Overview

The Fifth Edition of A Stata (R) Companion to Political Analysis by Philip H. Pollock III and Barry C. Edwards teaches your students statistics by analyzing research-quality data in Stata. It follows the structure of Essentials of Political Analysis with software instructions, explanations of tests, and many exercises for practice.

Full Product Details

Author:   Philip H. Pollock (University of Central Florida, USA) ,  Barry Clayton Edwards (University of Central Florida, USA)
Publisher:   SAGE Publications Inc
Imprint:   CQ Press
Edition:   5th Revised edition
Weight:   1.110kg
ISBN:  

9781071815045

ISBN 10:   1071815040
Pages:   400
Publication Date:   18 October 2023
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

Figures and Tables Preface Introduction: Getting Started with Stata I.1 Datasets for Stata Companion I.2 A Quick Tour of Stata I.3 Running Commands in Stata I.4 Quick Access to Tutorials and Resources Chapter 1 Using Stata for Data Analysis 1.1 General Syntax of Stata Commands 1.2 Using Stata’s Graphic User Interface Effectively 1.3 Do-files 1.4 Printing Results and Copying Output 1.5 Customizing Your Display 1.6 Log Files 1.7 Getting Help Chapter 1 Exercises Chapter 2 Descriptive Statistics 2.1 Identifying Levels of Measurement 2.2 Describing Nominal Variables A Closer Look: Weighted and Unweighted Analysis: What’s the Difference? 2.3 Describing Ordinal Variables 2.4 Bar Charts for Nominal and Ordinal Variables 2.5 Describing Interval Variables A Closer Look: Stata’s Graphics Editor 2.6 Histograms for Interval Variables 2.7 Obtaining Case-Level Information Chapter 2 Exercises Chapter 3 Transforming Variables 3.1 Creating Dummy Variables 3.2 Applying Math Operators to Variables 3.3 Managing Variable Descriptions and Labels 3.4 Collapsing Variables into Simplified Categories 3.5 Centering or Standardizing a Numeric Variable 3.6 Creating an Additive Index Chapter 3 Exercises Chapter 4 Making Comparisons 4.1 Cross-Tabulation Analysis A Closer Look: The replace Command 4.2 Mean Comparison Analysis A Closer Look: The format Command 4.3 Making Comparisons with Interval-Level Independent Variables Chapter 4 Exercises Chapter 5 Graphing Relationships and Describing Patterns 5.1 Graphs for Binary Dependent Variables 5.1.1 Simple Bar Charts with Nominal-Level Independent Variables 5.1.2 Area Chart with Ordinal-Level Independent Variables 5.1.3 Graphs with Interval-Level Independent Variables 5.2 Graphs for Nominal-Level Dependent Variables 5.2.1 Clustered Bar Charts with Nominal-Level Independent Variables 5.2.2 Multiple Line Plots with Ordinal-Level Independent Variables 5.2.3 Graphs with Interval-Level Independent Variables 5.3 Graphs for Ordinal-Level Dependent Variables 5.3.1 Using Bars to Represent Select Values 5.3.2 Stacked Bar Chart for Ordinal-Ordinal Relationship 5.3.3 Graphs with Interval-Level Independent Variables 5.4 Graphs for Interval-Level Dependent Variables 5.4.1 Plotting Means with Bars or Lines 5.4.2 Box Plots 5.4.3 Scatterplots Chapter 5 Exercises Chapter 6 Random Assignment and Sampling 6.1 Random Assignment 6.1.1 Two Groups with Equal Probability 6.1.2 Multiple Groups with Varying Probabilities 6.1.3 Random Assignment to Predetermined-Size Groups 6.2 Analyzing the Results of an Experiment 6.2.1 Assessing Random Assignment 6.2.2 Evaluating the Effect of Treatment 6.3 Random Sampling 6.3.1 Simple Random Sampling with Replacement 6.3.2 Simple Random Sampling without Replacement 6.3.3 Systematic Random Samples 6.3.4 Clustered and Stratified Random Samples 6.4 Selecting Cases for Qualitative Analysis 6.4.1 Most Similar Systems 6.4.2 Most Different Systems 6.5 Analyzing Data Ethically 6.5.1 Ethical Issues in Data Analysis 6.5.2 Ten Tips for Writing Replication Code Chapter 6 Exercises Chapter 7 Making Controlled Comparisons 7.1 Cross-Tabulation Analysis with a Control Variable 7.1.1 Start with a Basic Cross-Tabulation 7.1.2 Controlling for