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Guide to R for Social and Behavioral Science Statistics, A


Guide to R for Social and Behavioral Science Statistics, A

Paperback by Gillespie, Brian Joseph (University of Groningen, Netherlands); Hibbert, Kathleen Charli (U.S. Environmental Protection Agency, USA); Wagner, William E. (California State University, Dominguez Hills, USA)

Guide to R for Social and Behavioral Science Statistics, A

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ISBN:
9781544344027
Publication Date:
21 May 2020
Language:
English
Publisher:
SAGE Publications Inc
Pages:
304 pages
Format:
Paperback
For delivery:
Estimated despatch 22 May 2024
Guide to R for Social and Behavioral Science Statistics, A

Description

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.

Contents

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

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