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" The fundamentals of political science research / "
Paul M. Kellstedt, Texas A & M University, Guy D. Whitten Texas A & M University
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BL
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661236
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dltt
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| Main Entry
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Kellstedt, Paul M.,1968-
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| Title & Author
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The fundamentals of political science research /\ Paul M. Kellstedt, Texas A M University, Guy D. Whitten Texas A M University
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| Edition Statement
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Second edition
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| Page. NO
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xxiv, 316 pages :: illustrations ;; 26 cm
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| ISBN
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9781107621664
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: 1107621666
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| Bibliographies/Indexes
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Includes bibliographical references (pages 301-305) and index
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| Contents
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Machine generated contents note: 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.
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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
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| Subject
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Political science-- Research.
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| LC Classification
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JA86.K45 2013
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JA86.K45 2013
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| Added Entry
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Whitten, Guy D.,1965-
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