An Introduction to Political and Social Data Analysis (With R)

Thomas M. Holbrook is Emeritus Professor at the University of Wisconsin-Milwaukee, where he was a Distinguished Professor and the Wilder Crane Professor of Government in the political science department. He is a former editor of American Politics Research and the author of Do Campaigns Matter (Sage, 1996), Altered States (Oxford, 2016), and dozens of articles on various aspects of voting behavior and elections in the United States, most recently focusing on local politics. Professor Holbrook has taught undergraduate courses on data analysis and survey research for the past three decades and has integrated R into his data analysis courses for the past several years.

• Chapter 1: Introduction to Research and DataPolitical and Social Data AnalysisData Analysis or Statistics?Uses of Data AnalysisThe Research ProcessOther Data-Related IssuesCausal LanguageNext StepsExercisesChapter 2: Using R to Do Data AnalysisAccessing ROpening RStudioUnderstanding Where R (or Any Program) Fits InTime to Use RSome R TerminologyManaging Files and OutputNext StepsExercisesChapter 3: Frequencies and Basic GraphsGet ReadyIntroductionFrequenciesGraphing OutcomesNext StepsExercisesChapter 4: Data PreparationGet ReadyIntroductionData TransformationsCollapsing and Reordering CategoriesCombining VariablesSave Your ChangesNext StepsExercisesChapter 5: Measures of Central TendencyGet ReadyCentral TendencyModeMedianThe MeanMean, Median, and the Distribution of VariablesSkewness StatisticAdding Legends to GraphsNext StepsExercisesChapter 6: Measures of DispersionGet ReadyIntroductionMeasures of SpreadDispersion Around the MeanDichotomous VariablesDispersion in Categorical Variables?The Standard Deviation and the Normal CurveCalculating Area Under a Normal CurveOne Last ThingNext StepsExercisesChapter 7: ProbabilityGet ReadyProbabilityTheoretical ProbabilitiesEmpirical ProbabilitiesThe Normal Curve and ProbabilityNext StepsExercisesChapter 8: Sampling and InferenceGet ReadyStatistics and ParametersSampling ErrorSampling DistributionsProportionsConfidence IntervalsNext StepsExercisesChapter 9: Hypothesis TestingGet ReadyThe Logic of Hypothesis TestingDirect Hypothesis TestsProportionsT-DistributionTypes of Errort-test in RNext StepsExercisesChapter 10: Hypothesis Testing with Two GroupsGet ReadyTesting Hypotheses About Two MeansHypothesis Testing With Two MeansDifference in ProportionsPlotting Mean DifferencesWhat’s Next?ExercisesChapter 11: Hypothesis Testing With Multiple Groups (ANOVA)Get ReadyInternet Access as an Indicator of DevelopmentThe Relationship Between Wealth and Internet AccessAnalysis of VarianceAnova in REffect SizeConnecting the t-score and F-ratioNext StepsExercisesChapter 12: Hypothesis Testing with Non-Numeric Variables (Crosstabs)Get ReadyCrosstabsSampling ErrorHypothesis Testing With Crosstabs (Chi-square)Get ReadyDirectional Patterns in CrosstabsLimitations of Chi-squareNext StepsExercisesChapter 13: Measures of AssociationGet ReadyGoing Beyond Chi-squaredMeasures of Association for CrosstabsOrdinal Measures of AssociationRevisiting the Gender Gap in Abortion AttitudesNext StepsExercisesChapter 14: Correlation and ScatterplotsGet ReadyRelationships Between Numeric VariablesScatterplotsPearson’s rVariation in Strength of RelationshipsProportional Reduction in ErrorCorrelation and Scatterplot MatricesOverlapping ExplanationsNext StepsExercisesChapter 15: Simple RegressionGet ReadyLinear RelationshipsOrdinary Least Squares RegressionHow Well Does the Model Fit the Data?Proportional Reduction in ErrorGetting Regression Results in RUnderstanding the ConstantOrganizing the Regression OutputRevisiting Life ExpectancyImportant CaveatAdding Regression Information to ScatterplotsNext StepsExercisesChapter 16: Multiple RegressionGet ReadyMultiple RegressionModel AccuracyPredicted OutcomesRevisiting Presidential Votes in the StatesNext StepsExercisesChapter 17: Advanced Regression TopicsGet ReadyIncorporating Access to Health CareMulticollinearityChecking on LinearityWhich Variables Have the Greatest Impact?Statistics Versus SubstanceNext StepsExercisesChapter 18: Regression AssumptionsGet ReadyRegression AssumptionsNext StepsExercisesAppendix A: CodebooksAppendix B: Quarto TutorialAppendix C: Hidden R CodeEndnotesIndex

Clarity in communication is absolutely essentialin introductory methodology and data science courses. Holbrook′s way with words makes complicated statistical and computational language easy to understand and instills confidence in students.