The Fundamentals Of Political Science Research

The third edition of the best-selling The Fundamentals of Political Science Research provides an introduction to the scientific study of politics. It offers the basic tools necessary for readers to become both critical consumers and beginning producers of scientific research on politics. The authors present an integrated approach to research design and empirical analyses whereby researchers can develop and test causal theories. They use examples from political science research that students will find interesting and inspiring, and that will help them understand key concepts. The book makes technical material accessible to students who might otherwise be intimidated by mathematical examples. This revised third edition features new 'Your Turn' boxes meant to engage students. The edition also has new sections added throughout the book to enhance the content's clarity and breadth of coverage.; Provided by Publisher 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 Example 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 4.5.Summary 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 Era 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 o-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. 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 [lambda] link function for binomial logit models -- Appendix D. The [phi] link function for binomial probit models.