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Applied Statistics Using R: A Guide for the Social Sciences [Mīkstie vāki]

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  • Formāts: Paperback / softback, 472 pages, height x width: 242x170 mm, weight: 890 g
  • Izdošanas datums: 30-Nov-2021
  • Izdevniecība: Sage Publications Ltd
  • ISBN-10: 1526476223
  • ISBN-13: 9781526476227
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Applied Statistics Using R: A Guide for the Social SciencesPalielināt
  • Formāts: Paperback / softback, 472 pages, height x width: 242x170 mm, weight: 890 g
  • Izdošanas datums: 30-Nov-2021
  • Izdevniecība: Sage Publications Ltd
  • ISBN-10: 1526476223
  • ISBN-13: 9781526476227
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781526476227.html
Keywords:

Drawing on real world data to showcase different techniques, this practical book helps you use R for data analysis in your own research.



If you want to learn to use R for data analysis but aren’t sure how to get started, this practical book will help you find the right path through your data.

Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research.

It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers.

The book:

  • Shows you how to use R packages and apply functions, adjusting them to suit different datasets.
  • Gives you the tools to try new statistical techniques and empowers you to become confident using them.
  • Encourages you to learn by doing when running and adapting the authors’ own code.
  • Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect.
Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.

Recenzijas

This book is the best Ive seen for R, both in its clarity and coverage of topics. Practically oriented, with a profusion of examples and an engaging narrative, it is a must-have for all those studying applied social sciences. -- Sergio Venturini

