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Discovering Statistics Using R
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Discovering Statistics Using R

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March 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

Supplements

Click for online resources

Companion Website to accompany Discovering Statistics Using R

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



"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

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

This textbook is easy-to-understand, and it is written in a humorous way. It has all the information needed for an easy comprehension of statistical intricacies. The book also comes with vast database that can be used by students to test their knowledge of the material, and/or how to operate R.

Catalin Pavel
Business Administration Dept, Southwestern Adventist Univ
February 26, 2019

As I'm sure you know, R does more sophisticated statistical techniques for free that mainstream packages do not and so is potentially useful to the doctoral students I teach and supervise. An intermediate step is to use the R links in Field's SPSS book but in the end it is necessary to get to grips with R and this book is a relatively painless way of doing this (especially if you and/or your students are familiar with Fields SPSS book).

Ms Linda Morison
School of Psychology, Surrey University
February 7, 2018

Discovering Statistics using R is an excellent book to engage students in learning statistics using top of the line software. The content is presented in a clear and coherent way, and the exercises help reinforce and consolidate knowledge in quite a funny way. It is great material for teaching and learning, but also a handy reference book for researchers.

Miss Maria Barrera
Facultad de Ciencias Politicas, National Autonomous Univ of Mexico
April 18, 2017

As an open source software and with a vast supporting community, R is increasingly adopted by researchers. Discovering Statistics Using R allows a soft transition from other statistical softwares to this open source alternative.

Dr Teresa C. D'Oliveira
Psychology , King's College London
December 3, 2016

Clear and easy to use as an alternative to using SPSS for my psychology students

Dr Alyson Lamont Dodd
Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University
November 16, 2016

This book covers the material we need, with plenty of exercises, accessible explanations. Most importantly, it describes and teaches the R statistics platform integrated with the rest of the text.

Professor Mikael Vejdemo-Johansson
Mathematics Dept, Cuny College Of Staten Island
October 31, 2016

This textbook is quite thorough, but the overall style of the writing would not land very well to my California audience unfortunately.

Professor Daniel Pinedo
Social Sciences Division, Mount Saint Mary College
October 4, 2016

Sample Materials & Chapters

Chapter One


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