# Applied Statistics for the Social and Health Sciences

Routledge, Jul 26, 2012 - Social Science - 994 pages

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 analysis
• teaching of both SAS and Stata "side-by-side" 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 http://www.routledge.com/9780415991544/

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### Contents

 Examples of Quantitative Research in the Social and Health Sciences 3 Planning a Quantitative Research Project with Existing Data 24 Basic Features of Statistical Packages and Data Documentation 39 Basics of Writing Batch Programs with Statistical Packages 65 Expanding the Batch Program to Keep a Subset of Cases 83 BASIC DESCRIPTIVE AND INFERENTIAL STATISTICS 95 Sample Population and Sampling Distributions 143 Bivariate Inferential Statistics 196
 THE GENERALIZED LINEAR MODEL 517 TABLE OF CONTENTS IN DETAIL 537 Dichotomous Outcomes 552 MultiCategory Outcomes 609 Roadmap to Advanced Topics 671 APPENDICES 687 Appendix B Examples of Data Coding and of the SAS and Stata Interface B-1 Screenshots of DataSet Documentation D-1

 ORDINARY LEAST SOUARES REGRESSION 237 Basic Concepts of Multiple Regression 294 Dummy Variables 334 Interactions 381 Nonlinear Relationships 433 Indirect Effects and Omitted Variable Bias 461 Outliers Heteroskedasticity and Multicollinearity 481
 Appendix E Accessing the NHIS Data D-11 Example of HandCalculating the Intercept Slope D-19 Using HayesCai SAS Macro for HeteroskedasticityConsistent 683 Appendices 685 Bibliography 704 704 GlossaryIndex 715 Copyright