List of Figures

List of Tables

Preface to the Third Edition

Acknowledgments to the Third Edition

Acknowledgments to the Second Edition

Acknowledgments to the First Edition

**1 The Scientific Study of Politics**

Overview

1.1 Political

*Science*?

1.2 Approaching Politics Scientifically: the Search for Causal Explanations

1.3 Thinking about the World in Terms of Variables and Causal Explanations

1.4 Models of Politics

1.5 Rules of the Road to Scientific Knowledge about Politics

1.5.1 Focus on Causality

1.5.2 Don't Let Data Alone Drive Your Theories

1.5.3 Consider Only Empirical Evidence

1.5.4 Check Your Ideology at the Door and Avoid Normative Statements

1.5.5 Pursue Both Generality and Parsimony

1.6 A Quick Look Ahead

Concepts Introduced in This Chapter

Exercises

**2 The Art of Theory Building**

Overview

2.1 Good Theories Come from Good Theory-Building Strategies

2.2 Promising Theories Offer Answers to Interesting Research Questions

2.3 Identifying Interesting Variation

2.3.1 Cross-Sectional Examples

2.3.2 Time-Series Example

2.4 Learning to Use Your Knowledge

2.4.1 Moving from a Specific Event to More General Theories

2.4.2 Know Local, Think Global: Can You Drop the Proper Nouns?

2.5 Three Strategies toward Developing an Original Theory

2.5.1 Theory Type 1: a New *Y* (and Some *X*)

2.5.2 Project Type 2: an Existing *Y* and a New X

2.5.3 A New *Z* which Modifies an Established *X* → *Y*?

2.6 Using the Literature without Getting Buried in It

2.6.1 Identifying the Important Work on a Subject — Using Citation Counts

2.6.2 Oh No! Someone Else Has Already Done What I Was Planning to Do. What Do I Do Now?

2.6.3 Critically Examining Previous Research to Develop an Original Theory

2.7 Think Formally about the Causes that Lead to Variation in Your Dependent Variable

2.7.1 Utility and Expected Utility

2.7.2 The Puzzle of Turnout

2.8 Think about the Institutions: the Rules Usually Matter

2.8.1 Legislative Rules

2.8.2 The Rules Matter!

2.8.3 Extensions

2.9 Conclusion

Concepts Introduced in This Chapter

Exercises

**3 Evaluating Causal Relationships**

Overview

3.1 Causality and Everyday Language

3.2 Four Hurdles along the Route to Establishing Causal Relationships

3.2.1 Putting It All Together — Adding Up the Answers to Our Four Questions

3.2.2 Identifying Causal Claims Is an Essential Thinking Skill

3.2.3 What Are the Consequences of Failing to Control for Other Possible Causes?

3.3 Why Is Studying Causality So Important? Three Examples from Political Science

3.3.1 Life Satisfaction and Democratic Stability

3.3.2 Race and Political Participation in the United States

3.3.3 Evaluating Whether "Head Start" Is Effective

3.4 Wrapping Up

Concepts Introduced in This Chapter

Exercises

**4 Research Design**

Overview

4.1 Comparison as the Key to Establishing Causal Relationships

4.2 Experimental Research Designs

4.2.1 Experimental Designs and the Four Causal Hurdles

4.2.2 "Random Assignment" versus "Random Sampling"

