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A Guide to R for Social and Behavioral Science Statistics

A Guide to R for Social and Behavioral Science Statistics

Authors
Publisher SAGE Publications Inc
Year 21/05/2020
Pages 304
Version paperback
Readership level College/higher education
Language English
ISBN 9781544344027
Categories Research methods: general
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Book description

A short, accessible book for learning R, this text follows the most common progression of statistics for social scientists. Written as a companion book to be used alongside a larger introductory statistics text, this guide also services as a companion for conducting data analysis in a research methods course or as a stand-alone R and statistics text. With frequent reminders of basic statistical concepts to accompany instructions in R, the guide can teach anyone to how to use R for statistics. 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 Finally, a statistics book that makes statistics clear to those who hate statistics. -- Frank A. Salamone "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 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 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 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 Woosnam

A Guide to R for Social and Behavioral Science Statistics

Table of contents

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

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