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E-grāmata: Guide to R for Social and Behavioral Science Statistics

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(California State University, Dominguez Hills, USA), (U.S. Environmental Protection Agency, USA), (University of Groningen, Netherlands)
  • Formāts: PDF+DRM
  • Izdošanas datums: 07-Feb-2020
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781544344034
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Guide to R for Social and Behavioral Science StatisticsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 07-Feb-2020
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781544344034
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A Guide to R for Social and Behavioral Science Statistics is a short, accessible book for learning R, geared toward social and behavioral science students. Instructors Brian Gillespie, Kathleen Hibbert, and William E. Wagner, III, have combined a review of introductory statistics with an introduction to R to teach readers two of the most valuable skills for research and in the workplace. Designed for readers with no knowledge of statistics or R, A Guide to R for Social and Behavioral Science Statistics follows the most common progression of statistics, starting with basic descriptive statistics, and continuing up through inferential statistics and regression. This text provides step-by-step instructions for working with R, starting with downloading and installing R and RStudio®, featuring code and output so readers can follow along with each step. Readers can apply their knowledge with examples and exercises featuring data from the General Social Survey in each chapter. Tips on R show users how to avoid common pitfalls in R and most efficiently use the RStudio interface. With frequent reminders of statistical concepts to accompany instructions and tips in R, this text helps readers master R for statistics in the social and behavioral sciences.

