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9781506379708
A Stata® Companion to Political Analysis / Edition 4 available in Paperback
A Stata® Companion to Political Analysis / Edition 4
by Philip H. Pollock, Barry Clayton Edwards
Philip H. Pollock
- ISBN-10:
- 1506379702
- ISBN-13:
- 9781506379708
- Pub. Date:
- 10/27/2018
- Publisher:
- SAGE Publications
- ISBN-10:
- 1506379702
- ISBN-13:
- 9781506379708
- Pub. Date:
- 10/27/2018
- Publisher:
- SAGE Publications
A Stata® Companion to Political Analysis / Edition 4
by Philip H. Pollock, Barry Clayton Edwards
Philip H. Pollock
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Overview
Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Philip Pollock′s Fourth Edition of A Stata(R) Companion to Political Analysis, students quickly learn Stata via step-by-step instruction, more than 50 exercises, customized datasets, annotated screen shots, boxes that highlight Stata′s special capabilities, and guidance on using Stata to read raw data. This attractive and value-priced workbook, an ideal complement to Pollock's Essentials of Political Analysis, is a must-have for any political science student working with Stata.
Product Details
ISBN-13: | 9781506379708 |
---|---|
Publisher: | SAGE Publications |
Publication date: | 10/27/2018 |
Edition description: | Fourth Edition |
Pages: | 288 |
Sales rank: | 542,591 |
Product dimensions: | 8.50(w) x 11.00(h) x (d) |
About the Author
Philip H. Pollock III is a professor of political science at the University of Central Florida. He has taught courses in research methods at the undergraduate and graduate levels for more than thirty years. His main research interests are American public opinion, voting behavior, techniques of quantitative analysis, and the scholarship of teaching and learning. His recent research has been on the effectiveness of Internet-based instruction. Pollock's research has appeared in the American Journal of Political Science, Social Science Quarterly, and the British Journal of Political Science. Recent scholarly publications include articles in Political Research Quarterly, the Journal of Political Science Education, and PS: Political Science and Politics.
Barry C. Edwards is a lecturer in the Department of Political Science at the University of Central Florida. He received his B.A. from Stanford University, a J.D. from New York University, and a Ph.D. from the University of Georgia. His teaching and research interests include American politics, public law, and research methods. He founded the Political Science Data Group and created the PoliSciData.com web site. His research has been published in American Politics Research, Congress & the Presidency, Election Law Journal, Emory Law Journal, Georgia Bar Journal, Harvard Negotiation Law Review, Journal of Politics, NYU Journal of Legislation and Public Policy, Political Research Quarterly, Presidential Studies Quarterly, Public Management Review, and State Politics and Policy Quarterly.
Barry C. Edwards is a lecturer in the Department of Political Science at the University of Central Florida. He received his B.A. from Stanford University, a J.D. from New York University, and a Ph.D. from the University of Georgia. His teaching and research interests include American politics, public law, and research methods. He founded the Political Science Data Group and created the PoliSciData.com web site. His research has been published in American Politics Research, Congress & the Presidency, Election Law Journal, Emory Law Journal, Georgia Bar Journal, Harvard Negotiation Law Review, Journal of Politics, NYU Journal of Legislation and Public Policy, Political Research Quarterly, Presidential Studies Quarterly, Public Management Review, and State Politics and Policy Quarterly.
Table of Contents
Figures and TablesPrefaceIntroduction: Getting Started with StataI.1 Datasets for Stata CompanionI.2 A Quick Tour of StataI.3 Running Commands in StataI.4 Quick Access to Tutorials and ResourcesChapter 1 Using Stata for Data Analysis1.1 General Syntax of Stata Commands1.2 Using Stata’s Graphic User Interface Effectively1.3 Do-files1.4 Printing Results and Copying Output1.5 Customizing Your Display1.6 Log Files1.7 Getting HelpChapter 1 ExercisesChapter 2 Descriptive Statistics2.1 Identifying Levels of Measurement2.2 Describing Nominal VariablesA Closer Look: Weighted and Unweighted Analysis: What’s the Difference?2.3 Describing Ordinal Variables2.4 Bar Charts for Nominal and Ordinal Variables2.5 Describing Interval VariablesA Closer Look: Stata’s Graphics Editor2.6 Histograms for