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A Guide to R for Social and Behavioral Science Statistics
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A Guide to R for Social and Behavioral Science Statistics



February 2020 | 312 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. 


 
Chapter 1: R and RStudio
Introduction

 
Statistical Software Overview

 
Downloading R and RStudio

 
RStudio

 
Finding R and R Studio 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” Data Sets

 
The General Social Survey

 
Variables and Measurement

 
Recoding Variables

 
Logic of Coding

 
Recoding of Missing Values

 
Computing Variables

 
Eliminating 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 & 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

 
Non-Parametric Correlation Tests

 
The Correlation Matrix

 
Bivariate Linear Regression

 
Logistic Regression

 
Conclusion

 
 
Chapter 11: Multiple Regression
The Multiple Regression Equation

 
Interaction Effects and Interpretation

 
Logistic Regression

 
Interpretation & Presentation Logistic Regression Results

 
Conclusion

 
 
Chapter 12: Advanced Regression Topics
Advanced Regression Topics

 
Polynomials

 
Logarithms

 
Scaling Data

 
Mullticolinearity

 
Further Exploration

 
Multiple Imputation

 
Conclusion

 

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 author [insert here] does 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

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