Another Variable 7.1.3 Interpreting Controlled Cross-Tabulations A Closer Look: The If Qualifier 7.2 Visualizing Controlled Comparisons with Categorical Dependent Variables 7.3 Mean Comparison Analysis with a Control Variable 7.3.1 Start with Basic Mean Comparison Table 7.3.2 Adding Control Variables 7.3.3 Interpreting a Controlled Mean Comparison 7.4 Visualizing Controlled Mean Comparisons Chapter 7 Exercises Chapter 8 Foundations of Inference 8.1 Estimating Population Parameters with Simulations 8.2 Expected Shape of Sampling Distributions 8.2.1 Central Limit Theorem and the Normal Distribution 8.2.2 Normal Distribution of Sample Proportions 8.2.3 Normal Distribution of Sample Means 8.2.4 The Standard Normal Distribution 8.2.5 The Empirical Rule (68-95-99 Rule) 8.3 Confidence Interval and Margins of Error 8.3.1 Critical Values for Confidence Intervals 8.3.2 Reporting the Confidence Interval for a Sample Proportions 8.3.2 Reporting the Confidence Interval for a Sample Means 8.4 Student’s t-Distribution: When You’re Not Completely Normal 8.4.1 The t-Distribution’s Role in Inferential Statistics 8.4.1 Critical Values of t-Distributions Chapter 8 Exercises Chapter 9 Hypothesis Tests with One and Two Samples 9.1 Role of the Null Hypothesis 9.2 Testing Hypotheses with Sample Proportions 9.2.1 Testing One Sample Proportion Against Hypothesized Value 9.2.2 Testing Difference Between Two Sample Proportions Using Groups 9.2.3 Testing Difference Between Two Sample Proportions Using Variables 9.2.4 Testing Hypotheses about Proportions with Weighted Data 9.3 Testing Hypotheses with Sample Means 9.3.1 Testing One Sample Mean Against Hypothesized Value 9.3.2 Testing the Difference Between Two Sample Means Using Groups 9.3.3 Testing the Difference Between Two Sample Means Using Variables 9.3.4 T-Test Variations from Assumptions about Variance 9.3.5 Testing Hypotheses about Means with Weighted Data Chapter 9 Exercises Chapter 10 Chi-Square Test and Analysis of Variance 10.1 The Chi-Square Test of Independence 10.1.1 How the Chi-Square Test Works 10.1.2 Conducting a Chi-Square Test 10.1.3 Example with Nominal-Level Independent Variable A Closer Look: Chi-Square Test with Weighted Data 10.1.4 Reporting and Interpreting Results A Closer Look: Other Applications of Chi-Square Tests 10.2 Measuring the Strength of Association between Categorical Variables 10.2.1 Lambda 10.2.2 Somers’ D 10.2.3 Cramer’s V 10.3 Chi-Square Test and Measures of Association in Controlled Comparisons 10.3.1 Analyzing an Ordinal-Level Relationship with a Control Variable 10.3.2 Analyzing a Nominal-Level Relationship with a Control Variable (and Weighted Observations) 10.4 Analysis of Variance (ANOVA) 10.4.1 How ANOVA Works 10.4.2 Single Factor ANOVA 10.4.3 Two Factor ANOVA 10.4.4 Stata’s F-Distribution Functions Chapter 10 Exercises Chapter 11 Correlation and Bivariate Regression 11.1 Correlation Analysis 11.1.1 Correlation between Two Variables 11.1.2 Correlation Among More than Two Variables A Closer Look: Other Types and Application of Correlation Analysis 11.2 Bivariate Regression Analysis A Closer Look: Treating Census as a Sample A Closer Look: R-Squared and Adjusted R-Squared: What’s the Difference? 11.3 Creating a Scatterplot with a Linear Prediction Line A Closer Look: Creating Graphs with Multiple Layered Elements A Closer Look: What If a Scatterplot Doesn’t Show a Linear Relationship? 