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Index
Front Cover
APPLIED STATISTICS FOR THE SOCIAL AND HEALTH SCIENCES
Title Page
Copyright
TABLE OF CONTENTS IN BRIEF
TABLE OF CONTENTS IN DETAIL
Preface
Acknowledgments
PART 1: GETTING STARTED
Chapter 1: Examples of Quantitative Research in the Social and Health Sciences
1.1 What is Regression Analysis?
1.2 Literature Excerpt 1.1
1.3 Literature Excerpt 1.2
1.4 Literature Excerpt 1.3
1.5 Literature Excerpt 1.4
1.6 Summary
Chapter 2: Planning a Quantitative Research Project With Existing Data
2.1 Sources of Existing Data
2.2 Thinking Forward
2.3 Example Research Questions
2.4 Example of Locating Studies in ICPSR
2.5 Summary
Chapter 3: Basic Features of Statistical Packages and Data Documentation
3.1 How are our Data Stored in the Computer?
3.2 Why Learn Both SAS and STATA?
3.3 Getting Started with a Quantitative Research Project
3.4 Summary
Chapter 4: Basics of Writing Batch Programs with Statistical Packages
4.1 Getting Started with SAS and Stata
4.2 Writing a Simple Batch Program
4.3 Expanding the Batch Program to Create New Variables
4.4 Expanding the Batch Program to Keep a Subset of Cases
4.5 Complex Sampling Designs
4.6 Some Finishing Touches
4.7 Summary
PART 2: BASIC DESCRIPTIVE AND INFERENTIAL STATISTICS
Chapter 5: Basic Descriptive Statistics
5.1 Types of Variables
5.2 Literature Excerpts 5.1 and 5.2
5.3 Nominal Variables
5.4 Ordinal Variables
5.5 Interval Variables
5.6 Weighted Statistics
5.7 Creating a Descriptive Table
5.8 Summary
Chapter 6: Sample, Population and Sampling Distributions
6.1 Statistical Inference
6.2 Population and Sample Distributions
6.3 The Sampling Distribution
6.4 General Concepts for Statistical Inference
6.5 Other Common Theoretical Distributions
6.6 Summary
Chapter 7: Bivariate Inferential Statistics
7.1 Literature Excerpts
7.2 One Categorical and One Interval Variable
7.3 Two Categorical Variables
7.4 Two Interval Variables
7.5 Weighted Statistics
7.6 Summary
PART 3: ORDINARY LEAST SQUARES REGRESSION
Chapter 8: Basic Concepts of Bivariate Regression
8.1 Algebraic and Geometric Representations of Bivariate Regression
8.2 The Population Regression Line
8.3 The Sample Regression Line
8.4 Ordinary Least Squares Estimators
8.5 Complex Sampling Designs
8.6 Summary
Chapter 9: Basic Concepts of Multiple Regression
9.1 Algebraic and Geometric Representations of Multiple Regression
9.2 OLS Estimation of the Multiple Regression Model
9.3 Conducting Multiple Hypothesis Tests
9.4 General Linear F-Test
9.5 R -Squared
9.6 Information Criteria
9.7 Literature Excerpt 9.1
9.8 Summary
Chapter 10: Dummy Variables Dummy Variables
10.1 Why is a Different Approach Needed for Nominal and Ordinal Predictor Variables?
10.2 How Do We Define Dummy Variables?
10.3 Interpreting Dummy Variable Regression Models
10.4 Putting It All Together
10.5 Complex Sampling Designs
10.6 Summary
Chapter 11: Interactions
11.1 Literature Excerpt 11.1
11.2 Interactions Between Two Dummy Variables
11.3 Interaction Between a Dummy and an Interval Variable
11.4 Chow Test
11.5 Interaction Between Two Interval Variables
11.6 Literature Excerpt 11.2
11.7 Summary
Chapter 12: Nonlinear Relationships
12.1 Nonlinear Relationships
12.2 Summary
Chapter 13: Indirect Effects and Omitted Variable Bias
13.1 Literature Excerpt 13.1
13.2 Defining Confounders, Mediators, and Supressor Variables
13.3 Omitted Variable Bias
13.4 Summary
Chapter 14: Outliers, Heteroskedasticity, and Multicollinearity
14.1 Outliers and Influential Observations
14.2 Heteroskedasticity
14.3 Multicollinearity
14.4 Complex Sampling Designs
14.5 Summary
PART 4: THE GENERALIZED LINEAR MODEL
Chapter 15: Introduction to the Generalized Linear Model with a Continuous Outcome
15.1 Literature Excerpt 15.1
15.2 Maximum Likelihood Estimation
15.3 Hypothesis Testing with Maximum Likelihood Estimation
15.4 The Generalized Linear Model
15.5 Summary
Chapter 16: Dichotomous Outcomes
16.1 Literature Excerpt 16.1
16.2 Linear Probability Model
16.3 Generalized Linear Model
16.4 Goodness of Fit
16.5 Interpretation
16.6 Summary
Chapter 17: Multi-Category Outcomes
17.1 Multinomial Logit
17.2 Ordered Logit
17.3 Putting It All Together
17.4 Complex Sampling Designs
17.5 Summary
PART 5: WRAPPING UP
Chapter 18: Roadmap to Advanced Topics
18.1 Revisiting Literature Excerpts from Chapter
18.2 A Roadmap to Statistical Methods
18.3 A Roadmap to Locating Courses and Resources
18.4 Summary
APPENDICES
Appendix A: Summary of SAS and Stata Commands
Appendix B: Examples of Data Coding, and of the SAS and Stata Interface, Commands, and Results, Based on the National Survey of Families and Households B1
Appendix C: Screenshots of Data Set Documentation
Appendix D: Accessing the National Survey of Families and Households Raw Data File
Appendix E: Accessing the NHIS Data
Appendix F: Using SAS and Stata’s Online Documentation
Appendix G: Example of Hand-Calculating the Intercept, Slope, and Conditional Standard Deviation using Stylized Sample G1
Appendix H: Using Excel to Calculate and Graph Predicted Values
Appendix I: Using Hayes-Cai SAS Macro for Heteroskedasticity-Consistent Standard Errors
Notes
Bibliography
Glossary/Index
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