This book demonstrates the use of simple crosstabulation analyses based on causal models for the purpose of solving cause-and-effect problems involving quantities of statistical data. The author outlines the procedures for establishing causal effects from empirical data and the main kinds of problems faced when interpreting results. He also compares the causal approach to data analysis with other approaches, such as regression or loglinear analysis. In addition to introductory courses in statistics, the book will be useful in all courses where surveys are a primary source of information, such as those studying social stratification, political participation, or public opinion. Each chapter contains a summary and glossary, and exercises with answers. Social scientists and specialists on contingency table analysis will find many novel and interesting ideas.