Discovering Statistics Using R
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Discovering Statistics Using R

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© 2012 | 992 pages | SAGE Publications Ltd
Lecturers - request an e-inspection copy of this text or contact your local SAGE representative to discuss your course needs.

Watch Andy Field's introductory video to Discovering Statistics Using R

Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world.

The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect.

Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.

 

Why Is My Evil Lecturer Forcing Me to Learn Statistics?
What will this chapter tell me?  
What the hell am I doing here? I don't belong here  
Initial observation: finding something that needs explaining  
Generating theories and testing them  
Data collection 1: what to measure  
Data collection 2: how to measure  
Analysing data  
What have I discovered about statistics?  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Everything You Ever Wanted to Know About Statistics (Well, Sort of)
What will this chapter tell me?  
Building statistical models  
Populations and samples  
Simple statistical models  
Going beyond the data  
Using statistical models to test research questions  
What have I discovered about statistics?  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
The R Environment
What will this chapter tell me?  
Before you start  
Getting started  
Using R  
Getting data into R  
Entering data with R Commander  
Using other software to enter and edit data  
Saving Data  
Manipulating Data  
What have I discovered about statistics?  
R Packages Used in This Chapter  
R Functions Used in This Chapter  
Key terms that I've discovered  
Smart Alex's Tasks  
Further reading  
Exploring Data with Graphs
What will this chapter tell me?  
The art of presenting data  
Packages used in this chapter  
Introducing ggplot2  
Graphing relationships: the scatterplot  
Histograms: a good way to spot obvious problems  
Boxplots (box-whisker diagrams)  
Density plots  
Graphing means  
Themes and options  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Exploring Assumptions
What will this chapter tell me?  
What are assumptions?  
Assumptions of parametric data  
Packages used in this chapter  
The assumption of normality  
Testing whether a distribution is normal  
Testing for homogeneity of variance  
Correcting problems in the data  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Correlation
What will this chapter tell me?  
Looking at relationships  
How do we measure relationships?  
Data entry for correlation analysis  
Bivariate correlation  
Partial correlation  
Comparing correlations  
Calculating the effect size  
How to report correlation coefficents  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Regression
What will this chapter tell me?  
An Introduction to regression  
Packages used in this chapter  
General procedure for regression in R  
Interpreting a simple regression  
Multiple regression: the basics  
How accurate is my regression model?  
How to do multiple regression using R Commander and R  
Testing the accuracy of your regression model  
Robust regression: bootstrapping  
How to report multiple regression  
Categorical predictors and multiple regression  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Logistic Regression
What will this chapter tell me?  
Background to logistic regression  
What are the principles behind logistic regression?  
Assumptions and things that can go wrong  
Packages used in this chapter  
Binary logistic regression: an example that will make you feel eel  
How to report logistic regression  
Testing assumptions: another example  
Predicting several categories: multinomial logistic regression  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Comparing Two Means
What will this chapter tell me?  
Packages used in this chapter  
Looking at differences  
The t-test  
The independent t-test  
The dependent t-test  
Between groups or repeated measures?  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Comparing Several Means: ANOVA (GLM 1)
What will this chapter tell me?  
The theory behind ANOVA  
Assumptions of ANOVA  
Planned contrasts  
Post hoc procedures  
One-way ANOVA using R  
Calculating the effect size  
Reporting results from one-way independent ANOVA  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Analysis of Covariance, ANCOVA (GLM 2)
What will this chapter tell me?  
What is ANCOVA?  
Assumptions and issues in ANCOVA  
ANCOVA using R  
Robust ANCOVA  
Calculating the effect size  
Reporting results  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Factorial ANOVA (GLM 3)
What will this chapter tell me?  
Theory of factorial ANOVA (independant design)  
Factorial ANOVA as regression  
Two-Way ANOVA: Behind the scenes  
Factorial ANOVA using R  
Interpreting interaction graphs  
Robust factorial ANOVA  
Calculating effect sizes  
Reporting the results of two-way ANOVA  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Repeated-Measures Designs (GLM 4)
What will this chapter tell me?  
Introduction to repeated-measures designs  
Theory of one-way repeated-measures ANOVA  
One-way repeated measures designs using R  
Effect sizes for repeated measures designs  
Reporting one-way repeated measures designs  
Factorisal repeated measures designs  
Effect Sizes for factorial repeated measures designs  
Reporting the results from factorial repeated measures designs  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Mixed Designs (GLM 5)
What will this chapter tell me?  
Mixed designs  
What do men and women look for in a partner?  
Entering and exploring your data  
Mixed ANOVA  
Mixed designs as a GLM  
Calculating effect sizes  
Reporting the results of mixed ANOVA  
Robust analysis for mixed designs  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Non-Parametric Tests
What will this chapter tell me?  
When to use non-parametric tests  
Packages used in this chapter  
Comparing two independent conditions: the Wilcoxon rank-sum test  
Comparing two related conditions: the Wilcoxon signed-rank test  
Differences between several independent groups: the Kruskal-Wallis test  
Differences between several related groups: Friedman's ANOVA  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Multivariate Analysis of Variance (MANOVA)
What will this chapter tell me?  
When to use MANOVA  
Introduction: similarities and differences to ANOVA  
Theory of MANOVA  
Practical issues when conducting MANOVA  
MANOVA using R  
Robust MANOVA  
Reporting results from MANOVA  
Following up MANOVA with discriminant analysis  
Reporting results from discriminant analysis  
Some final remarks  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Exploratory Factor Analysis
What will this chapter tell me?  
When to use factor analysis  
Factors  
Research example  
Running the analysis with R Commander  
Running the analysis with R  
Factor scores  
How to report factor analysis  
Reliability analysis  
Reporting reliability analysis  
What have I discovered about statistics?  
R Packages Used in This Chapter  
R Functions Used in This Chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Categorical Data
What will this chapter tell me?  
Packages used in this chapter  
Analysing categorical data  
Theory of Analysing Categorical Data  
Assumptions of the chi-square test  
Doing the chi-square test using R  
Several categorical variables: loglinear analysis  
Assumptions in loglinear analysis  
Loglinear analysis using R  
Following up loglinear analysis  
Effect sizes in loglinear analysis  
Reporting the results of loglinear analysis  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Multilevel Linear Models
What will this chapter tell me?  
Hierarchical data  
Theory of multilevel linear models  
The multilevel model  
Some practical issues  
Multilevel modelling on R  
Growth models  
How to report a multilevel model  
What have I discovered about statistics?  
R packages used in this chapter  
R functions used in this chapter  
Key terms that I've discovered  
Smart Alex's tasks  
Further reading  
Interesting real research  
Epilogue: Life After Discovering Statistics
Troubleshooting R
Glossary
Appendix  
Table of the standard normal distribution  
Critical Values of the t-Distribution  
Critical Values of the F-Distribution  
Critical Values of the chi-square Distribution  
References

