-life conclusions from categorical data sets.

SAMPLING DISTRIBUTIONS Experimental Design for a Population Proportion Further Properties of the Binomial Distribution Statistical Procedures for the Binomial Distribution The Poisson Distribution Statistical Procedures for the Poisson Distribution The Multinomial distribution Sir Ronald Fisher's Conditioning Result More General Sampling Models Generalizing the Binomial Distribution The Discrete Exponential Family of Distributions Generalizing the Multinomial Distribution TWO BY TWO CONTINGENCY TABLES Conditional Probability and Independence Independence of Rows and Columns Investigating Independence, Given Observational Data Edwards' Theorem Log Contrasts and the Multinomial Distribution The Log Measure of Association Test The Product Binomial Model The Independent Poisson Model Fisher's Exact Test Power Properties of our Test Procedures SIMPSON'S PARADOX AND 23 TABLES Probability Theory The Cornish Pixie/Irish Leprechaun Example Interpretation of Simpson's Paradox The Three-Directional Approach Measure of Association Analysis for 23 Tables Medical Example Testing Equality for two 2 x 2 Tables The Three-directional Approach of the Analysis 23 Tables THE MADISON DRUG ALCOHOL ABUSE STUDY Experimental Design Statistical Results (phase 3) of Study Further Validation of Results GOODMAN'S FULL RANK INTERACTION ANALYZED FOR TWO WAY TABLES Introductory Example Methodological Developments Numerical Example Methodological Developments Business School Example Methodological Developments Advertising Example Testing for Equality of Unconditional Cell Probabilities Analysis of Berkeley Admissions Data Further Data Sets FURTHER EXAMPLES AND EXTENSIONS Hypertension, Obesity, and Alcohol Consumption The Bristol Cervical Screening Data The Multiple Sclerosis Data The Dundee Dental Health Data CONDITIONAL INDEPENDENCE MODELS FOR TWO-WAY TABLES Fixed Zeros and Missing Observations Incomplete Tables Perfectly Fitting Further Cells Complete Tables Further Data Sets LOGISTIC REGRESSION Review of General Methodology Analyzing Your Data Using S-PLUS Analysis of Mice Exposure Data Analysis of Space Shuttle Failure Data Further Data Sets FURTHER REGRESSION MODELS Regression Models for Poisson Data The California Eqrthquake Data A Generalization of Logistic Regression Logistic Regression for Matched Case-Control Studies Further Data FINAL TOPICS Continuous Random Variables Logistic Discrimination Analysis Testing The Slope and Quadratic Term Extensions Three-Way Contingency Tables INDEX