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The Essentials of Political Analysis
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The Essentials of Political Analysis

Seventh Edition
Available with:
Learning platform
January 2025 | CQ Press
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Equip students with the skills and confidence they need to conduct political analyses and critically assess statistical research. In the Seventh Edition of The Essentials of Political Science, bestselling authors Philip H. Pollock III and Barry C. Edwards build students’ analytic abilities and develop their statistical reasoning with new data, fresh exercises, and clear examples. This brief and reader-friendly guide walks students through the essentials— defining measurement, formulating and testing hypotheses, measuring variables—while using key terms, chapter-opening objectives, over 80 tables and figures, and practical exercises to get them using and applying their new skills. 

Using Excel, R, SPSS, or STATA? Companion workbooks featuring statistical software instructions and exercises help your students apply their knowledge. See the full suite of companions here.

 
List of Tables
 
List of Boxes
 
Preface
 
Acknowledgments
 
About the Authors
 
Chapter 1 The Definition and Measurement of Concepts
1.1 Conceptual Definitions

 
1.2 Operational Definitions

 
1.3 Measurement Error

 
1.4 Reliability and Validity

 
1.5 Working With Datasets, Codebooks, and Software

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 2 Measuring and Describing Variables
2.1 Essential Features

 
2.2 Levels of Measurement

 
2.3 Central Tendency and Dispersion of Variables

 
2.4 Describing Nominal-Level Variables

 
2.5 Describing Ordinal-Level Variables

 
2.6 Describing Interval-Level Variables

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 3 Creating and Transforming Variables
3.1 Transforming Interval-Level Variables With Math Functions

 
3.2 Sometimes, Less Is More: Simplifying Variables

 
3.3 Managing Data and Metadata

 
3.4 Additive Indexes and Measurement Scales

 
3.5 Advanced Data Transformation Methods

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 4 Proposing Explanations, Framing Hypotheses, and Making Comparisons
4.1 “All Models Are Wrong, but Some Are Useful”

 
4.2 Proposing Explanations

 
4.3 Framing Hypotheses

 
4.4 Making Comparisons

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 5 Graphing Relationships and Describing Patterns
5.1 Historic Examples of Data Visualization

 
5.2 Levels of Measurement and Choice of Graph Types

 
5.3 Visualizing Relationships With Categorical Variables

 
5.4 Describing Patterns

 
5.5 Graphing Relationship Between Interval-Level Variables

 
5.6 Challenges of Visualizing Data

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 6 Research Design, Research Ethics, and Evidence of Causation
6.1 Establishing Causation

 
6.2 Experimental Designs

 
6.3 Selecting Cases for Analysis

 
6.4 Conducting Research Ethically

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 7 Making Controlled Comparisons
7.1 The Logic of Controlled Comparisons

 
7.2 Essential Terms and Concepts

 
7.3 Effect of Partisanship on Gun Control Vote, Controlling for Gender: An Illustrative Example

 
7.4 Controlled Mean Comparisons

 
7.5 Identifying Patterns

 
7.6 Advanced Methods of Making Controlled Comparisons

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 8 Foundations of Statistical Inference
8.1 Population Parameters and Sample Statistics

 
8.2 The Central Limit Theorem and the Normal Distribution

 
8.3 Quantifying Standard Errors

 
8.4 Confidence Intervals

 
8.5 Sample Size and the Margin of Error of a Poll

 
8.6 Inferences With Small Batches: The Student’s t-Distribution

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 9 Hypothesis Tests With One or Two Samples
9.1 Statistical Significance and Null Hypothesis Testing

 
9.2 One-Sample Significance Tests

 
9.3 Two-Sample Significance Tests

 
9.4 Criticisms of Null Hypothesis Testing

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 10 Chi-Square Test and Analysis of Variance
10.1 Null Hypothesis Tests With More than Two Groups

 
10.2 The Chi-Square Test of Independence

 
10.3 Measures of Association

 
10.4 Analysis of Variance (ANOVA)

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 11 Correlation and Bivariate Regression
11.1 Correlation

 
11.2 Bivariate Regression

 
11.3 Educational Attainment and Voter Turnout in States Example

 
11.4 R-Square and Adjusted R-Square

 
11.5 All Models Are Still Wrong, but Some Are Useful

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 12 Multiple Regression
12.1 Multiple Regression Equation

 
12.2 Educational Attainment and Voter Turnout in States Revisited

 
12.3 Regression With Multiple Dummy Variables

 
12.4 Interaction Effects in Multiple Regression

 
12.5 Some Practical Issues in Multiple Regression Analysis

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 13 Analyzing Regression Residuals
13.1 What Are Regression Residuals?

