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In Philip H. Pollock III and Barry C. Edwards' trusted IBM SPSS (R) 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. This Sixth Edition of An IBM SPSS (R) Companion to Political Analysis features thoroughly revised and updated datasets and is compatible with all post-12 releases of SPSS. "[The text] provides by far the best introduction for students wanting to learn how to use SPSS in conducting statistical analysis. Its clear in-depth examples makes data analysis accessible to even the most numbers-phobic student." -- Michael Burch "With a focus on both the mechanics of SPSS and interpretation of data, An IBM SPSS Companion to Political Analysis provides a very useful and useable addition to a Political Science Methods course. The chapters have engaging exercises that address many areas of the discipline and are organized to build students' understanding of statistical interpretation and skills with SPSS. Students learn to run the statistical procedures, to interpret the results, and to construct arguments with the data." -- Matthew J. Costello

An IBM (R) SPSS (R) Companion to Political Analysis

Figures

Preface

Getting Started

Downloading the Datasets

SPSS Full and Grad Pack Versions: What Is the Difference?

Watch Screencasts from SAGE Edge

Chapter 1. Introduction to SPSS

The Data Editor

Setting Options for Variable Lists

The Viewer

Selecting, Printing, and Saving Output

How to Format an SPSS Table

Saving Commands in Syntax Files

Getting Help

Chapter 1 Exercises

Chapter 2. Descriptive Statistics

How SPSS Stores Information about Variables

Interpreting Measures of Central Tendency and Variation

Describing Nominal Variables

Describing Ordinal Variables

Using the Chart Editor to Modify Graphics

Describing Interval Variables

Obtaining Case-level Information with Case Summaries

Chapter 2 Exercises

Chapter 3: Transforming Variables

Creating Indicator Variables

Working with Variable Labels

Recoding Interval-level Variables into Simplified Categories

Simplifying an Internal-level Variable with Visual Binning

Centering or Standardizing a Numeric Variable

Using Compute to Create an Additive Index

Chapter 3 Exercises

Chapter 4. Making Comparisons

Cross-tabulation Analysis

Visualizing Cross-tabulation Analysis with a Bar Chart

Mean Comparison Analysis

Visualizing Mean Comparison Analysis with a Line Chart

Creating a Box Plot to Make Comparisons

Chapter 4 Exercises

Chapter 5. Making Controlled Comparisons

Cross-tabulation Analysis with a Control Variable

Graphing Controlled Comparisons with Categorical Dependent Variables

Mean Comparison Analysis with a Control Variable

Visualizing Controlled Mean Comparisons

Chapter 5 Exercises

Chapter 6. Making Inferences about Sample Means

Finding the 95% Confidence Interval of a Sample Mean

Testing a Hypothetical Claim about the Population Mean

Inferences about the Difference between Two Sample Means

Visualizing Mean Comparisons with Error Bars

Making Inferences about Sample Proportions

Chapter 6 Exercises

Chapter 7: Chi-square and Measures of Association

Analyzing an Ordinal-level Relationship

Analyzing an Ordinal-level Relationship with a Control Variable

Analyzing a Nominal-level Relationship

Analyzing a Nominal-level Relationship with a Control Variable

Chapter 7 Exercises

Chapter 8. Correlation and Linear Regression

Correlation Analysis

Bivariate Regression

Creating Scatterplots for Bivariate Regression Analysis

Multiple Regression

Visualizing Multiple Regression Analysis with Bubble Plots

Chapter 8 Exercises

Chapter 9. Dummy Variables and Interaction Effects

Regression with Multiple Dummy Variables

Interaction Effects in Multiple Regression

Graphing Linear Prediction Lines for Interaction Relationships

Chapter 9 Exercises

Chapter 10. Logistic Regression

Thinking about Odds, Logged Odds, and Probabilities

Estimating Logistic Regression Models

Logistic Regression with Multiple Independent Variables

Graphing Predicted Probabilities with One Independent Variable

Graphing Predicted Probabilities with Multiple Independent Variables

Chapter 10 Exercises

Chapter 11. Doing Your Own Political Analysis

Seven Doable Ideas

Importing Data into SPSS

Writing It Up

Chapter 11 Exercises

Appendix, Table A-1: Variables in the GSS Dataset in Alphabetical Order

Appendix, Table A-2: Variables in the NES 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