A Course in Categorical Data Analysis

Thomas Leonard

• Publisher: Chapman and Hall/CRC
• Year: 2020
• eBook ISBN: 9781000154658
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Preface
• Special Software
• 1 Sampling Distributions
• 2 Two-by-Two Contingency Tables
• 3 Simpson’s Paradox and 23 Tables
• 4 The Madison Drug and Alcohol Abuse Study
• 5 Goodman’s Full-Rank Interaction Analysis
• 6 Further Examples and Extensions
• 7 Conditional Independence Models for Two-Way Tables
• 8 Logistic Regression
• 9 Further Regression Models
• 10 Final Topics
• References
• Index