Discovering Statistics Using R
Discovering Statistics Using R book cover

Discovering Statistics Using R

1st Edition

by
Price
$48.88
Format
Paperback
Pages
992
Publisher
SAGE Publications Ltd
Publication Date
ISBN-13
978-1446200469
Dimensions
7.6 x 1.6 x 10.4 inches
Weight
5.07 pounds

Description

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 StewartWarwick 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 ― Choice Published On: 2012-12-01The 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 Published On: 2013-12-12 Andy Field is Professor of Quantitative Methods at the University of Sussex. He has published widely (100+ research papers, 29 book chapters, and 17 books in various editions) in the areas of child anxiety and psychological methods and statistics. His current research interests focus on barriers to learning mathematics and statistics. He is internationally known as a statistics educator. He has written several widely used statistics textbooks including Discovering Statistics Using IBM SPSS Statistics (winner of the 2007 British Psychological Society book award), Discovering Statistics Using R , and An Adventure in Statistics (shortlisted for the British Psychological Society book award, 2017; British Book Design and Production Awards, primary, secondary and tertiary education category, 2016; and the Association of Learned & Professional Society Publishers Award for innovation in publishing, 2016), which teaches statistics through a fictional narrative and uses graphic novel elements. He has also written thexa0adventrxa0andxa0discovrxa0packages for the statistics software R that teach statistics and R through interactive tutorials. His uncontrollable enthusiasm for teaching statistics to psychologists has led to teaching awards from the University of Sussex (2001, 2015, 2016, 2018, 2019), the British Psychological Society (2006) and a prestigious UK National Teaching fellowship (2010). He′s done the usual academic things: had grants, been on editorial boards, done lots of admin/service but he finds it tedious trying to remember this stuff. None of them matter anyway because in the unlikely event that you′ve ever heard of him it′ll be as the ′Stats book guy′. In his spare time, he plays the drums very noisily in a heavy metal band, and walks his cocker spaniel, both of which he finds therapeutic. Jeremy Miles , RAND Corporation, USA. Zoë Field , University of Sussex, UK

Features & Highlights

  • The R version of Andy Field′s hugely popular
  • Discovering Statistics Using SPSS
  • takes students on a journey of statistical discovery using the freeware R. Like its sister textbook,
  • Discovering Statistics Using R
  • is written in an irreverent style and follows the same ground breaking structure and pedagogical approach. The core material is enhanced 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.

Customer Reviews

Rating Breakdown

★★★★★
60%
(321)
★★★★
25%
(134)
★★★
15%
(80)
★★
7%
(37)
★
-7%
(-37)

Most Helpful Reviews

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The Best Introduction to Statistics Available

Andy Field writes some of the most intuitive and entertaining accounts of statistics available, and this book is no exception to that standard. This book is geared towards those who want to start from the beginning and progress through a complete account of the most common methods in statistics based on the general linear model. If you are a beginner, this is one of the best places to start. If you are experienced, this book is a great reference to have around.

The most enjoyable aspect of this book, aside from its humor, is that Field addresses issues of using robust statical methods when assumptions are not met in the data. Instead of glossing over the issues, Field provides the most recent findings in the field and even examples of how to run robust tests in R. However, note if you want to do something very complicated with robust methods, this book is not a cure all, and you would be hard pressed to find one that is.

With regards to R, this book will get you up and running with R even if you have no previous experience with R or programming languages in general. However, a few of the R libraries have changed since this edition's publication, so you will need to search a bit to fix a few errors, but it's not hard and is good practice.

Finally, I must mention that Andy Field has gone out of his way to provide datasets and examples like no other author I have encountered. The book has a companion website full of these datasets and all of the R scripts used in the book. Additionally, the companion website is packed full of extra material for each chapter in the book. Finally, Field has several videos posted to the website which includes a lecture series on statistics.
34 people found this helpful
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Very good for R - perhaps less good for statistics

I chose this book because I wanted to learn R through a progression of examples, and for that it has served well. I would recommend it for that purpose. My understanding of statistics, though, has not progressed as much as my R programming.

Specifically, I struggled in almost every chapter with "assumptions" - the criteria that must be met to justify the use of a particular statistical method. How important is each one? Why is it important? What exactly is it testing for, or trying to prevent you from doing?Close reading of the text was usually unhelpful to my understanding, and I frequently had to turn to outside sources. After a fair amount of struggle with the topic, here's what I've learned:

Basically, this issue is confusing because the whole idea of assumption checking is a simplification born out of the rise of statistics software. Which allows people like me, with limited mathematical literacy, to blithely run lots of analyses using wildly inappropriate and mis-specified models, then report the results as if they were something other than meaningless noise. If I actually understood how the models work, when they fail, and how to choose a meaningful specification, then I wouldn't need strict guidelines for assumption checking.