4.2.3 Varieties of Experiments and Near-Experiments

4.2.4 Are There Drawbacks to Experimental Research Designs?

4.3 Observational Studies (in Two Flavors)

4.3.1 Datum, Data, Data Set

4.3.2 Cross-Sectional Observational Studies

4.3.3 Time-Series Observational Studies

4.3.4 The Major Difficulty with Observational Studies

4.4 Dissecting the Research by Other Scholars

Concepts Introduced in This Chapter

Exercises

**5 Measuring Concepts of Interest**

Overview

5.1 Getting to Know Your Data

5.2 Social Science Measurement: the Varying Challenges of Quantifying Human Behavior

5.3 Problems in Measuring Concepts of Interest

5.3.1 Conceptual Clarity

5.3.2 Reliability

5.3.3 Measurement Bias and Reliability

5.3.4 Validity

5.3.5 The Relationship between Validity and Reliability

5.4 Controversy 1: Measuring Democracy

5.5 Controversy 2: Measuring Political Tolerance

5.6 Are There Consequences to Poor Measurement?

5.7 Conclusions

Concepts Introduced in This Chapter

Exercises

**6 Getting to Know Your Data**

Overview

6.1 Getting to Know Your Data Statistically

6.2 What Is the Variable's Measurement Metric?

6.2.1 Categorical Variables

6.2.2 Ordinal Variables

6.2.3 Continuous Variables

6.2.4 Variable Types and Statistical Analyses

6.3 Describing Categorical Variables

6.4 Describing Continuous Variables

6.4.1 Rank Statistics

6.4.2 Moments

6.5 Limitations of Descriptive Statistics and Graphs

6.6 Conclusions

Concepts Introduced in This Chapter

Exercises

**7 Probability and Statistical Inference**

Overview

7.1 Populations and Samples

7.2 Some Basics of Probability Theory

7.3 Learning about the Population from a Sample: the Central Limit Theorem

7.3.1 The Normal Distribution

7.4 Example: Presidential Approval Ratings

7.4.1 What Kind of Sample Was That?

7.4.2 Obtaining a Random Sample in the Cellphone Eras

7.4.3 A Note on the Effects of Sample Size

7.5 A Look Ahead: Examining Relationships between Variables

Concepts Introduced in This Chapter

Exercises

**8 Bivariate Hypothesis Testing**

Overview

8.1 Bivariate Hypothesis Tests and Establishing Causal Relationships

8.2 Choosing the Right Bivariate Hypothesis Test

8.3 All Roads Lead to

*p*
8.3.1 The Logic of *p*-Values

8.3.2 The Limitations of *p*-Values

8.3.3 From *p*-Values to Statistical Significance

8.3.4 The Null Hypothesis and *p*-Values

8.4 Three Bivariate Hypothesis Tests

8.4.1 Example 1: Tabular Analysis

8.4.2 Example 2: Difference of Means

8.4.3 Example 3: Correlation Coefficient

8.5 Wrapping Up

Concepts Introduced in This Chapter

Exercises

**9 Two-Variable Regression Models**

Overview

9.1 Two–Variable Regression

9.2 Fitting a Line: Population ⇔ Sample

9.3 Which Line Fits Best? Estimating the Regression Line

9.4 Measuring Our Uncertainty about the OLS Regression Line

9.4.1 Goodness-of-Fit: Root Mean-Squared Error

9.4.2 Goodness-of-Fit: *R*-Squared Statistic

9.4.3 Is That a "Good" Goodness-of-Fit?

9.4.4 Uncertainty about Individual Components of the Sample Regression Model

9.4.5 Confidence Intervals about Parameter Estimates

9.4.6 Two-Tailed Hypothesis Tests

9.4.7 The Relationship between Confidence Intervals and Two-Tailed Hypothesis Tests

9.4.8 One-Tailed Hypothesis Tests

9.5 Assumptions, More Assumptions, and Minimal Mathematical Requirements

9.5.1 Assumptions about the Population Stochastic Component

9.5.2 Assumptions about Our Model Specification

9.5.3 Minimal Mathematical Requirements

9.5.4 How Can We Make All of These Assumptions?

Concepts Introduced in This Chapter

Exercises

**10 Multiple Regression: the Basics**

Overview

10.1 Modeling Multivariate Reality

10.2 The Population Regression Function

10.3 From Two-Variable to Multiple Regression

10.4 Interpreting Multiple Regression

10.5 Which Effect Is "Biggest"?

10.6 Statistical and Substantive Significance

10.7 What Happens when We Fail to Control for

*Z*?

10.7.1 An Additional Minimal Mathematical Requirement in Multiple Regression

10.8 An Example from the Literature: Competing Theories of How Politics Affects International Trade

10.9 Making Effective Use of Tables and Figures

10.9.1 Constructing Regression Tables

10.9.2 Writing about Regression Tables

10.10 Implications and Conclusions

Concepts Introduced in This Chapter

Exercises

**11 Multiple Regression Model Specification**

Overview

11.1 Extensions of Ordinary Least-Squares

11.2 Being Smart with Dummy Independent Variables in OLS

11.2.1 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with Only Two Values

11.2.2 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with More Than Two Values

11.2.3 Using Dummy Variables to Test Hypotheses about Multiple Independent Variables

11.3 Testing Interactive Hypotheses with Dummy Variables

11.4 Outliers and Influential Cases in OLS

11.4.1 Identifying Influential Cases

11.4.2 Dealing with Influential Cases

11.5 Multicollinearity

11.5.1 How Does Multicollinearity Happen?

11.5.2 Detecting Multicollinearity

11.5.3 Multicollinearity: a Simulated Example

11.5.4 Multicollinearity: a Real-World Example

11.5.5 Multicollinearity: What Should I Do?

11.6 Wrapping Up

Concepts Introduced in This Chapter

Exercises

**12 Limited Dependent Variables and Time-Series Data**

Overview

12.1 Extensions of Ordinary Least Squares

12.2 Dummy Dependent Variables

12.2.1 The Linear Probability Model

12.2.2 Binomial Logit and Binomial Probit

12.2.3 Goodness-of-Fit with Dummy Dependent Variables

12.3 Being Careful with Time Series

12.3.1 Time-Series Notation

12.3.2 Memory and Lags in Time-Series Analysis

12.3.3 Trends and the Spurious Regression Problem

12.3.4 The Differenced Dependent Variable

12.3.5 The Lagged Dependent Variable

12.4 Example: the Economy and Presidential Popularity

12.5 Wrapping Up

Concepts Introduced in This Chapter

Exercises

**Appendix A. Critical Values of Chi-Squared**

**Appendix B. Critical Values of** *t*

**Appendix C. The Λ Link Function for Binomial Logit Models**

**
****Appendix D. The Φ Link Function for Binomial Probit Models**

**
**Bibliography

Index