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.Make Your Theories Causal -- 1.5.2.Don't Let Data Alone Drive Your Theories -- 1.5.3.Consider Only Empirical Evidence -- 1.5.4.Avoid Normative Statements -- 1.5.5.Pursue Both Generality and Parsimony -- 1.6.A Quick Look Ahead -- Concepts Introduced in This Chapter -- Exercises -- 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.Time-Series Example -- 2.3.2.Cross-Sectional 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.Examine Previous Research -- 2.5.1.What Did the Previous Researchers Miss? -- 2.5.2.Can Their Theory Be Applied Elsewhere? -- 2.5.3.If We Believe Their Findings, Are There Further Implications? -- 2.5.4.How Might This Theory Work at Different Levels of Aggregation (Micro Macro)? -- 2.6.Think Formally about the Causes That Lead to Variation in Your Dependent Variable -- 2.6.1.Utility and Expected Utility -- 2.6.2.The Puzzle of Turnout -- 2.7.Think about the Institutions: The Rules Usually Matter -- 2.7.1.Legislative Rules -- 2.7.2.The Rules Matter! -- 2.8.Extensions -- 2.9.How Do I Know If I Have a "Good" Theory? -- 2.9.1.Does Your Theory Offer an Answer to an Interesting Research Question? -- 2.9.2.Is Your Theory Causal? -- 2.9.3.Can You Test Your Theory on Data That You Have Not Yet Observed? -- 2.9.4.How General Is Your Theory? -- 2.9.5.How Parsimonious Is Your Theory? -- 2.9.6.How New Is Your Theory?^^^^

2.9.7.How Nonobvious Is Your Theory? -- 2.10.Conclusion -- Concepts Introduced in This Chapter -- Exercises -- 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 -- Overview -- 4.1.Comparison as the Key to Establishing Causal Relationships -- 4.2.Experimental Research Designs -- 4.2.1."Random Assignment" versus "Random Sampling^^^^^

4.2.2.Varieties of Experiments and Near-Experiments -- 4.2.3.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.Summary -- Concepts Introduced in This Chapter -- Exercises -- Overview -- 5.1.Getting to Know Your Data -- 5.2.Social Science Measurement: The Varying Challenges of Quantifying Humanity -- 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.Getting to Know Your Data Statistically -- 5.8.What Is the Variable's Measurement Metric?^^^^

5.8.1.Categorical Variables -- 5.8.2.Ordinal Variables -- 5.8.3.Continuous Variables -- 5.8.4.Variable Types and Statistical Analyses -- 5.9.Describing Categorical Variables -- 5.10.Describing Continuous Variables -- 5.10.1.Rank Statistics -- 5.10.2.Moments -- 5.11.Limitations of Descriptive Statistics and Graphs -- 5.12.Conclusions -- Concepts Introduced in This Chapter -- Exercises -- Overview -- 6.1.Populations and Samples -- 6.2.Some Basics of Probability Theory -- 6.3.Learning about the Population from a Sample: The Central Limit Theorem -- 6.3.1.The Normal Distribution -- 6.4.Example: Presidential Approval Ratings -- 6.4.1.What Kind of Sample Was That? -- 6.4.2.A Note on the Effects of Sample Size -- 6.5.A Look Ahead: Examining Relationships between Variables -- Concepts Introduced in This Chapter -- Exercises -- Overview -- 7.1.Bivariate Hypothesis Tests and Establishing Causal Relationships -- 7.2.Choosing the Right Bivariate Hypothesis Test -- 7.3.All Roads Lead to p^^^^

7.3.1.The Logic of p-Values -- 7.3.2.The Limitations of p-Values -- 7.3.3.From p-Values to Statistical Significance -- 7.3.4.The Null Hypothesis and p-Values -- 7.4.Three Bivariate Hypothesis Tests -- 7.4.1.Example 1: Tabular Analysis -- 7.4.2.Example 2: Difference-of Means -- 7.4.3.Example 3: Correlation Coefficient -- 7.5.Wrapping Up -- Concepts Introduced in This Chapter -- Exercises -- Overview -- 8.1.Two-Variable Regression -- 8.2.Fitting a Line: Population q Sample -- 8.3.Which Line Fits Best? Estimating the Regression Line -- 8.4.Measuring Our Uncertainty about the OLS Regression Line -- 8.4.1.Goodness-of-Fit: Root Mean-Squared Error -- 8.4.2.Goodness-of-Fit: R-Squared Statistic -- 8.4.3.Is That a "Good" Goodness-of-Fit? -- 8.4.4.Uncertainty about Individual Components of the Sample Regression Model -- 8.4.5.Confidence Intervals about Parameter Estimates -- 8.4.6.Two-Tailed Hypothesis Tests^^^^

