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.
Зміст
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
Про автора
William E. Wagner, III, Ph D, 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).
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