Quantitative data analysis: doing social research to test ideas

Contents
List of Tables, Figures, Exhibits, and Boxes
Preface
About 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 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