Discovering Statistics Using R
- Andy Field - University of Sussex, UK
- Jeremy Miles - RAND Corporation, USA
- Zoë Field - University of Sussex, UK
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.
Supplements
Prefer cheaper eBook of same author, if available.
core text for Quants research
I really liked this book but unfortunately, it was too advanced for the course I am currently teaching.
Again, Andy Field has produce a masterpiece of the 21th Century! Before I read this book, R to me is so scary, but now, after reading this book, R is just like a cheesecake to me!
This book has some very nice features that make using R and dealing with statistics a bit more fun for students.
The flexibility of R, great examples by Andy Field, consistency in materials between SPSS and R makes the shift from SPSS to R easier.
This is a good book. I adopted this book as the supplemental and another book written by the same author, Discovering Statistics Using SPSS, in my graduate research methold course. Students could easy to compare the same method but different usages, and they could transfer SPSS to R easily. Finallly, this is a very good pratcical and reference book.
Just like the SPSS book it is fun to read and a good overview. BUT: although R is a free and increasingly wide-spread, it is sometimes a little hard to get used to it, especially if you switch from some other software package (S...).
So, I just recommend this book for some procedures (robust tests etc.) that can not be done in SPSS and for those of my students who like to learn using R which I strongly encourage. For this endeavor this book is a great help. So it's not the book's fault that it just supplemental material in my course, but the time is not yet ripe for a full switch to R in our department.
This is an engaging and entertaining book and has to be one of the easiest to follow.
I like the book very much and I recommended it to the students who want to switch to R for all their statistical needs. However, currently, most statistics are still taught in SPSS at the BSI at Radboud University and only the "mixed/multilevel" course was taught in R. The problem with the respective chapter in this otherwise great book is that Andy Fields explains the use of the nlme library, which is a somewhat outdated/older library for that type of model. For a while, there has now been a newer package (lme4; actually by the same developer(s), which is more advanced). Accordingly, I taught the students how to use lme4 and therefore the book was not as helpful for mixed/multilevel models as it could have been.
