Applied Statistics for the Social and Health Sciences

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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

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About the author (2012)

Rachel A. Gordon is an Associate Professor in the Department of Sociology and the Institute of Government and Public Affairs at the University of Illinois at Chicago. Professor Gordon has multidisciplinary substantive and statistical training and a passion for understanding and teaching applied statistics.

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