Description
SAGE PUBLISHING Ibm® Spss® Companion To Political Analysis Sixth Edition 2019 Edition by Pollock
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. FiguresPrefaceGetting StartedDownloading the DatasetsSPSS Full and Grad Pack Versions: What Is the Difference?Watch Screencasts from SAGE EdgeChapter 1. Introduction to SPSSThe Data EditorSetting Options for Variable ListsThe ViewerSelecting, Printing, and Saving OutputHow to Format an SPSS TableSaving Commands in Syntax FilesGetting HelpChapter 1 ExercisesChapter 2. Descriptive StatisticsHow SPSS Stores Information about VariablesInterpreting Measures of Central Tendency and VariationDescribing Nominal VariablesDescribing Ordinal VariablesUsing the Chart Editor to Modify GraphicsDescribing Interval VariablesObtaining Case-level Information with Case SummariesChapter 2 ExercisesChapter 3: Transforming VariablesCreating Indicator VariablesWorking with Variable LabelsRecoding Interval-level Variables into Simplified CategoriesSimplifying an Internal-level Variable with Visual BinningCentering or Standardizing a Numeric VariableUsing Compute to Create an Additive IndexChapter 3 ExercisesChapter 4. Making ComparisonsCross-tabulation AnalysisVisualizing Cross-tabulation Analysis with a Bar ChartMean Comparison AnalysisVisualizing Mean Comparison Analysis with a Line ChartCreating a Box Plot to Make ComparisonsChapter 4 ExercisesChapter 5. Making Controlled ComparisonsCross-tabulation Analysis with a Control VariableGraphing Controlled Comparisons with Categorical Dependent VariablesMean Comparison Analysis with a Control VariableVisualizing Controlled Mean ComparisonsChapter 5 ExercisesChapter 6. Making Inferences about Sample MeansFinding the 95% Confidence Interval of a Sample MeanTesting a Hypothetical Claim about the Population MeanInferences about the Difference between Two Sample MeansVisualizing Mean Comparisons with Error BarsMaking Inferences about Sample ProportionsChapter 6 ExercisesChapter 7: Chi-square and Measures of AssociationAnalyzing an Ordinal-level RelationshipAnalyzing an Ordinal-level Relationship with a Control VariableAnalyzing a Nominal-level RelationshipAnalyzing a Nominal-level Relationship with a Control VariableChapter 7 ExercisesChapter 8. Correlation and Linear RegressionCorrelation AnalysisBivariate RegressionCreating Scatterplots for Bivariate Regression AnalysisMultiple RegressionVisualizing Multiple Regression Analysis with Bubble PlotsChapter 8 ExercisesChapter 9. Dummy Variables and Interaction EffectsRegression with Multiple Dummy VariablesInteraction Effects in Multiple RegressionGraphing Linear Prediction Lines for Interaction RelationshipsChapter 9 ExercisesChapter 10. Logistic RegressionThinking about Odds, Logged Odds, and ProbabilitiesEstimating Logistic Regression ModelsLogistic Regression with Multiple Independent VariablesGraphing Predicted Probabilities with One Independent VariableGraphing Predicted Probabilities with Multiple Independent VariablesChapter 10 ExercisesChapter 11. Doing Your Own Political AnalysisSeven Doable IdeasImporting Data into SPSSWriting It UpChapter 11 ExercisesAppendix, Table A-1: Variables in the GSS Dataset in Alphabetical OrderAppendix, Table A-2: Variables in the NES Dataset in Alphabetical OrderAppendix, Table A-3: Variables in the States Dataset by TopicAppendix, Table A-4: Variables in the World Dataset by Topic