11.4 Correlation and Bivariate Regression Analysis with Weighted Data A Closer Look: Creating Tables of Regression Results Chapter 11 Exercises Chapter 12 Multiple Regression 12.1 Multiple Regression Analysis 12.1.1 Estimating and Interpreting a Multiple Regression Model 12.1.2 Visualizing Multiple Regression with Bubble Plots 12.1.3 Multiple Regression with Weighted Observations 12.2 Regression with Multiple Dummy Variables 12.2.1 Estimating and Interpreting Regression with Multiple Dummy Variables 12.2.2 Changing the Reference Category 12.2.3 Visualizing Regression with Multiple Dummy Variables 12.3 Interaction Effects in Multiple Regression 12.3.1 Estimating Regression Model with Interaction Term 12.3.2 Graphing Linear Prediction Lines for Interaction Relationships Chapter 12 Exercises Chapter 13 Analyzing Regression Residuals 13.1 Expected Values, Observed Values, and Regression Residuals 13.1.1 Example from Bivariate Regression Analysis 13.1.2 Residuals from Multiple Regression Analysis 13.2 Squared and Standarized Residuals 13.2.1 Squared Residuals 13.2.2 Standardized Residuals 13.3 Assumptions about Regression Residuals 13.4 Analyzing Graphs of Regression Residuals 13.4.1 Histogram of Regression Residuals 13.4.2 Residual Diagnostic Plots 13.5 Testing Regression Assumptions with Residuals 13.5.1 Testing Assumption that Residuals are Normally Distributed 13.5.2 Testing the Constant Variance Assumption 15.3.3 Regression Diagnostics for Multiple Regression Analysis A Closer Look: Other Regression Diagnostic Tests 13.6 What If You Diagnose Problems with Residuals? Chapter 13 Exercises Chapter 14 Logistic Regression 14.1 Odds, Logged Odds, and Probabilities 14.2 Estimating Logistic Regression Models 14.2.1 Logistic Regression with One Independent Variable 14.2.2 Reporting and Interpreting Odds Ratios 14.2.3 Evaluating Model Fit A Closer Look: Logistic Regression Analysis with Weighted Observations 14.3 Logistic Regression with Multiple Independent Variables 14.4 Graphing Predicted Probabilities with One Independent Variable 14.4.1 Interval-Level Independent Variable 14.4.2 Categorical Independent Variable A Closer Look: Marginal Effects and Expected Changes in Probability 14.5 Graphing Predicted Probabilities with Multiple Independent Variables 14.5.1 One Categorical and One Interval-Level Independent Variable 14.5.2 Two Categorical Independent Variables A Closer Look: Stata’s Quiet Mode 14.5.3 Plotting Predicted Probabilities with atmeans Option 14.5.4 Combining atmeans and over Options Chapter 14 Exercises Chapter 15 Doing Your Own Political Analysis 15.1 Doable Research Ideas 15.1.1 Political Knowledge and Interest 15.1.2 Self-Interest and Policy Preferences 15.1.3 Economic Performance and Election Outcomes 15.1.4 Electoral Turnout in Comparative Perspective 15.1.5 Correlates of State Policies 15.1.6 Religion and Politics 15.1.7 Race and Politics 15.2 Getting Data into Stata 15.2.1 Opening Stata Formatted Datasets 15.2.2 Importing Microsoft Excel Datasets 15.2.3 Using HTML Table Data 15.2.4 Entering Data with Stata’s Data Editor 15.3 Writing It Up 15.3.1 The Research Question 15.3.2 Previous Research 15.3.3 Data, Hypotheses, and Analysis 15.3.4 Conclusions and Implications Chapter 15 Exercises Appendix Table A-1: Variables in the Debate Dataset in Alphabetical Order Table A-2: Variables in the GSS Dataset in Alphabetical Order Table A-3: Variables in the NES Dataset in Alphabetical Order Table A-4: Variables in the States Dataset by Topic Table A-5: Variables in the World Dataset by Topic

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Author Information

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 writes textbooks and works for Fair Trial Analysis, LLC, a company that conducts research on juries and jurors for civil and criminal litigation. He received his B.A. from Stanford University, a J.D. from New York University, and a Ph.D. from the University of Georgia. He taught survey design and analysis, research methods, and prelaw courses at the University of Central Florida and continues to teach occasional courses for the University of Georgia. His political science interests include American politics, public law, and research methods. He founded the Political Science Data Group and created the PoliSciData.com website. 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, State Politics and Policy Quarterly, and UCLA Criminal Justice Law Review.

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