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An IBM® SPSS® Companion to Political Analysis
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An IBM® SPSS® Companion to Political Analysis

Seventh Edition
April 2025 | 440 pages | CQ Press
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In Phillip H. Pollock III and Barry C. Edwards' trusted An IBM® SPSS® Companion to Political Analysis workbook, students dive headfirst into actual political data and work with a software tool that prepares them not only for future political science research but the job world as well. Students learn by doing with new guided examples, annotated screenshots, step-by-step instructions, and exercises that reflect current scholarly debates in American political behavior and comparative politics. The Seventh Edition has been thoroughly revised to break up larger chapters for a more detailed and focused exploration of key topics. This edition has also been updated to reflect current datasets from the General Social Survey (GSS) and American National Election Studies (ANES), including new variables related to the 2020 presidential election, ensuring students are working with relevant and up-to-date political science data. Datasets are all compatible with all post-12 releases of SPSS.
 
Figures
 
Preface
 
Acknowledgements
 
About the Authors
 
Introduction: Getting Started with SPSS
I.1 Downloading the Datasets

 
I.2 SPSS Full and Student Versions: What’s the Difference?

 
I.3 Watch Screencasts from SAGE Edge

 
 
Chapter 1: Using SPSS for Data Analysis
1.1 The Data Editor

 
1.2 Setting Options for Variable Lists

 
1.3 The Viewer

 
1.4 Selecting, Printing, and Saving Output

 
1.5 How to Format an SPSS Table

 
1.6 Saving Commands in Syntax Files

 
1.7 Getting Help

 
1.8 Chapter Review

 
Chapter 1 Exercises

 
 
Chapter 2: Descriptive Statistics
2.1 How SPSS Stores Information about Variables

 
2.2 Identifying Levels of Measurement

 
2.3 Describing Nominal Variables

 
2.4 Describing Ordinal Variables

 
2.5 Describing Interval Variables

 
2.6 Using the Chart Editor to Modify Graphics

 
2.7 Obtaining Case-level Information with Case Summaries

 
2.8 Chapter Review

 
Chapter 2 Exercises

 
 
Chapter 3: Creating and Transforming Variables
3.1 Creating Indicator Variables

 
3.2 Working with Variable Labels

 
3.3 Recoding Interval-level Variables into Simplified Categories

 
3.4 Simplifying an Internal-level Variable with Visual Binning

 
3.5 Centering or Standardizing a Numeric Variable

 
3.6 Using Compute to Create an Additive Index

 
3.7 Chapter Review

 
Chapter 3 Exercises

 
Chapter 4: Making Comparisons

 
4.1 Cross-Tabulation Analysis

 
4.2 Visualizing Cross-Tabulation Analysis with a Bar Chart

 
4.3 Mean Comparison Analysis

 
4.4 Visualizing Mean Comparison Analysis with a Line Chart

 
4.5 Making Comparisons with Interval-Level Independent Variables

 
4.6 Chapter Review

 
Chapter 4 Exercises

 
 
Chapter 5: Graphing Relationships and Describing Patterns
5.1 Graphs for Binary Dependent Variables

 
5.2 Graphs for Nominal Dependent Variables

 
5.3 Graphs for Ordinal-Level Dependent Variables

 
5.4 Graphs for Interval-Level Dependent Variables

 
5.5 Chapter Review

 
Chapter 5 Exercises

 
 
Chapter 6: Random Assignment and Sampling
6.1 Random Assignment

 
6.2 Analyzing the Results of an Experiment

 
6.3 Random Sampling

 
6.4 Selecting Cases for Qualitative Analysis

 
6.5 Analyzing Data Ethically

 
6.6 Chapter Review

 
Chapter 6 Exercises

 
 
Chapter 7: Making Controlled Comparisons
7.1 Cross-Tabulation Analysis with a Control Variable

 
7.2 Graphs for Controlled Cross-Tabulations

 
7.3 Mean Comparison Analysis with a Control Variable

 
7.4 Visualizing Controlled Mean Comparisons

 
7.5 Controlled Comparisons with Interval-Level Control Variables

 
7.6 Chapter Review

 
Chapter 7 Exercises

 
 
Chapter 8: Foundations of Statistical Inference
8.1 Estimating a Population Proportion with Computer Simulation

 
8.2 Expected Shape of Sampling Distributions

 
8.3 Confidence Intervals and Margins of Error

 
8.4 Student’s t-Distribution: When You’re Not Completely Normal

 
8.5 Chapter Review

 
Chapter 8 Exercises

 
 
Chapter 9: Hypothesis Tests with One or Two Samples
9.1 Role of the Null Hypothesis

 
9.2 Testing Hypothesis about a Population Proportion

 
9.3 Testing Hypothesis about Difference between Two Population Proportions

 
9.4 Testing Hypothesis about Population Mean

 
9.5 Testing Hypothesis about Difference between Two Population Means

 
9.6 Chapter Review

 
Chapter 9 Exercises

 
 
Chapter 10: Chi-Square Test and Analysis of Variance
10.1 The Chi-Square Test of Independence

