Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with the basic skills that they need to estimate, interpret, present, and publish statistical models using contemporary standards. The book targets the social and health science branches such as human development, public health, sociology, psychology, education, and social work in which students bring a wide range of mathematical skills and have a wide range of methodological affinities. For these students, a successful course in statistics will not only offer statistical content but will also help them develop an appreciation for how statistical techniques might answer some of the research questions of interest to them. This book is for use in a two-semester graduate course sequence covering basic univariate and bivariate statistics and regression models for nominal and ordinal outcomes, in addition to covering ordinary least squares regression.Key features of the book include:interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature thorough integration of teaching statistical theory with teaching data processing and analysisteaching of both SAS and Stata "e;side-by-side"e; and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.This book is for a two-semester course. For a one-semester course, see routledge.com/9780415991544/

Part 1: Getting Started 1. Examples of Social Science Research Using Regression Analyses 2. Planning a Quantitative Research Project with Existing Data 3. Basic Features of Statistical Packages and Data Documentation 4. Basics of Writing Batch Programs with Statistical Packages Part 2: Basic Descriptive and Inferential Statistics 5. Basic Descriptive Statistics 6. Sample, Population and Sampling Distributions 7. Basic Inferential Statistics Part 3: Ordinary Least Squares Regression 8. Basic Concepts of Bivariate Regression 9. Basic Concepts of Multiple Regression 10. Dummy Variables 11. Interactions 12. Nonlinear Relationships 13. Indirect Effects and Omitted Variable Bias 14. Outliers, Heteroskedasticity, and Multicollinearity Part 4: The Generalized Linear Model 15. Introduction to the Generalized Linear Model with a Continuous Outcome 16. Dichotomous outcomes 17. Multi-Category Outcomes Part 5: Wrapping Up 18. Roadmap to Advanced Topics