Online Resources xi
About the Authors xiii
Preface xv
Acknowledgements xvi
How to Use This Book xvi
Common Error Messages in R to Know About xviii
Regression Approach to ANOVA xxi
Authors' Contact xxii
1 Introduction to R
1(22)
1.1 What Is R and Why Should You Use It?
2(4)
1.2 What Is RStudio?
6(1)
1.3 How to Install R and RStudio?
7(2)
1.3.1 R for Windows
7(1)
1.3.2 R for Mac
7(1)
1.3.3 R for Linux
8(1)
1.3.4 RStudio for Windows, Mac OS, and Linux
8(1)
1.4 Getting to Know RStudio
9(4)
1.4.1 Script pane
10(1)
1.4.2 Console pane
11(1)
1.4.3 Environment pane
12(1)
1.4.4 Plot pane
13(1)
1.5 Internet Resources for R
13(3)
1.6 Contributed R Packages
16(2)
1.7 Updating R, RStudio, and Contributed Packages
18(1)
1.8 Conclusion
19(4)
2 Importing and Working With Data in R
23(22)
2.1 How Is a Dataset Represented in R?
24(1)
2.2 Importing Data Into R
25(7)
2.3 Entering Data in R
32(4)
2.4 How Do We Work With a Dataset in R?
36(3)
2.5 Data Types
39(3)
2.6 Conclusion
42(3)
3 How Does R Work?
45(24)
3.1 The Way R Works
46(1)
3.2 What Is a Function?
47(3)
3.3 What Is an Object?
50(15)
3.3.1 Vectors
53(2)
3.3.2 Data frames
55(2)
Extracting rows and columns from data frames
57(2)
Adding observations and variables
59(2)
Renaming variables
61(1)
3.3.3 Matrices
62(1)
3.3.4 Lists
63(2)
3.4 Conclusion
65(4)
4 Data Management
69(40)
4.1 Data Management of Variables
71(19)
4.1.1 Generating new variables
71(3)
4.1.2 Recoding variables
74(2)
4.1.3 Replacing values
76(3)
4.1.4 Renaming variables
79(1)
4.1.5 Exploring missing values
80(4)
4.1.6 Generating dummy variables
84(2)
4.1.7 Changing the data types of variables
86(2)
4.1.8 Labelling variables
88(1)
4.1.9 Tidying up categorical variables
88(2)
4.2 Data Management of Datasets
90(14)
4.2.1 Selecting and excluding variables
90(2)
4.2.2 Selecting observations
92(2)
4.2.3 Merging datasets by variables
94(2)
4.2.4 Merging datasets by observations
96(1)
4.2.5 Sorting a dataset
97(1)
4.2.6 Reshaping a dataset
98(2)
4.2.7 Ordering variables
100(1)
4.2.8 Drawing random samples from a dataset
101(1)
4.2.9 Piping (chaining)
102(2)
4.3 Conclusion
104(5)
5 Data Visualization With ggplot2
109(40)
5.1 The Role of Visualization in Data Analysis
110(2)
5.2 Understanding ggplot2
112(20)
5.2.1 Structure of a layer
113(1)
Data
114(1)
Mapping
114(1)
Geometric objects
115(3)
Statistical transformations
118(4)
Positions
122(2)
5.2.2 Additional components affecting all layers
124(1)
Scales
124(1)
Coordinate systems
125(2)
Labels and annotations
127(1)
Themes
128(3)
Facets
131(1)
5.3 Examples in R
132(12)
5.3.1 Univariate graphs
133(1)
One continuous variable
133(5)
One categorical variable
138(2)
5.3.2 Bivariate graphs
140(1)
One categorical X and one continuous y
140(2)
One continuous X and one continuous y
142(1)
5.3.3 Multivariate
143(1)
Continuous X1 and Y and one categorical X2
143(1)
5.4 Conclusion
144(5)
6 Descriptive Statistics
149(34)
6.1 Describing a Single Variable
151(19)
6.1.1 Central tendency of a distribution
153(3)
6.1.2 Measures of spread
156(4)
6.1.3 Skew and kurtosis
160(2)
6.1.4 Discrete distributions
162(3)
6.1.5 Quick descriptive analysis
165(1)
Summarytools package
166(2)
Skimr package
168(2)
6.2 Describing Relationships Between Variables
170(6)
6.2.1 Correlation coefficient
171(3)
6.2.2 Cross-tabulation
174(2)
6.3 Summarizing Variables Across Groups
176(3)
6.4 Conclusion
179(4)
7 Simple (Bivariate) Regression
183(20)
7.1 What Is Regression Analysis?
184(1)
7.2 Simple Linear Regression Analysis
185(11)
7.2.1 Ordinary least squares
188(2)
7.2.2 Goodness of fit
190(1)
Residual standard deviation
190(1)
Coefficient of determination (R2)
191(2)
7.2.3 Hypothesis test for slope coefficient
193(1)
The p-value approach
193(2)
The confidence interval approach
195(1)
7.2.4 Prediction in linear regression
195(1)
7.3 Example in R
196(4)
7.4 Conclusion
200(3)
8 Multiple Linear Regression
203(26)
8.1 Multiple Regression Analysis
204(9)
8.1.1 Estimation
205(1)
8.1.2 Goodness of fit and the F test
206(1)
8.1.3 Adjusted R2
207(1)
8.1.4 Partial slope coefficient
208(1)
8.1.5 Prediction in multiple regression
209(1)
8.1.6 Standardization and relative importance
210(1)
8.1.7 Regression assumptions and diagnostics
211(2)
8.2 Example in R
213(12)
8.3 Conclusion
225(4)
9 Dummy-Variable Regression
229(30)
9.1 Why Dummy-Variable Regression?
230(3)
9.1.1 Creating dummy variables
231(2)
9.1.2 The logic behind dummy-variable regression
233(1)
9.2 Regression With One Dummy Variable
233(3)
9.2.1 Example in R
234(2)
