Discovering Statistics Using R
Share

Discovering Statistics Using R

Companion Website


© 2012 | 992 pages | SAGE Publications Ltd

Watch Andy talk about the new version of his book for R: click here!

Hot on the heels of the award-winning and best selling Discovering Statistics Using SPSS Third Edition, Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and self-depreciating 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 the freeware R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioral sciences throughout the world.

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

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, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more (at www.uk.sagepub.com/dsur/).

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.


Available with
 Perusall—an eBook that makes it easier to prepare for class
Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more
 
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

Companion Website

Companion Website to accompany Discovering Statistics Using R

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 a marvelous tool to get to know not only the basics in statistics, but also some intermediate and complex analyses. Its structure allows students to grasp concepts, processes and actual uses of the techniques. I can forget about the usual fear experimented by students when starting on a statistics course. An excellent way to start using a powerful tool such as R.

Dr Erwin Rogelio Villuendas-Gonzalez
Psychology , UNIVERSIDAD MICHOACANA DE SAN NICOLAS DE HIDALGO
September 29, 2016

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

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


Preview this book

For instructors

Purchasing options

Please select a format:

ISBN: 9781446200469
ISBN: 9781446200452