Gordon R.A. Applied Statistics for the Social and Health Sciences

London: Routledge, 2012. — 742 p. — ISBN: 9780415875363.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.Getting Started
Examples of Quantitative Research in the Social and Health Sciences
Planning a Quantitative Research Project with Existing Data
Basic Features of Statistical Packages and Data Documentation
Basics of Writing Batch Programs with Statistical Packages
Basic Descriptive and Inferential Statistics
Basic Descriptive Statistics
Sample, Population and Sampling Distributions
Bivariate Inferential Statistics
Ordinary Least Squares Regression
Basic Concepts of Bivariate Regression
Basic Concepts of Multiple Regression
Dummy Variables
Interactions
Nonlinear Relationships
Indirect Effects and Omitted Variable Bias
Outliers, Heteroskedasticity, and Multicollinearity
The Generalized Linear Model
Introduction to the Generalized Linear Model with a Continuous Outcome
Dichotomous Outcomes
Multi-Category Outcomes
Wrapping Up
Roadmap to Advanced Topics