9.3 Regression With One Dummy Variable and a Covariate
236(3)
9.3.1 Example in R
237(2)
9.4 Regression With More Than One Dummy Variable
239(10)
9.4.1 Example in R
241(1)
9.4.2 Comparing the included groups
242(1)
Changing the reference group
243(1)
Linear combination
244(3)
9.4.3 Pairwise multiple comparison adjustment
247(2)
9.5 Regression With More Than One Dummy Variable and a Covariate
249(2)
9.5.1 Example in R
250(1)
9.6 Regression With Two Separate Sets of Dummy Variables
251(5)
9.6.1 Example in R
253(3)
9.7 Conclusion
256(3)
10 Moderation/Interaction Analysis Using Regression
259(28)
10.1 Interaction/Moderation Effects
260(2)
10.2 Product-Term Approach
262(2)
10.3 Interaction Between a Continuous Predictor and a Dummy Moderator
264(4)
10.3.1 Example in R
266(2)
10.4 Interaction Between a Continuous Predictor and a Continuous Moderator
268(5)
10.4.1 Example in R
269(4)
10.5 Interaction Between a Dummy Predictor and a Dummy Moderator
273(3)
10.5.1 Example in R
273(3)
10.6 Interaction Between a Continuous Predictor and a Polytomous Moderator
276(6)
10.6.1 Example in R
277(5)
10.7 Additional Considerations
282(1)
10.7.1 Significant versus non-significant interactions
282(1)
10.7.2 Centring and standardization
282(1)
10.8 Conclusion
283(4)
11 Logistic Regression
287(34)
11.1 Simple Logistic Regression in R
292(9)
11.1.1 Interpretation of coefficients in logistic regression
295(4)
11.1.2 Goodness-of-fit and model selection
299(2)
11.2 Multiple Logistic Regression
301(10)
11.3 Logistic Regression for Classification
311(6)
11.4 Conclusion
317(4)
12 Multilevel and Longitudinal Analysis
321(30)
12.1 Representation of Nested Data Structures
324(5)
12.1.1 Converting between wide and long format
326(3)
12.2 Complete, Partial, and No Pooling
329(7)
12.3 Significance Testing for Linear Mixed Models
336(7)
12.3.1 Mixing fixed and random effects
340(3)
12.4 Model Comparison for Longitudinal Mixed Models
343(5)
12.5 Conclusion
348(3)
13 Factor Analysis
351(24)
13.1 What Is Factor Analysis?
352(3)
13.1.1 What is factor analysis used for?
354(1)
13.2 The Factor Analysis Process
355(8)
13.2.1 Determining the number of factors
355(1)
Eigenvalue rule
356(1)
Scree test
356(1)
Parallel analysis
356(1)
Theoretical sensitivity
356(1)
13.2.2 Extracting the factors
357(2)
13.2.3 Rotating the factors
359(3)
13.2.4 Refining and interpreting the factors
362(1)
13.3 Composite Scores and Reliability Tests
363(1)
13.4 Example in R
364(7)
13.4.1 Determining the number of factors
365(2)
13.4.2 Extracting with rotation
367(4)
13.5 Conclusion
371(4)
14 Structural Equation Modelling
375(32)
14.1 What Is Structural Equation Modelling?
376(3)
14.1.1 Types of structural equation modelling
377(2)
14.2 Confirmatory Factor Analysis
379(15)
14.2.1 Model specification
379(2)
14.2.2 Model identification
381(2)
14.2.3 Parameter estimation
383(1)
14.2.4 Model assessment
383(1)
Interpreting parameter estimates
383(4)
Model fit indices
387(1)
Chi-squared test
387(2)
Standardized root mean square residual
389(1)
Root mean squared error of approximation
390(1)
Comparative fit index
390(1)
Tucker--Lewis index
391(1)
14.2.5 Model modification
392(2)
14.3 Latent Path Analysis
394(8)
14.3.1 Specification of the LPA model
395(1)
14.3.2 Measurement part
395(4)
14.3.3 Structural part
399(3)
14.4 Conclusion
402(5)
15 Bayesian Statistics
407(30)
15.1 Bayesian Data Analysis
410(2)
15.2 Bayesian Data Analysis in R
412(2)
15.3 Example Analysis in R
414(18)
15.3.1 Model diagnostics
415(2)
15.3.2 Bayesian estimation of regression coefficients
417(5)
15.3.3 Bayesian model selection
422(5)
15.3.4 Model checking
427(2)
15.3.5 Choice of prior distribution
429(3)
15.4 Conclusion
432(5)
Bibliography 437(6)
Index 443
Mehmet Mehmetoglu is a Professor of Research Methods in the Department of Psychology at the Norwegian University of Science and Technology (NTNU). His research interests include consumer psychology, evolutionary psychology and statistical methods. Mehmetoglu has co/publications in about 35 different refereed international journals, among which include Personality and Individual Differences, Evolutionary Psychology and the Journal of Statistical Software. 

Matthias Mittner is a Professor of Cognitive Neuroscience and Head of the Research Group for Cognitive Neuroscience in the Department of Psychology at UiT - The Arctic University of Norway. His research interests include neuroscience, cognitive psychology and statistical methods. He is the author of several R and Python packages in the field of neuroscience.