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A Guide to R for Social and Behavioral Science Statistics

A Guide to R for Social and Behavioral Science Statistics

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February 2020 | 304 pages | SAGE Publications, Inc

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. 

About the Authors
Chapter 1 • R and RStudio®

Statistical Software Overview

Downloading R and RStudio


Finding R and RStudio Packages

Opening Data

Saving Data Files


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


Chapter 3 • Data Frequencies and Distributions
Frequencies for Categorical Variables

Cumulative Frequencies and Percentages

Frequencies for Interval/Ratio Variables


The Normal Distribution

Non-Normal Distribution Characteristics

Exporting Tables


Chapter 4 • Central Tendency and Variability
Measures of Central Tendency

Measures of Variability

The z-Score

Selecting Cases for Analysis


Chapter 5 • Creating and Interpreting Univariate and Bivariate Data Visualizations

R’s Color Palette

Univariate Data Visualization

Bivariate Data Visualization

Exporting Figures


Chapter 6 • Conceptual Overview of Hypothesis Testing and Effect Size

Null and Alternative Hypotheses

Statistical Significance

Test Statistic Distributions

Choosing a Test of Statistical Significance

Hypothesis Testing Overview

Effect Size


Chapter 7 • Relationships Between Categorical Variables
Single Proportion Hypothesis Test

Goodness of Fit

Bivariate Frequencies

The Chi-Square Test of Independence (?2)


Chapter 8 • Comparing One or Two Means

One-Sample t-Test

The Independent Samples t-Test


Additional Independent Samples t-Test Examples

Effect Size for t-Test: Cohen’s d

Paired t-Test


Chapter 9 • Comparing Means Across Three or More Groups (ANOVA)
Analysis of Variance (ANOVA)


Two-Way Analysis of Variance


Chapter 10 • Correlation and Bivariate Regression
Review of Scatterplots


Pearson’s Correlation Coefficient

Coefficient of Determination

Correlation Tests for Ordinal Variables

The Correlation Matrix

Bivariate Linear Regression

Logistic Regression


Chapter 11 • Multiple Regression
The Multiple Regression Equation

Interaction Effects and Interpretation

Logistic Regression

Interpretation and Presentation of Logistic Regression Results


Chapter 12 • Advanced Regression Topics
Advanced Regression Topics



Scaling Data


Multiple Imputation

Further Exploration




Student Study Site
The student study site contains the R code detailed in the book.

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. 

Kyle Woosnam
University of Georgia

Finally, a statistics book that makes statistics clear to those who hate statistics.

Frank A. Salamone
Iona College and University of Phoenix

"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.

Renato Corbetta
University of Alabama at Birmingham

This is a great resource for both undergraduate and graduate students for training in fields increasingly utilizing R in data analyses!

Dr. Lisa Hollis-Sawyer
Northeastern Illinois University

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.

Ahmed Ibrahim
Johns Hopkins University

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! 

Kyle Woosnam
University of Georgia
Key features
  • Clear, concise explanations of how to use R and RStudio® to perform statistical calculations
  • Follows the most common progression of statistical texts to serve as a companion for any applied statistics text
  • Features data from the General Social Survey to give students context for each statistical test 
  • Tip boxes sprinkled throughout provide helpful hints on how to use R efficiently
  • Data exercises help students apply their knowledge of R and statistics
  • Assumes no knowledge of R or statistics and serves as an easy guide for those new to either

For instructors

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