Quantitative Data Analysis: Conducting Informed Social Research, introduces students to quantitative data analysis, concentrating on the key issues such as how to decide which statistical procedure is suitable, and how to interpret the subsequent results. Each chapter includes illustrative examples, with exercises allowing reader to test their understanding of each topic. No previous knowledge of statistics or computing is required, as the book takes the reader step-by-step through the most common procedures and techniques, including how to conduct theoretically informed quantitative social research. Perfect for beginning graduate students in social sciences, medical sciences, and education, the book imparts a solid and practical approach to making sociological sense out of a body of quantitative data. A bonus is the book's many examples using Stata software. This book can be used as a core text in courses offered through sociology and anthropology programs, under names such as Quantitative Data Analysis, Methods of Analysis in the Social Sciences, Statistical Analysis of Quantitative Data. The book can also be used in any research methods course in social and health sciences, including general/quantitative methods and survey research methods. The book, based on the authors 20 years of teaching at one of the nations leading sociology programs, covers the most key and essential topics for researchers: tabular analysis, log linear models for tabular data, regression analysis in its various forms, regression diagnostics and robust regression, ways to cope with missing data, logistic regression, factor analysis and other techniques of scale construction, measurement error, and related topics. While not a statistics text, it emphasizes the use of statistical procedures to draw substantive conclusions about how the social world works. Readers will become familiar with analysis and interpretation of nonexperimental quantitative data, focusing on sample survey and census data. The book explains the logic of analysis and problems of statistical inference, including diagnostic procedures and methods for handling complex sample survey designs. Table of Contents: 1. Quantitative Data Analysis: What It Is and How Its Used 2. Cross Tabulations 3. Interpreting Cross Tabulations 4. Manipulation of Tabular Data 5. Computerized Manipulation and Statistical Analysis of Data 6. Correlation and Regression 7. Bayesian Alternatives 8. Multiple Regression Tricks for Handling Analytic Problems 9. Multiple Imputation of Missing Data 10. Sample Design and Survey Estimation 11. Regression Diagnostics 12. Scale Construction 13. Log-linear Analysis 14. Binomial Logistic Regression 15. Multinomial and Ordinal Logistic Regression and Tobit Regression 16. Improving Causal Inference: Fixed Effects and Random Effects Modeling 17. Final Thoughts and Future Directions: Comments on Issues of Research Design and Interpretations