8.4.7.The Relationship between Confidence Intervals and Two-Tailed Hypothesis Tests -- 8.4.8.One-Tailed Hypothesis Tests -- 8.5.Assumptions, More Assumptions, and Minimal Mathematical Requirements -- 8.5.1.Assumptions about the Population Stochastic Component -- 8.5.2.Assumptions about Our Model Specification -- 8.5.3.Minimal Mathematical Requirements -- 8.5.4.How Can We Make All of These Assumptions? -- Concepts Introduced in This Chapter -- Exercises -- Overview -- 9.1.Modeling Multivariate Reality -- 9.2.The Population Regression Function -- 9.3.From Two-Variable to Multiple Regression -- 9.4.Interpreting Multiple Regression -- 9.5.Which Effect Is "Biggest"? -- 9.6.Statistical and Substantive Significance -- 9.7.What Happens When We Fail to Control for Z? -- 9.7.1.An Additional Minimal Mathematical Requirement in Multiple Regression -- 9.8.An Example from the Literature: Competing Theories of How Politics Affects International Trade -- 9.9.Implications^^^^

Concepts Introduced in This Chapter -- Exercises -- Overview -- 10.1.Extensions of OLS -- 10.2.Being Smart with Dummy Independent Variables in OLS -- 10.2.1.Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with Only Two Values -- 10.2.2.Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with More Than Two Values -- 10.2.3.Using Dummy Variables to Test Hypotheses about Multiple Independent Variables -- 10.3.Testing Interactive Hypotheses with Dummy Variables -- 10.4.Outliers and Influential Cases in OLS -- 10.4.1.Identifying Influential Cases -- 10.4.2.Dealing with Influential Cases -- 10.5.Multicollinearity -- 10.5.1.How Does Multicollinearity Happen? -- 10.5.2.Detecting Multicollinearity -- 10.5.3.Multicollinearity: A Simulated Example -- 10.5.4.Multicollinearity: A Real-World Example -- 10.5.5.Multicollinearity: What Should I Do? -- 10.6.Wrapping Up -- Concepts Introduced in This Chapter -- Exercises -- Overview^^^^

11.1.Extensions of OLS -- 11.2.Dummy Dependent Variables -- 11.2.1.The Linear Probability Model -- 11.2.2.Binomial Logit and Binomial Probit -- 11.2.3.Goodness-of-Fit with Dummy Dependent Variables -- 11.3.Being Careful with Time Series -- 11.3.1.Time-Series Notation -- 11.3.2.Memory and Lags in Time-Series Analysis -- 11.3.3.Trends and the Spurious Regression Problem -- 11.3.4.The Differenced Dependent Variable -- 11.3.5.The Lagged Dependent Variable -- 11.4.Example: The Economy and Presidential Popularity -- 11.5.Wrapping Up -- Concepts Introduced in This Chapter -- Exercises -- Overview -- 12.1.Two Routes toward a New Scientific Project -- 12.1.1.Project Type 1: A New Y (and Some X) -- 12.1.2.Project Type 2: An Existing Y and a New X -- 12.1.3.Variants on the Two Project Types -- 12.2.Using the Literature without Getting Buried in It -- 12.2.1.Identifying the Important Work on a Subject -- Using Citation Counts^^^^

12.2.2.Oh No! Someone Else Has Already Done What I Was Planning to Do. What Do I Do Now? -- 12.2.3.Dissecting the Research by Other Scholars -- 12.2.4.Read Effectively to Write Effectively -- 12.3.Writing Effectively about Your Research -- 12.3.1.Write Early, Write Often because Writing Is Thinking -- 12.3.2.Document Your Code -- Writing and Thinking while You Compute -- 12.3.3.Divide and Conquer -- a Section-by-Section Strategy for Building Your Project -- 12.3.4.Proofread, Proofread, and Then Proofread Again -- 12.4.Making Effective Use of Tables and Figures -- 12.4.1.Constructing Regression Tables -- 12.4.2.Writing about Regression Tables -- 12.4.3.Other Types of Tables and Figures -- Exercises.