Interval Variables2.7 Obtaining Case-Level InformationChapter 2 ExercisesChapter 3 Transforming Variables3.1 Creating Dummy Variables3.2 Applying Math Operators to Variables3.3 Managing Variable Descriptions and Labels3.4 Collapsing Variables into Simplified Categories3.5 Centering or Standardizing a Numeric Variable3.6 Creating an Additive IndexChapter 3 ExercisesChapter 4 Making Comparisons4.1 Cross-Tabulation AnalysisA Closer Look: The replace Command4.2 Mean Comparison AnalysisA Closer Look: The format Command4.3 Making Comparisons with Interval-Level Independent VariablesChapter 4 ExercisesChapter 5 Graphing Relationships and Describing Patterns5.1 Graphs for Binary Dependent Variables5.1.1 Simple Bar Charts with Nominal-Level Independent Variables5.1.2 Area Chart with Ordinal-Level Independent Variables5.1.3 Graphs with Interval-Level Independent Variables5.2 Graphs for Nominal-Level Dependent Variables5.2.1 Clustered Bar Charts with Nominal-Level Independent Variables5.2.2 Multiple Line Plots with Ordinal-Level Independent Variables5.2.3 Graphs with Interval-Level Independent Variables5.3 Graphs for Ordinal-Level Dependent Variables5.3.1 Using Bars to Represent Select Values5.3.2 Stacked Bar Chart for Ordinal-Ordinal Relationship5.3.3 Graphs with Interval-Level Independent Variables5.4 Graphs for Interval-Level Dependent Variables5.4.1 Plotting Means with Bars or Lines5.4.2 Box Plots5.4.3 ScatterplotsChapter 5 ExercisesChapter 6 Random Assignment and Sampling6.1 Random Assignment6.1.1 Two Groups with Equal Probability6.1.2 Multiple Groups with Varying Probabilities6.1.3 Random Assignment to Predetermined-Size Groups6.2 Analyzing the Results of an Experiment6.2.1 Assessing Random Assignment6.2.2 Evaluating the Effect of Treatment6.3 Random Sampling6.3.1 Simple Random Sampling with Replacement6.3.2 Simple Random Sampling without Replacement6.3.3 Systematic Random Samples6.3.4 Clustered and Stratified Random Samples6.4 Selecting Cases for Qualitative Analysis6.4.1 Most Similar Systems6.4.2 Most Different Systems6.5 Analyzing Data Ethically6.5.1 Ethical Issues in Data Analysis6.5.2 Ten Tips for Writing Replication CodeChapter 6 ExercisesChapter 7 Making Controlled Comparisons7.1 Cross-Tabulation Analysis with a Control Variable7.1.1 Start with a Basic Cross-Tabulation7.1.2 Controlling for Another Variable7.1.3 Interpreting Controlled Cross-TabulationsA Closer Look: The If Qualifier7.2 Visualizing Controlled Comparisons with Categorical Dependent Variables7.3 Mean Comparison Analysis with a Control Variable7.3.1 Start with Basic Mean Comparison Table7.3.2 Adding Control Variables7.3.3 Interpreting a Controlled Mean Comparison7.4 Visualizing Controlled Mean ComparisonsChapter 7 ExercisesChapter 8 Foundations of Inference8.1 Estimating Population Parameters with Simulations8.2 Expected Shape of Sampling Distributions8.2.1 Central Limit Theorem and the Normal Distribution8.2.2 Normal Distribution of Sample Proportions8.2.3 Normal Distribution of Sample Means8.2.4 The Standard Normal Distribution8.2.5 The Empirical Rule (68-95-99 Rule)8.3 Confidence Interval and Margins of Error8.3.1 Critical Values for Confidence Intervals8.3.2 Reporting the Confidence Interval for a Sample Proportions8.3.2 Reporting the Confidence Interval for a Sample Means8.4 Student’s t-Distribution: When You’re Not Completely Normal8.4.1 The t-Distribution’s Role in Inferential Statistics8.4.1 Critical Values of t-DistributionsChapter 8 ExercisesChapter 9 Hypothesis Tests with One and Two Samples9.1 Role of the Null Hypothesis9.2 Testing Hypotheses with Sample Proportions9.2.1 Testing One Sample Proportion Against Hypothesized Value9.2.2 Testing Difference Between Two Sample Proportions Using Groups9.2.3 Testing Difference Between Two Sample Proportions Using Variables9.2.4 Testing Hypotheses about Proportions with Weighted Data9.3 Testing Hypotheses with Sample Means9.3.1 Testing One Sample Mean Against Hypothesized Value9.3.2 Testing the Difference Between Two Sample Means Using Groups9.3.3 Testing the Difference Between Two Sample Means Using Variables9.3.4 T-Test Variations from Assumptions about Variance9.3.5 Testing Hypotheses about Means with Weighted DataChapter 9 ExercisesChapter 10 Chi-Square Test and Analysis of Variance10.1 The Chi-Square Test of Independence10.1.1 How the Chi-Square Test Works10.1.2 Conducting a Chi-Square Test10.1.3 Example with Nominal-Level Independent VariableA Closer Look: Chi-Square Test with Weighted Data10.1.4 Reporting and Interpreting ResultsA Closer Look: Other Applications of Chi-Square Tests10.2 Measuring the Strength