A Guide to R for Social and Behavioral Science Statistics

Brian Joseph Gillespie, Ph.D. is a researcher in the Faculty of Spatial Sciences at the University of Groningen in the Netherlands. He is the author of Household Mobility in America: Patterns, Processes, and Outcomes (Palgrave, 2017) and coauthor of The Practice of Survey Research: Theory and Applications (Sage, 2016) and Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences (Sage, 2018). He has also published research in a variety of social science journals on topics related to family, migration, the life course, and interpersonal relationships. Kathleen Charli Hibbert, Ph.D. is a social ecologist at the U.S. Environmental Protection Agency researching potential health impacts from relationships and interactions between humans and their environment(s). She has published works on micro-activity behavior, intentional living communities, vulnerable communities, e-waste, non-chemical stressors, children’s health, and older adult sexuality. She has taught quantitative analysis and research methods in sociology, psychology, and research departments using a variety of statistical applications. William E. Wagner, III,  PhD, is Chair of the Department of Sociology at California State University, Dominguez Hills and Executive Director of the Social Science Research & Instructional Council of the CSU. He is co-author of Adventures in Social Research, 11th edition (SAGE, 2022), The Practice of Survey Research (SAGE, 2016), and A Guide to R for Social and Behavioral Sciences (SAGE, 2020) and author of Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics, 7th edition (SAGE, 2019).

• PrefaceAcknowledgmentsAbout the AuthorsChapter 1 • R and RStudio®IntroductionStatistical Software OverviewDownloading R and RStudioRStudioFinding R and RStudio PackagesOpening DataSaving Data FilesConclusionChapter 2 • Data, Variables, and Data ManagementAbout the Data and VariablesStructure and Organization of Classic “Wide” DatasetsThe General Social SurveyVariables and MeasurementRecoding VariablesLogic of CodingRecoding Missing ValuesComputing VariablesRemoving OutliersConclusionChapter 3 • Data Frequencies and DistributionsFrequencies for Categorical VariablesCumulative Frequencies and PercentagesFrequencies for Interval/Ratio VariablesHistogramsThe Normal DistributionNon-Normal Distribution CharacteristicsExporting TablesConclusionChapter 4 • Central Tendency and VariabilityMeasures of Central TendencyMeasures of VariabilityThe z-ScoreSelecting Cases for AnalysisConclusionChapter 5 • Creating and Interpreting Univariate and Bivariate Data VisualizationsIntroductionR’s Color PaletteUnivariate Data VisualizationBivariate Data VisualizationExporting FiguresConclusionChapter 6 • Conceptual Overview of Hypothesis Testing and Effect SizeIntroductionNull and Alternative HypothesesStatistical SignificanceTest Statistic DistributionsChoosing a Test of Statistical SignificanceHypothesis Testing OverviewEffect SizeConclusionChapter 7 • Relationships Between Categorical VariablesSingle Proportion Hypothesis TestGoodness of FitBivariate FrequenciesThe Chi-Square Test of Independence (?2)ConclusionChapter 8 • Comparing One or Two MeansIntroductionOne-Sample t-TestThe Independent Samples t-TestExamplesAdditional Independent Samples t-Test ExamplesEffect Size for t-Test: Cohen’s dPaired t-TestConclusionChapter 9 • Comparing Means Across Three or More Groups (ANOVA)Analysis of Variance (ANOVA)ANOVA in RTwo-Way Analysis of VarianceConclusionChapter 10 • Correlation and Bivariate RegressionReview of ScatterplotsCorrelationsPearson’s Correlation CoefficientCoefficient of DeterminationCorrelation Tests for Ordinal VariablesThe Correlation MatrixBivariate Linear RegressionLogistic RegressionConclusionChapter 11 • Multiple RegressionThe Multiple Regression EquationInteraction Effects and InterpretationLogistic RegressionInterpretation and Presentation of Logistic Regression ResultsConclusionChapter 12 • Advanced Regression TopicsAdvanced Regression TopicsPolynomialsLogarithmsScaling DataMulticollinearityMultiple ImputationFurther ExplorationConclusionIndex

This text is most timely given the popular use of R in many introductory stats courses throughout our universities. The reader will find the presentation of visuals, tips, and syntax in using R to be most impressive relative to what other books provide! This is a "must have" text for faculty and students embarking on a stats course that utilizes the R program.