The problem is that the simplification is an oversimplification. A checklist of assumptions is no substitute for an understanding of the reasoning behind the modeling techniques. There will be cases where the model is appropriate even if you fail an item on the checklist, and cases where the model is inappropriate even if your data happens to check all the boxes. A checklist will never tell you that there's a better technique for your purposes. And it will always be hard to get a straight answer to questions like "how much is too much?" because the checklist thresholds are arbitrary in the first place, and therefore constantly open to debate.

Field is very little help in understanding how the models work or why they fail. Instead he takes a cookbook approach that mostly amounts to glorifying checklists. To the point that I suspect at least one of his chapters - on analysis of covariance - uses a mis-specified model as its core example (though it checks all the checklist boxes). It certainly doesn't look much like the appropriate situations for the technique according to outside sources, including some sources he cites in his bibliography.

I learned quite a bit of R. But if I want more than a surface understanding of statistics, I'm going to need another book.
26 people found this helpful
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I found a Positive Correlation between Andy Field and Awesomeness

Andy Field simply has a way with making a scary subject- statistics (dare I say it) fun.

This book is an excellent step-by-step guide that a beginner will feel comfortable with, but also serves as a great reference for someone like me, who has a background in stats. More importantly, it shows how to perform various statistical analyses using R - which is a great & highly in demand skill. When you buy this book, you have access to example data sets and supplemental material - it's as if Andy is there to help you along the way. If you are freaked out about learning stats, or feel like you don't understand the theory behind how the tests work and when you should perform certain ones, then this book is you.

Admittedly, I'm a nerd, so the fact that I actually do enjoy reading the chapters and working the examples might be biased. It's possible that the general population might disagree with me. I do, however, think that Andy's sense of humor and clear examples, rather than my own nerdiness, explains more unique variance in predicting why I so highly recommend this book. Even if you're dreading learning stats, I think this book will show you how much fun it can be- or at least that you don't have to be a math wiz to get the basic concepts. Five stars all the way!
25 people found this helpful
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OK for basic statistics but poor coverage of R programming

As introductory textbook to classical/frequentist statistics, this book could be useful, especially for readers who desire lengthy explanatory vignettes and are unwilling to invest the effort to learn programming. Still, I do not recommend this book for most. Despite the title, it does not effectively teach the R language. I believe it is a mistake to learn statistics without simultaneously developing programming skills, and the author's use of R is rudimentary at best. Your favorite spreadsheet program or point-and-click stats software can crunch T tests just as well as R, and so the programming novice who reads this book may well ask why they should be pulling out their hair struggling to make code work in R, when the examples in the book would work just as easily in Excel or SPSS.

I purchased this book as well as [[ASIN:1593276516 The Book of R: A First Course in Programming and Statistics]], and highly recommend the latter. With the Davies book I was able to acquire a basic competency in R code, and then learn statistics using my newfound programming knowledge. Many of the examples and exercises utilize R programming to solve problems that are impractical/impossible with commercial statistical software.

Pros:
- Good explanations of fundamental topics like ANOVA
- Examples that address the common dilemma of which particular test or model to use
- Lots of examples/vignettes to frame each chapter and give context
- Quirky, personal writing style may appeal to some

Cons:
- Pages started falling out literally the first time I opened the brand-new book
- Verbose; almost every page is riddled with irrelevant personal anecdotes
- Writing is often bizarrely risqué; seems every chapter has multiple references to male sexual function
- Not a good introduction to R programming (see above)
18 people found this helpful
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Great Stats Book!

I loved the SPSS version of this book, and I was so excited to see the R version. Everyone who has attempted to learn R knows that there is a HUGE learning curve. Andy, Jeremy, and Zoë have found a way to make it so much easier with step-by-step code explanations and lots of humor. The companion site is great, too! Thank you for making stats enjoyable, and thank you for the laughs!
18 people found this helpful
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Great Value and concise, pragmatic explanations

I purchased this book in order to answer some stats questions surrounding my research because I've nearly forgotten all of what I learned from the one undergrad stats course I took yeas ago. Having worked through several chapters of the book, I was inclined to write a review for several reasons:
1) it's a great value - it begins with very basic stats and goes through mixed model analysis. Given it costs about $80 new, this is an exceptional value in terms of breadth of content.

2) it's taught in the context of R - IMO, statistics ought to be taught in the context of how one will actually carry out the modeling calculations; it is done this way throughout the book.

3) the binding is nice and the online material is easily accessible.

4) Andy does a good job with introducing the (sometimes quirky) nature of R.

5) While the depth of content may be too shallow for complicated problems (it's not a theory book by any means), this is made up for by the inclusion of "further reading" at the end of each chapter where Andy lists and explains why each resource was selected and what he believes it best explains. I used this when diving into mixed model repeated measure ANOVAs.
17 people found this helpful
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Don't buy the Kindle Version because It is impossible to read.