Recenzijas

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

Preface xi
Purpose of the Book xi
Other Features xi
Notes on R xi
Tips for Learning R xii
Code, Output, and Annotation Conventions xii
Overview xiii
Acknowledgments xiv
About the Authors xv
Chapter 1 R and RStudio®
1(24)
Introduction
1(1)
Statistical Software Overview
1(2)
Downloading R and RStudio
3(5)
R
3(5)
RStudio
8(6)
Introduction to RStudio
9(1)
RStudio GUI
10(1)
The Help Function
11(3)
Finding R and RStudio Packages
14(3)
Base R Packages
15(1)
RStudio Packages
15(1)
The Tidyverse
15(2)
Opening Data
17(6)
Opening Existing Data
17(4)
Importing Other Formats
21(1)
Reading Data From a URL
22(1)
Entering Data
22(1)
Saving Data Files
23(1)
Conclusion
24(1)
Chapter 2 Data, Variables, and Data Management
25(40)
About the Data and Variables
25(1)
Structure and Organization of Classic "Wide" Datasets
25(2)
The General Social Survey
27(2)
Variables and Measurement
29(2)
Recoding Variables
31(1)
Logic of Coding
31(14)
Recoding Missing Values
45(8)
Imputation
49(4)
Computing Variables
53(2)
Removing Outliers
55(7)
Conclusion
62(3)
Chapter 3 Data Frequencies and Distributions
65(26)
Frequencies for Categorical Variables
65(8)
Absolute Frequencies
66(4)
Relative Frequencies
70(1)
Percentages
70(1)
Missing Values in Frequencies
71(1)
Full Percent
72(1)
Valid Percent
72(1)
Cumulative Frequencies and Percentages
73(5)
Cumulative Frequencies
73(2)
Cumulative Percentages
75(3)
Frequencies for Interval/Ratio Variables
78(2)
Histograms
80(3)
Titles
81(1)
Bins
81(1)
Relative Frequencies
82(1)
The Normal Distribution
83(1)
Non-Normal Distribution Characteristics
84(3)
Skewness
84(1)
Kurtosis
85(2)
Exporting Tables
87(2)
Microsoft Word
87(2)
Microsoft Excel
89(1)
Conclusion
89(2)
Chapter 4 Central Tendency and Variability
91(12)
Measures of Central Tendency
91(2)
Measures of Variability
93(2)
Range
93(1)
IOR (Inter-Quartile Range)
94(1)
Variance
94(1)
Standard Deviation
95(1)
Thez-Score
95(4)
How to Calculate a z-Score
96(3)
Selecting Cases for Analysis
99(2)
Conclusion
101(2)
Chapter 5 Creating and Interpreting Univariate and Bivariate Data Visualizations
103(27)
Introduction
103(1)
R's Color Palette
104(1)
Univariate Data Visualization
104(11)
Bar Graphs
104(4)
Pie Charts
108(2)
Cumulative Frequency Polygons
110(2)
Boxplots
112(2)
Histograms Revisited
114(1)
Bivariate Data Visualization
115(12)
Stacked Bar Graphs
115(2)
Clustered Bar Graphs
117(1)
Bar Charts for Grouped Means
118(2)
Grouped Boxplots
120(1)
Scatterplots
121(6)
Exporting Figures
127(1)
Conclusion
128(2)
Chapter 6 Conceptual Overview of Hypothesis Testing and Effect Size
130(11)
Introduction
130(1)
Null and Alternative Hypotheses
130(2)
Determination About the Null Hypothesis
131(1)
Statistical Significance
132(2)
Type I and Type II Errors
132(1)
Alpha
133(1)
Test Statistic Distributions
134(2)
Choosing a Test of Statistical Significance
136(1)
Hypothesis Testing Overview
137(1)
Effect Size
138(2)
Introduction
138(1)
Effect Size
138(1)
Effect Size Overview
139(1)
Conclusion
140(1)
Chapter 7 Relationships Between Categorical Variables
141(23)
Single Proportion Hypothesis Test
141(2)
Goodness of Fit
143(3)
Bivariate Frequencies
146(6)
The Chi-Square Test of Independence (x2)
152(10)
Observed and Expected Frequencies
153(1)
Chi-Square Test Statistic
154(1)
A Brief Note on Degrees of Freedom and the X2 Critical Value
155(1)
Additional Chi-Square Examples
156(1)
Example 1 Gender and Fear of Walking Alone in Neighborhood at Night
156(1)
Example 2 Sexual Orientation and Self-Reported Happiness
157(2)
Chi-Square Overview
159(1)
Effect Sizes for Chi-Square: φ and Cramer's V
159(3)
Conclusion
162(2)
Chapter 8 Comparing One or Two Means
164(26)
Introduction
164(1)
One-Sample t-Test
165(4)
The Independent Samples t-Test
169(3)
Independent Samples t-Test Notation and Hypotheses
170(1)
The Logic of the Independent Samples t-Test Statistic
171(1)
One-Tailed and Two-Tailed t-Tests
171(1)
A Brief Note on Degrees of Freedom and the t-Critical Value
172(1)
Examples
172(5)
Additional Independent Samples f-Test Examples
177(1)
Example 1 Gender and Number of Siblings (Non-Directional)
177(3)
Example 2 Children and Number of Siblings (Directional)
180(3)
Effect Size for t-Test: Cohen's d
183(3)
Paired t-Test
186(2)
Conclusion
188(2)
Chapter 9 Comparing Means Across Three or More Groups (ANOVA)
190(21)
Analysis of Variance (ANOVA)
190(1)
Anova Assumptions and Notation
190(1)
Anova Hypotheses
190(1)
A Brief Note on the F-Statistic and Degrees of Freedom
191(1)
Anova in R
191(14)
Post-Hoc Tests
195(1)
Additional Anova Examples
196(1)
Example 1 Sexual Orientation and Ideal Number of Children
196(4)
Example 2 Age Group and Number of Hours Watching Television
200(3)
Effect Size for One-Way ANOVA: n2
203(2)
Two-Way Analysis of Variance
205(4)
Conclusion
209(2)
Chapter 10 Correlation and Bivariate Regression
211(26)
Review of Scatterplots
211(2)
Correlations
213(1)
Pearson's Correlation Coefficient
214(3)
Coefficient of Determination
217(1)
Correlation Tests for Ordinal Variables
218(3)
The Correlation Matrix
221(6)
Bivariate Linear Regression
227(7)
Logistic Regression
234(2)
Conclusion
236(1)
Chapter 11 Multiple Regression
237(24)
The Multiple Regression Equation
250(1)
Interaction Effects and Interpretation
251(4)
Logistic Regression
255(3)
Interpretation and Presentation of Logistic Regression Results
258(1)
Conclusion
259(2)
Chapter 12 Advanced Regression Topics
261(20)
Advanced Regression Topics
261(4)
Polynomials
265(2)
Logarithms
267(2)
Scaling Data
269(2)
Multicollinearity
271(3)
Multiple Imputation
274(5)
Further Exploration
279(1)
Conclusion
280(1)
Index 281
Brian Joseph Gillespie, Ph.D. is a researcher in the Faculty of Spatial Sciences at the University of Groningen in the Netherlands. He is the author of Household Mobility in America: Patterns, Processes, and Outcomes (Palgrave, 2017) and coauthor of The Practice of Survey Research: Theory and Applications (Sage, 2016) and Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences (Sage, 2018). He has also published research in a variety of social science journals on topics related to family, migration, the life course, and interpersonal relationships.







 

Kathleen Charli Hibbert, Ph.D. is a social ecologist at the U.S. Environmental Protection Agency researching potential health impacts from relationships and interactions between humans and their environment(s). She has published works on micro-activity behavior, intentional living communities, vulnerable communities, e-waste, non-chemical stressors, childrens health, and older adult sexuality. She has taught quantitative analysis and research methods in sociology, psychology, and research departments using a variety of statistical applications. William E. Wagner, III,  PhD, 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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