A Guide to R for Social and Behavioral Science Statistics

Preface Acknowledgments About the Authors Chapter 1 * R and RStudio (R) 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