of Association between Categorical Variables10.2.1 Lambda10.2.2 Somers’ D10.2.3 Cramer’s V10.3 Chi-Square Test and Measures of Association in Controlled Comparisons10.3.1 Analyzing an Ordinal-Level Relationship with a Control Variable10.3.2 Analyzing a Nominal-Level Relationship with a Control Variable (and Weighted Observations)10.4 Analysis of Variance (ANOVA)10.4.1 How ANOVA Works10.4.2 Single Factor ANOVA10.4.3 Two Factor ANOVA10.4.4 Stata’s F-Distribution FunctionsChapter 10 ExercisesChapter 11 Correlation and Bivariate Regression11.1 Correlation Analysis11.1.1 Correlation between Two Variables11.1.2 Correlation Among More than Two VariablesA Closer Look: Other Types and Application of Correlation Analysis11.2 Bivariate Regression AnalysisA Closer Look: Treating Census as a SampleA Closer Look: R-Squared and Adjusted R-Squared: What’s the Difference?11.3 Creating a Scatterplot with a Linear Prediction LineA Closer Look: Creating Graphs with Multiple Layered ElementsA Closer Look: What If a Scatterplot Doesn’t Show a Linear Relationship?11.4 Correlation and Bivariate Regression Analysis with Weighted DataA Closer Look: Creating Tables of Regression ResultsChapter 11 ExercisesChapter 12 Multiple Regression12.1 Multiple Regression Analysis12.1.1 Estimating and Interpreting a Multiple Regression Model12.1.2 Visualizing Multiple Regression with Bubble Plots12.1.3 Multiple Regression with Weighted Observations12.2 Regression with Multiple Dummy Variables12.2.1 Estimating and Interpreting Regression with Multiple Dummy Variables12.2.2 Changing the Reference Category12.2.3 Visualizing Regression with Multiple Dummy Variables12.3 Interaction Effects in Multiple Regression12.3.1 Estimating Regression Model with Interaction Term12.3.2 Graphing Linear Prediction Lines for Interaction RelationshipsChapter 12 ExercisesChapter 13 Analyzing Regression Residuals13.1 Expected Values, Observed Values, and Regression Residuals13.1.1 Example from Bivariate Regression Analysis13.1.2 Residuals from Multiple Regression Analysis13.2 Squared and Standarized Residuals13.2.1 Squared Residuals13.2.2 Standardized Residuals13.3 Assumptions about Regression Residuals13.4 Analyzing Graphs of Regression Residuals13.4.1 Histogram of Regression Residuals13.4.2 Residual Diagnostic Plots13.5 Testing Regression Assumptions with Residuals13.5.1 Testing Assumption that Residuals are Normally Distributed13.5.2 Testing the Constant Variance Assumption15.3.3 Regression Diagnostics for Multiple Regression AnalysisA Closer Look: Other Regression Diagnostic Tests13.6 What If You Diagnose Problems with Residuals?Chapter 13 ExercisesChapter 14 Logistic Regression14.1 Odds, Logged Odds, and Probabilities14.2 Estimating Logistic Regression Models14.2.1 Logistic Regression with One Independent Variable14.2.2 Reporting and Interpreting Odds Ratios14.2.3 Evaluating Model FitA Closer Look: Logistic Regression Analysis with Weighted Observations14.3 Logistic Regression with Multiple Independent Variables14.4 Graphing Predicted Probabilities with One Independent Variable14.4.1 Interval-Level Independent Variable14.4.2 Categorical Independent VariableA Closer Look: Marginal Effects and Expected Changes in Probability14.5 Graphing Predicted Probabilities with Multiple Independent Variables14.5.1 One Categorical and One Interval-Level Independent Variable14.5.2 Two Categorical Independent VariablesA Closer Look: Stata’s Quiet Mode14.5.3 Plotting Predicted Probabilities with atmeans Option14.5.4 Combining atmeans and over OptionsChapter 14 ExercisesChapter 15 Doing Your Own Political Analysis15.1 Doable Research Ideas15.1.1 Political Knowledge and Interest15.1.2 Self-Interest and Policy Preferences15.1.3 Economic Performance and Election Outcomes15.1.4 Electoral Turnout in Comparative Perspective15.1.5 Correlates of State Policies15.1.6 Religion and Politics15.1.7 Race and Politics15.2 Getting Data into Stata15.2.1 Opening Stata Formatted Datasets15.2.2 Importing Microsoft Excel Datasets15.2.3 Using HTML Table Data15.2.4 Entering Data with Stata’s Data Editor15.3 Writing It Up15.3.1 The Research Question15.3.2 Previous Research15.3.3 Data, Hypotheses, and Analysis15.3.4 Conclusions and ImplicationsChapter 15 ExercisesAppendixTable A-1: Variables in the Debate Dataset in Alphabetical OrderTable A-2: Variables in the GSS Dataset in Alphabetical OrderTable A-3: Variables in the NES Dataset in Alphabetical OrderTable A-4: Variables in the States Dataset by TopicTable A-5: Variables in the World Dataset by TopicFrom the B&N Reads Blog
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