Sample Materials & Chapters

Chapter One


Supplements

Companion Website

Companion Website to accompany Discovering Statistics Using R

"This work should be in the library of every institution where statistics is taught. It contains much more content than what is required for a beginning or advanced undergraduate course, but instructors for such courses would do well to consider this book; it is priced comparably to books which contain only basic material, and students who are fascinated by the subject may find the additional material a real bonus. The book would also be very good for self-study. Overall, an excellent resource."

R. Bharath
Northern Michigan University
Choice

In statistics, R is the way of the future. The big boys and girls have known this for some time: There are now millions of R users in academia and industry. R is free (as in no cost) and free (as in speech). Andy, Jeremy, and Zoe's book now makes R accessible to the little boys and girls like me and my students. Soon all classes in statistics will be taught in R.

I have been teaching R to psychologists for several years and so I have been waiting for this book for some time. The book is excellent, and it is now the course text for all my statistics classes. I'm pretty sure the book provides all you need to go from statistical novice to working researcher.

Take, for example, the chapter on t-tests. The chapter explains how to compare the means of two groups from scratch. It explains the logic behind the tests, it explains how to do the tests in R with a complete worked example, which papers to read in the unlikely event you do need to go further, and it explains what you need to write in your practical report or paper. But it also goes further, and explains how t-tests and regression are related---and are really the same thing---as part of the general linear model. So this book offers not just the step-by-step guidance needed to complete a particular test, but it also offers the chance to reach the zen state of total statistical understanding.


Prof. Neil Stewart
Warwick University


Field's Discovering Statistics is popular with students for making a sometimes deemed inaccessible topic accessible, in a fun way. In Discovering Statistics Using R, the authors have managed to do this using a statistics package that is known to be powerful, but sometimes deemed just as inaccessible to the uninitiated, all the while staying true to Field's off-kilter approach.


Dr Marcel van Egmond
University of Amsterdam


Probably the wittiest and most amusing of the lot (no, really), this book takes yet another approach: it is 958 pages of R-based stats wisdom (plus online accoutrements)... A thoroughly engaging, expansive, thoughtful and complete guide to modern statistics. Self-deprecating stories lighten the tone, and the undergrad-orientated 'stupid faces' (Brian Haemorrhage, Jane Superbrain, Oliver Twisted, etc.) soon stop feeling like a gimmick, and help to break up the text with useful snippets of stats wisdom. It is very mch a student textbook but it is brilliant... Field et al. is the complete package.
David M. Shuker
AnimJournal of Animal Behaviour



The main strength of this book is that it presents a lot of information in an accessible, engaging and irreverent way. The style is informal with interesting excursions into the history of statistics and psychology. There is reference to research papers which illustrate the methods explained, and are also very entertaining. The authors manage to pull off the Herculean task of teaching statistics through the medium of R... All in all, an invaluable resource.

Paul Webb
Research Officer, Praxis Care, Belfast

Although it is more for social scientists, the book introduces both R and statistics. It is what I needed.

Dr Craig Whippo
Natural Science Dept, Dickinson State University
June 20, 2015

Very good explanations for fundamental concepts and decent coverage of R.

But the book has too much trivial and annoying trivial personal stories that makes me want to change it at the first opportunity in the future.

Mr Ali Sanaei
School Of Information, Univ Of California-Berkeley
April 11, 2015

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