 
10.2 Measuring the Strength of Association between Categorical Variables

 
10.3 Chi-Square Test and Measures of Association in Controlled Comparisons

 
10.4 Analysis of Variance

 
10.5 Chapter Review

 
Chapter 10 Exercises

 
 
Chapter 11: Correlation and Bivariate Regression
11.1 Correlation Analysis

 
11.2 Bivariate Regression

 
11.3 Creating Scatterplots for Bivariate Regression Analysis

 
11.4 Chapter Review

 
Chapter 11 Exercises

 
 
Chapter 12: Multiple Regression
12.1 Estimating and Interpreting Multiple Regression

 
12.2 Regression with Multiple Dummy Variables

 
12.3 Interaction Effects in Multiple Regression

 
12.4 Visualizing Multiple Regression Analysis with Bubble Plots

 
12.5 Graphing Interaction Relationships

 
12.6 Chapter Review

 
Chapter 12 Exercises

 
 
Chapter 13: Analyzing Regression Residuals
13.1 Expected Values, Observed Values, and Regression Residuals

 
13.2 Squared and Standardized Residuals

 
13.3 Assumptions about Regression Residuals

 
13.4 Analyzing Graphs of Regression Residuals

 
13.5 Testing Regression Assumptions with Residual Values

 
13.6 Identifying Outliers and Influential Observations

 
13.7 What If You Diagnose Problems with Residuals?

 
13.8 Chapter Review

 
Chapter 13 Exercises

 
 
Chapter 14: Logistic Regression
14.1 Odds, Logged Odds, and Probabilities

 
14.2 Estimating Logistic Regression Models

 
14.3 Graphing Predicted Probabilities with One Independent Variable

 
14.4 Logistic Regression with Multiple Independent Variables

 
14.5 Graphing Predicted Probabilities with Multiple Independent Variables

 
14.6 Chapter Review

 
Chapter 14 Exercises

 
 
Chapter 15 Doing Your Own Political Analysis
15.1 Doable Research Ideas

 
15.2 Importing Data into SPSS

 
15.3 Writing It Up

 
15.4 Chapter Review

 
Chapter 15 Exercises

 
 
Appendix, Table A-1: Variables in the GSS Dataset in Alphabetical Order
 
Appendix, Table A-2: Variables in the ANES Dataset in Alphabetical Order
 
Appendix, Table A-3: Variables in the States Dataset by Topic
 
Appendix, Table A-4: Variables in the World Dataset by Topic

Supplements

Student Resource Site
Online resources included with this text:
 - Datasets
- Video
 - Syntax Files
 - Solutions Manual
 - Tables and Figures 

The online resources for your text are available via the password-protected Instructor Resource Site, which offers access to all text-specific resources, including a test bank and editable, chapter-specific PowerPoint® slides. 

For additional information, custom options, or to request a personalized walkthrough of these resources, please contact your sales representative. 
Instructor Resource Site
Online resources included with this text:
 - Datasets
- Video
 - Syntax Files
 - Solutions Manual
 - Tables and Figures 

The online resources for your text are available via the password-protected Instructor Resource Site, which offers access to all text-specific resources, including a test bank and editable, chapter-specific PowerPoint® slides. 

For additional information, custom options, or to request a personalized walkthrough of these resources, please contact your sales representative. 
Key features
NEW TO THIS EDITION:
  • Content has been reorganized into 15 chapters from 11 chapters to break up concepts for students and better follow typical class schedules. 
  • New chapters, such as those on graphing relationships, random assignment, and analyzing regression residuals, help provide a more comprehensive approach to teaching political analysis with SPSS.
  • Expanded content on data visualization, statistical inference, and regression analysis offer more detailed information on these important areas.
  • Updated Companion datasets can be used for assignments and exercises and include current datasets from the General Social Survey (GSS) and American National Election Studies (ANES), including new variables related to the 2020 presidential election.
  • Examples drawn from all major subfields reflect current scholarly debates American politics, political behavior, comparative politics, international relations, and law.

KEY FEATURES:

  • Chapters match the structure of The Essentials of Political Analysis 7th edition so students can learn data analysis with SPSS alongside the main text.
  • Multiple datasets, guided examples, screenshots, graphics instruction, and end-of-chapter exercises help students with little or no experience with SPPS to learn how to interpret and conduct political analysis
  • The chapters cover all major topics in data analysis—familiarizing students with a wide range of topics, from descriptive statistics to logistic regression.
  • The pages of this book are perforated, three-hole punched, and 8.5” x 11” in size so instructors can use end-of-chapter exercises as homework assignments that students complete and submit.
  • A two-step approach to skill-set learning—(1) Perform the guided examples. (2) Work the exercises—give students opportunities to apply their new skills and to engage students in discovering the meaning of their findings and learning to interpret them
  • Syntax files for book and chapter exercises are available to Instructors so they can quickly and easily replicate what's done in the book and the solutions to chapter exercises.
  • Instructors can obtain a complete solution’s manual with instructor’s access to the book’s web site (edge.sagepub.com/pollock). 
  • QR codes linking to how-to tutorial videos have been added throughout the text to reinforce student learning. 
  • Screencasts of all of the book's guided examples, plus bonus examples not covered in the book, are available on the student study site and via QR codes in the text.
 

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