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Lab Manual for Social Science Statistics Using R
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Lab Manual for Social Science Statistics Using R



September 2026 | 352 pages | SAGE Publications, Inc
Lab Manual for Social Science Statistics Using R is a hands-on, workbook-style text that introduces students to statistical analysis through active engagement with real data using R and RStudio®. Designed for introductory statistics and research methods courses, the book guides students step by step through the research process using data from the General Social Survey. Beginning with an orientation to R, RStudio, and data preparation, the text covers core statistical topics—including descriptive statistics, data visualization, chi-square tests, t-tests, ANOVA, correlation, and regression—using accessible Base R commands. Brief conceptual explanations are paired with guided demonstrations, cumulative exercises, and lab assignments that emphasize learning by doing, helping students build confidence in managing data, conducting analyses, and professionally interpreting and reporting results from start to finish.

 
Preface
 
Chapter 1: RStudio and the 2018 General Social Survey
Installation

 
Introduction to RStudio

 
The General Social Survey (GSS)

 
Tips for Learning R & Coding

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 1: Exploring the Basics of R/RStudio

 
 
Chapter 2: Measurement and Modifying Data
Overview of Variable Types

 
Transforming Variable Types

 
The Logic of Recoding

 
Frequencies

 
Recoding Variables

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 2

 
 
Chapter 3: Univariate Analysis
Frequencies (marital)

 
Descriptive Statistics

 
Central Tendency

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 3

 
 
Chapter 4: Visually Presenting Univariate Data
Data Visualization

 
Bar Charts

 
Cumulative Frequency Polygon

 
Boxplots

 
Histograms

 
Data Distributions

 
Saving Figures

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 4

 
 
Chapter 5: Bivariate Analysis with Chi-Square
Contingency Tables (Crosstabulation)

 
Data Visualization for Two Categorical Variables

 
Overview of Variables, Hypotheses and Significance

 
Writing Up Results

 
Goodness of Fit Test

 
Test of Independence

 
Effect Size Phi and Cramer’s V

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Exercise 5

 
 
Chapter 6: The t-Test for Difference Between Means
Overview of the t-Test

 
One-Sample t-test

 
Independent Samples t-test

 
Paired t-test

 
Effect Size: Cohen’s d

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 6

 
 
Chapter 7: Analysis of Variance
Overview of the Analysis of Variance (ANOVA)

 
One-Way ANOVA

 
Comparing More Than One Mean

 
Post -Hoc Analysis

 
Effect Size for a One-Way ANOVA: Eta-Squared

 
Two-Way ANOVA

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 7

 
 
Chapter 8: Correlation and Regression
Scatterplots

 
Pearson’s correlation coefficient

 
Correlation Coefficient: r

 
Coefficient of Determination

 
Pearson’s Product Moment Correlation and Correlation Matrix

 
Bivariate Linear Regression

 
Regression Equation

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 8

 
 
Chapter 9: Advanced Regression Topics
Overview of Regressions

 
Binary Logistic Regression

 
Odds Ratios

 
Multiple Regression

 
Functions

 
Packages

 
Main Points

 
Key Terms

 
Review Questions

 
Lab Assignment 9

 
 
Chapter & Lab Exercises

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 M. Woosnam
University of Georgia

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

Lisa Hollis-Sawyer
Northeastern Illinois University
Key features
  • Hands-on, lab manual approach that emphasizes learning statistics by actively working with real data rather than passively reading explanations
  • Step-by-step guidance using R and RStudio®, with detailed demonstrations, screenshots, and worked examples that support novice users
  • Real-world data throughout, featuring the General Social Survey (GSS) to help students explore meaningful social science questions
  • Coverage aligned with introductory statistics and methods courses, including data preparation, descriptive statistics, visualization, chi-square tests, t-tests, ANOVA, correlation, and regression
  • Focus on accessible Base R commands, minimizing reliance on rapidly changing packages while introducing additional tools only when necessary