Don't buy the Kindle Version because It is impossible to read.
16 people found this helpful
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Great For its Kind

Even in my own student days, statistics teaching was split. A course of top quality (for a particular school) would be offered in the mathematics department. At schools with greater resources, a whole separate department would be devoted to statistics, and they would teach the top quality course. These courses were always very worthwhile, although usually quite demanding. Other courses, ostensibly at a similar level, were taught by other faculties, usually in the social sciences. By constructing their own courses, the faculties in social sciences would have the chance to fit the details of the curriculum more precisely to their own context. At least, that was the theory. In actual fact, at the top universities (Stanford, Princeton, etc.), this was exactly the case. A high quality course of special content would be offered in the social sciences context. Not every university is Stanford. At lesser universities, the alternate course was usually hugely watered down. It was simplified disastrously, mainly so that the students from those courses would stand a chance to pass the course. It was, in many instances, pitiful indeed.

Times have changed. This book is written for the courses that used to be pitiful, offered in the social sciences departments. The book is extremely long and very chatty. While I myself don't connect in every instance to the sense of humor, it is very valuable pedagogically that the authors write with a sense of humor. They are very wordy, and that can be very good. They allow the student time to think through the issues well and at an appropriate pace. This is in contrast to some more "mathy" books that expect the student to see those implications independently. So, if you are trying to prepare students in their first statistics course to go from ground zero to being able to generate "research papers" using the standard statistics correctly and with some facility, then this is really a good book. It is careful, and it is correct. If used by diligent students at a very good university (or, for that matter, diligent students at any university), it can give them a very big advantage. It's easy to understand. It's clear. It's friendly to them and will help very much in establishing a first level of very practical skills.

On the other hand, it is not very good for students who lack a little diligence. For example, in the first chapter, the normal distribution is introduced by saying that it is a symmetric bell curve. That's entirely true, of course, but leaves open a number of important points of confusion. These are all gotten straight in later chapters. So, it is a gradual revelation. However, it also leaves a period of time during the learning when confusion is not suppressed as it might well be. For students who are not as diligent, it is likely that the confusions corrected a hundred and fifty pages later might persist.

Also, this is a little bit of a cook book approach. The part that goes missing between the math course and the social sciences course is the underpinnings of how statistical tests can be chosen an structured---the why they work part. That is not entirely absent, of course, but the "user friendliness" desired by many faculty, essentially all students, and absolutely all commercial publishers sweeps this aside too much, at least in my opinion.

I am currently tutoring from this book for a "master's" level social sciences course at a nearby "university" (not Stanford). While the book is very good, it is not really having a good effect on the students in that class. The students don't even bother to read the book in a timely way. They just simply show up and ask for a verbal summary of the "big ideas." In a sense, it is user friendly enough to let people get the idea that they need not work to learn. That is never the case, although it is also not the fault of the authors or even of the publishers (always more the villain).

This is a really good book for its audience, for its kind. If you foresee taking many statistics courses, this is a kind and gentle way to get started. If, however, you have limited curriculum time to devote to statistics, it might be better to clench your teeth and go to the real mathematics or real statistics department for a more detailed, if less palatable, course. In the end, you may eventually forget these things either way.
12 people found this helpful
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missing example files?

Unfortunately, many of the files referred to during the examples are missing on the companion website (i.e. . This makes it difficult (actually impossible for someone whose bad at stats) to follow the examples in the book. I do appreciate the authors humor though!
8 people found this helpful
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A great intro to R and stats with some easily corrected flaws

I must start by saying that this is an excellent book and one of the most approachable that I've found for teaching students new to R. This could be a five-star book if the author would consider a few things, many of which would shorten the length of the book considerably or as an even better alternative the author could place even more code and data examples in the book with the saved space:

1) Ditch the discussion and elements of using R commander. This is a hold-over GUI need from the author's last book on SPSS. Learning R well means getting into the code and the command line and staying there. GUI based add -on's like R commander just get in the way and you cannot even run all of the available stats in it anyway. Users are buying this book to learn the code, so teach them the code only!

2) Dispense with the witty banter. The author clearly has a sense of humor and likes it, but there are needles pages in the book, essentially the start of each chapter, that are just silly asides that serve no purpose and take up a LOT of space. The humorous examples for many of the datasets are great and a welcome change to a stuffy stats book but the excessive chatter and jokes end up taking up more space that could be used for more practical examples or hints for the new R user. I also found these witty asides distracting once I was knee-deep in the methods of running a test.

3) Dispense with the repetitive instructions and inefficient methods. Do we really need to see the code for loading a new data set over and over? Does that data set really need to be a .dat file? It would be better if the author would stick with conventional excel files (.csv) that most people are commonly going to use. Provide an early chapter for getting data into R and move on already!

If you are coming at this topic from the life sciences (biological, ecological etc.) the author tends to favor planned contrasts a lot more than post hoc multiple comparisons which are much more common in these sciences. I would strongly recommend that you buy this book, but find a good bio stats book to read in tandem so you can get your head around more biological examples.
6 people found this helpful

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