An IBM (R) SPSS (R) Companion to Political Analysis 6/e

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.

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

"[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