 
13.2 Assumptions About Regression Residuals

 
13.3 Diagnostic Graphs of Regression Residuals

 
13.4 Testing Assumptions About Regression Residuals

 
13.5 What If Assumptions Are Violated?

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 14 Logistic Regression
14.1 The Logistic Regression Approach

 
14.2 Logistic Regression Analysis of Vote Choice in the 2020 Presidential Election

 
14.3 Finding the Best Fit: Maximum Likelihood Estimation

 
14.4 Logistic Regression With Multiple Independent Variables

 
14.5 Graphing Predicted Probabilities With Multiple Independent Variables

 
Summary

 
Key Terms

 
Exercises

 
 
Chapter 15 Conducting Your Own Political Analysis
15.1 Picking a Good Topic

 
15.2 Getting Focused and Staying Motivated

 
15.3 Reviewing Prior Literature

 
15.4 Collecting Data

 
15.5 Writing It Up

 
15.6 Maintain a Scientific Mindset

 
Summary

 
Key Terms

 
Exercises

 
 
Glossary
 
Endnotes
 
Index

An excellent introduction on how to carry out social scientific research. Highly recommended for undergraduate courses on research design and the basics of statistical analysis.

David Dreyer
Lenoir-Rhyne University

An approachable and lucidly written text that provides students with the essential knowledge and tools for conducting empirical political science research.

Yi Yang
James Madison University

An excellent textbook to use for scope and methods and undergraduate statistics courses.

Maurice Mangum
Jackson State University

A great book that introduces students to the basic elements of research, data, and data analysis. Students don't necessarily need to start the class with a working knowledge of statistics to be successful.

Brian Crisher
University of West Florida

A well-written undergraduate methods textbook that even the most apprehensive student can understand.

Nadine Gibson
University of North Carolina Wilmington

Foundations of statistics for poli sci major in plain language with easy to comprehend exercises.

Volodymyr Guptan
University of Connecticut

Straightforward with good resources.

Youssef Chouhoud
Christopher Newport University

This is a thorough and understandable introduction to research methods that can easily be adapted to your classroom preferences and needs.

Scott Leibertz
University of South Alabama
Key features
NEW TO THIS EDITION:
  • New organization now divides the text into 15 chapters, providing a more structured and manageable approach for instructors and students alike. This now mirrors the companion texts to provide better symmetry.       
    • Former Chapter 2: Measuring and Describing Variables split into two chapters on “Measuring and Describing Variables” and “Creating and Transforming Variables.”
    • Former Chapter 3: Proposing Explanations, Framing Hypotheses, and Making Comparisons split into two chapters on “Proposing Explanations, Framing Hypotheses, and Making Comparisons” and “Graphing Relationships and Describing Patterns.”
    • Finally, former Chapter 8: Correlation and Linear Regression is now split into three chapters on “Correlation and Bivariate Regression,” “Multiple Regression,” and “Analyzing Regression Residuals
  • Expanded material on research ethics discusses new issues such as research transparency, double-blind peer review, P-hacking, and ethical responsibilities when conducting human subject research. 
  • Expanded coverage of data visualization in Chapter 5, Chapter 12, and throughout ensures students learn the skills behind creating clear and useful data visualizations.
  • New coverage of crucial issues in analysis such as data management, AI in research and analysis, and measures of correlation other than Pearson's ensure the relevance of the book to today's students. 
  • Updated figures and examples throughout the book bring concepts to life by connecting the chapter material to current events such as the 2020 Presidential Election.  

KEY FEATURES:

  • Clear pedagogy: The Essentials of Political Analysis is organized around a time-honored pedagogical principle: Foreshadow the topic, present the material, and then review the main points.
  • QR codes linking to author videos reinforce learning by enabling students to hear first-hand from the authors about the material that was discussed in the book
  • An emphasis on the supporting role of statistical reasoning helps students describe and interpret the empirical world.   
  • The essentials are presented in a non-statistical context within the first five chapters to enable students think clearly about concepts, describe variables, frame and test hypotheses, evaluate research design, and control for rival explanations.  
  • With these essentials in place, students are able to appreciate the pivotal role of inferential statistics—introduced and applied, with increasing sophistication, in Chapters 6 through 9
  • The final chapter, “Conducting Your Own Political Analysis” guides students through the process of writing an effective research paper.
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