Scott M. Lynch is a professor in the Department of Sociology and Office of Population Research at Princeton University.  His substantive research interests are in changes in racial and socioeconomic inequalities in health and mortality across age and time, as well as in understanding the sources of regional disparities in health in the U.S.  His methodological interests are in the application of Bayesian statistics and estimation methods to problems that cannot be easily addressed with classical statistical methods in sociology and demography.

Preface.- List of Symbols.- Contents.- List of Figures.- List of Tables.- Introduction.- Overview of Research Process.- Data and Its Acquisition.- Summarizing Data with Descriptive Statistics.- Probability Theory.- Statistical Inference.- Statistical Approaches for Nominal Data.- Comparing Means Across Multiple Groups.- Correlation and Simple Regression.- Introduction to Multiple Regression.- Presenting Results of Statistical Analysis.- Conclusion.- Statistical Tables.- Answers to Selected Exercises.- References.- Index.

This book covers applied statistics for the social sciences with upper-level undergraduate students in mind. The chapters are based on lecture notes from an introductory statistics course the author has taught for a number of years. The book integrates statistics into the research process, with early chapters covering basic philosophical issues underpinning the process of scientific research. These include the concepts of deductive reasoning and the falsifiability of hypotheses, the development of a research question and hypotheses, and the process of data collection and measurement. Probability theory is then covered extensively with a focus on its role in laying the foundation for statistical reasoning and inference. After illustrating the Central Limit Theorem, later chapters address the key, basic statistical methods used in social science research, including various z and t tests and confidence intervals, nonparametric chi square tests, one-way analysis of variance,  correlation, simple regression, and multiple regression, with a discussion of the key issues involved in thinking about causal processes. Concepts and topics are illustrated using both real and simulated data. The penultimate chapter presents rules and suggestions for the successful presentation of statistics in tabular and graphic formats, and the final chapter offers suggestions for subsequent reading and study.