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