Tables, Figures, Exhibits, and Boxes
Preface
The Author
Introduction
1 Cross-Tabulations
What This Chapter Is About
Introduction to the Book via a Concrete Example
Cross-Tabulations
What This Chapter Has Shown
2 More on Tables
What This Chapter Is About
The Logic of Elaboration
Suppressor Variables
Additive and Interaction Effects
Direct Standardization
A Final Note on Statistical Controls Versus Experiments
What This Chapter Has Shown
3 Still More on Tables
What This Chapter is About
Reorganizing Tables to Extract New Information
When to Percentage a Table “Backwards”
Cross-Tabulations in Which the Dependent Variable is Represented by a Mean
Index of Dissimilarity
Writing About Cross-Tabulations
What This Chapter Has Shown
4 On the Manipulation of Data by Computer
What This Chapter Is About
Introduction
How Data Files Are Organized
Transforming Data
What This Chapter Has Shown
Appendix 4.A Doing Analysis Using Stata
Tips on Doing Analysis Using Stata
Some Particularly Useful Stata 10.0 Commands
5 Introduction to Correlation and Regression (Ordinary Least Squares)
What This Chapter Is About
Introduction
Quantifying the Size of a Relationship: Regression Analysis
Assessing the Strength of a Relationship: Correlation Analysis
The Relationship Between Correlation and Regression Coefficients
Factors Affecting the Size of Correlation (and Regression) Coefficients
Correlation Ratios
What This Chapter Has Shown
6 Introduction to Multiple Correlation and Regression (Ordinary Least Squares)
What This Chapter is About
Introduction
A Worked Example: The Determinants of Literacy in China
Dummy Variables
A Strategy for Comparisons Across Groups
A Bayesian Alternative for Comparing Models
Independent Validation
What This Chapter Has Shown
7 Multiple Regression Tricks: Techniques for Handling Special Analytic Problems
What This Chapter Is About
Nonlinear Transformations
Testing the Equality of Coefficients
Trend Analysis: Testing the Assumption of Linearity
Linear Splines
Expressing Coefficients as Deviations from the Grand Mean (Multiple Classification Analysis)
Other Ways of Representing Dummy Variables
Decomposing the Difference Between Two Means
What This Chapter Has Shown
8 Multiple Imputation of Missing Data
What This Chapter Is About
Introduction
A Worked Example: The Effect of Cultural Capital on Educational Attainment in Russia
What This Chapter Has Shown
9 Sample Design and Survey Estimation
What This Chapter Is About
Survey Samples
Conclusion
What This Chapter Has Shown
10 Regression Diagnostics
What This Chapter Is About
Introduction
A Worked Example: Societal Differences in Status Attainment
Robust Regression
Bootstrapping and Standard Errors
What This Chapter Has Shown
11 Scale Construction
What This Chapter Is About
Introduction
Validity
Reliability
Scale Construction
Errors-in-Variables Regression
What This Chapter Has Shown
12 Log-Linear Analysis
What This Chapter Is About
Introduction
Choosing a Preferred Model
Parsimonious Models
A Bibliographic Note
What This Chapter Has Shown
Appendix 12.A Derivation of the Effect Parameters
Appendix 12.B Introduction to Maximum Likelihood Estimation
Mean of a Normal Distribution
Log-Linear Parameters
13 Binomial Logistic Regression
What This Chapter Is About
Introduction
Relation to a Log-Linear Analysis
A Worked Logistic Regression Example: Predicting Prevalence of Armed Threats
A Second Worked Example: Schooling Progression Ratios in Japan
A Third Worked Example (Discrete-Time Hazard-Rate Models): Age at First Marriage
A Fourth Worked Example (Case–Control Models): Who Was Appointed to a Nomenklatura Position in Russia?
What This Chapter Has Shown
Appendix 13.A Some Algebra for Logs and Exponents
Appendix 13.B Introduction to Probit Analysis
14 Multinomial and Ordinal Logistic Regression and Tobit Regression
What This Chapter Is About
Multinomial Logit Analysis
Ordinal Logistic Regression
Tobit Regression (and Allied Procedures) for Censored Dependent Variables
Other Models for the Analysis of Limited Dependent Variables
What This Chapter Has Shown
15 Improving Causal Inference: Fixed Effects and Random Effects Modeling
What This Chapter Is About
Introduction
Fixed Effects Models for Continuous Variables
Random Effects Models for Continuous Variables
A Worked Example: The Determinants of Income in China
Fixed Effects Models for Binary Outcomes
A Bibliographic Note
What This Chapter Has Shown
16 Final Thoughts and Future Directions: Research Design and Interpretation Issues
What This Chapter Is About
Research Design Issues
The Importance of Probability Sampling
A Final Note: Good Professional Practice
What This Chapter Has Shown
Appendix A: Data Descriptions and Download Locations for the Data Used in This Book
Appendix B: Survey Estimation with the General Social Survey
References
Index
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