A Guide to R for Social and Behavioral Science Statistics

A Guide to R for Social and Behavioral Science Statistics is a short, accessible book for learning R. This handy guide contains basic information on statistics for undergraduates and graduate students, shown in the R statistical language using RStudio®. The book is geared toward social and behavioral science statistics students, especially those with no background in computer science. Written as a companion book to be used alongside a larger introductory statistics text, the text follows the most common progression of statistics for social scientists. The guide also serves as a companion for conducting data analysis in a research methods course or as a stand-alone R and statistics text. This guide can teach anyone how to use R to analyze data, and uses frequent reminders of basic statistical concepts to accompany instructions in R to help walk students through the basics of learning how to use R for statistics. 

Table of Contents:
Preface Acknowledgments About the Authors Chapter 1 • R and RStudio® Introduction Statistical Software Overview Downloading R and RStudio RStudio Finding R and RStudio Packages Opening Data Saving Data Files Conclusion Chapter 2 • Data, Variables, and Data Management About the Data and Variables Structure and Organization of Classic “Wide” Datasets The General Social Survey Variables and Measurement Recoding Variables Logic of Coding Recoding Missing Values Computing Variables Removing Outliers Conclusion Chapter 3 • Data Frequencies and Distributions Frequencies for Categorical Variables Cumulative Frequencies and Percentages Frequencies for Interval/Ratio Variables Histograms The Normal Distribution Non-Normal Distribution Characteristics Exporting Tables Conclusion Chapter 4 • Central Tendency and Variability Measures of Central Tendency Measures of Variability The z-Score Selecting Cases for Analysis Conclusion Chapter 5 • Creating and Interpreting Univariate and Bivariate Data Visualizations Introduction R’s Color Palette Univariate Data Visualization Bivariate Data Visualization Exporting Figures Conclusion Chapter 6 • Conceptual Overview of Hypothesis Testing and Effect Size Introduction Null and Alternative Hypotheses Statistical Significance Test Statistic Distributions Choosing a Test of Statistical Significance Hypothesis Testing Overview Effect Size Conclusion Chapter 7 • Relationships Between Categorical Variables Single Proportion Hypothesis Test Goodness of Fit Bivariate Frequencies The Chi-Square Test of Independence (?2) Conclusion Chapter 8 • Comparing One or Two Means Introduction One-Sample t-Test The Independent Samples t-Test Examples Additional Independent Samples t-Test Examples Effect Size for t-Test: Cohen’s d Paired t-Test Conclusion Chapter 9 • Comparing Means Across Three or More Groups (ANOVA) Analysis of Variance (ANOVA) ANOVA in R Two-Way Analysis of Variance Conclusion Chapter 10 • Correlation and Bivariate Regression Review of Scatterplots Correlations Pearson’s Correlation Coefficient Coefficient of Determination Correlation Tests for Ordinal Variables The Correlation Matrix Bivariate Linear Regression Logistic Regression Conclusion Chapter 11 • Multiple Regression The Multiple Regression Equation Interaction Effects and Interpretation Logistic Regression Interpretation and Presentation of Logistic Regression Results Conclusion Chapter 12 • Advanced Regression Topics Advanced Regression Topics Polynomials Logarithms Scaling Data Multicollinearity Multiple Imputation Further Exploration Conclusion Index

About the Author :
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).

Review :
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.  Finally, a statistics book that makes statistics clear to those who hate statistics. "A Guide to R for Social and Behavioral Sciences" provides just the right balance between coverage of statistical concepts ad R guidelines. It eliminates the need to adopt a separate textbook for statistics and an R workbook. This is a great resource for both undergraduate and graduate students for training in fields increasingly utilizing R in data analyses! This is an excellent comprehensive book that fills in many of the gaps that researchers struggle to find in many sources. This is a great reference for Social and Behavioral scientists who want to get quickly to applying concepts using R, getting results, and understanding them. 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!