Lab Manual for Social Science Statistics Using R

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.

This text is a welcome addition to the existing works that seek to explain how to use R and R Studio. The authors do a marvelous job in breaking the program down to its most basic elements for beginners and advanced users as they undertake numerous statistical procedures. Some of the finest qualities of the work are the visuals and screenshots that give readers the confidence they need to run statistics using R in the most proficient means possible!

• PrefaceChapter 1: RStudio and the 2018 General Social SurveyInstallationIntroduction to RStudioThe General Social Survey (GSS)Tips for Learning R & CodingFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 1: Exploring the Basics of R/RStudioChapter 2: Measurement and Modifying DataOverview of Variable TypesTransforming Variable TypesThe Logic of RecodingFrequenciesRecoding VariablesFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 2Chapter 3: Univariate AnalysisFrequencies (marital)Descriptive StatisticsCentral TendencyFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 3Chapter 4: Visually Presenting Univariate DataData VisualizationBar ChartsCumulative Frequency PolygonBoxplotsHistogramsData DistributionsSaving FiguresFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 4Chapter 5: Bivariate Analysis with Chi-SquareContingency Tables (Crosstabulation)Data Visualization for Two Categorical VariablesOverview of Variables, Hypotheses and SignificanceWriting Up ResultsGoodness of Fit TestTest of IndependenceEffect Size Phi and Cramer’s VFunctionsPackagesMain PointsKey TermsReview QuestionsLab Exercise 5Chapter 6: The t-Test for Difference Between MeansOverview of the t-TestOne-Sample t-testIndependent Samples t-testPaired t-testEffect Size: Cohen’s dFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 6Chapter 7: Analysis of VarianceOverview of the Analysis of Variance (ANOVA)One-Way ANOVAComparing More Than One MeanPost -Hoc AnalysisEffect Size for a One-Way ANOVA: Eta-SquaredTwo-Way ANOVAFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 7Chapter 8: Correlation and RegressionScatterplotsPearson’s correlation coefficientCorrelation Coefficient: rCoefficient of DeterminationPearson’s Product Moment Correlation and Correlation MatrixBivariate Linear RegressionRegression EquationFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 8Chapter 9: Advanced Regression TopicsOverview of RegressionsBinary Logistic RegressionOdds RatiosMultiple RegressionFunctionsPackagesMain PointsKey TermsReview QuestionsLab Assignment 9Chapter & Lab Exercises