Conditions of Use
This books covers the fundamentals in both statistics and R programming. I would suggest add a little touch of Bayesian statistics in the section of the Stats Theory given the broad application of Bayesian inference in psychology. read more
This books covers the fundamentals in both statistics and R programming. I would suggest add a little touch of Bayesian statistics in the section of the Stats Theory given the broad application of Bayesian inference in psychology.
Content is accurate, error-free and unbiased in my opinion.
Contents are up-to-date.
Delivery of statistical theories in this book is clear, with the support of reproducible examples and relevant practice questions.
Terminology is consistent throughout the book.
Each chapter is divided into accessible modules that can be assigned to the course segments by demand.
It would be better if the author could add a brief "landmark" in the beginning to help the readers decide: Which chapters I need to read If I want to learn X given Y time? e.g. Which chapters to read If I want to learn about chi square test so that I can i) work on a dataset and interprets the results on next week's presentation or ii) develop in-depth understanding of the analysis and use it in my thesis in half a year.
Interface is clear and compact, my favorite style!
The text contains no grammatical errors to the best of my knowledge.
I feel the text is not culturally insensitive or offensive in any way.
The book did a very good job of gently working students up to analyses in R. The text was clear and incorporated existing datasets that students (and faculty) could use to engage in hands-on learning. read more
The book did a very good job of gently working students up to analyses in R. The text was clear and incorporated existing datasets that students (and faculty) could use to engage in hands-on learning.
Because of the plethora of R packages, there will always be some discussion about which packages are "easier" to use or more appropriate for particular sections. However, this text does a good job of guiding students toward the construction of an R package toolbox that is appropriate for social science analysis.
The only significant changes that are likely to be needed in the book are alterations to, or selection of, different packages if and when they arise. Those changes should be relatively easy to make.
This text is very clear. There is some jargon with R that one must become accustomed to, but once students understand the jargon (which is really essential to understanding how the R environment works) the text is clear and easy to understand.
No real comments here. The text is as consistent as one would expect from a book teaching students statistics in R.
The text has clear section delineation. As is true of many "beginner" texts teaching a particular statistical platform, the units largely build off one another. So, although the sections are very well delineated, I would not recommend rearranging the chapters as that would likely not benefit students.
The structure of the book made good sense and made R feel more accessible.
My one comment would be a link (at the end of sections or chapters) to take the reader back to the Table of Contents.
No significant errors.
I did not see any cultural insensitivities in my review of the book.
This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of... read more
This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of undergraduate behavioral science statistics -- descriptive statistics, probability, analysis of variance, regression, and a very welcome chapter on Bayesian approaches-- plus how to implement a lot of data description and analysis in R, including step-by. It is a effective and useful mashup of these two topics. It does not have an index or glossary. I did not miss them, as it’s easy to use the search function to find the first instance of any term within the text, and Navarro is very good about defining and contextualizing new terms clearly as she goes.
Version 0.6 appeared free of errors as far as I could see. Furthermore, it did a nonpartisan job of framing debates that are throwing out a lot of light at the moment (such as the debates between proponents of frequentist vs. Bayesian approaches). Navarro’s approach is exemplary: she carefully contextualizes the issues at stake, explains why she feels as she does, and provides useful resources to follow up in more detail.
The content of the book seems up-to-date; indeed, at the moment I write this, the book has emerged as a common recommendation on social media for those hoping to learn R, so it’s clear that it is broadly seen as relevant. Navarro has already implemented several revisions, showing that necessary updates are easy and straightforward to incorporate. The core information in the book (statistics) is nearly timeless and should not need constant updating.
Clarity is absolutely paramount when one is attempting to learn a new skill -- or to learn two new skills, R and statistics, as is envisioned here. Navarro is an extremely useful guide to this process: it’s as if she takes your hand and walks you through step by step, so that learning these new skills is quite painless. Version 0.6 features clear and carefully chosen examples, no doubt honed over the prior versions. As noted above, new terms are clearly defined (almost obviating the need for a glossary or index).
Navarro has thought carefully about when/where and how to introduce new concepts, and then is thoughtful in using them consistently. She includes summary sections at the end of each chapter that are more helpful than ‘typical’ summaries, and useful sample R code is provided where appropriate.
Where possible, the book is modular. For example, a reader who is reasonably competent in statistics but using the book to learn R would have no trouble using the Table of Contents and closing chapter summaries to jump right to a specific section (say on graphing) that captures what they need to learn. A reader who is trying to use the book to learn statistics would be best advised to go in order, as later topics build on earlier ones and trying to do an end-run around this organization is ill-advised, but this is not a flaw of the text per se; it is inherent in learning about the topic.
The book is organized carefully and intentionally. There is a ‘received organization’ to most texts that introduce behavioral statistics, building in terms of complexity (for example, covering t-tests before moving to analysis of variance). The book follows this in the relevant sections (most of its second half). There was more scope for choice and design in the book’s first half, which introduces all of the topics the reader will need to understand before diving into implementing statistical learning in R. Here, Navarro has done a fantastic job of making choices that are friendly to the reader.
The text is rendered as a PDF, and everything is laid out quite cleanly, with helpful clickable links in the Table of Contents to each section.
The text was very competently copy-edited (despite being still being in, as it were, beta-testing) and did not appear to contain any unintentional errors.
I found nothing culturally insensitive or offensive in the text. The author is not based in North America, which was occasionally lightly apparent, which I consider all to the good.
A valuable asset of this book is its congenial tone. Navarro is chatty and funny, sometime even a bit irreverent, and the reader benefits quite a bit from this well calibrated conversational tone.
Table of Contents
- Chapter 1: Why do we learn statistics?
- Chatper 2: A brief introduction to research design
II. An introduction to R
- Chapter 3: Getting started with R
- Chapter 4: Additional R concepts
III. Working with data
- Chapter 5: Descriptive statistics
- Chapter 6: Drawing graphs
- Chapter 7: Pragmatic matters
- Chapter 8: Basic programming
IV. Statistical theory
- Chapter 9: Introduction to probability
- Chapter 10: Estimating unknown quantities from a sample
- Chapter 11: Hypothesis testing
V. Statistical tools
- Chapter 12: Categorical data analysis
- Chapter 13: Comparing two means
- Chapter 14: Comparing several means (one-way ANOVA)
- Chapter 15: Linear regression
- Chapter 16: Factorial ANOVA
VI. Other topics
- Chapter 17: Bayesian statistics
- Chapter 18: Epilogue
About the Book
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
About the Contributors
Danielle Navarro, PhD is a computational cognitive scientist at the University of New South Wales. Her research focuses on human concept learning, reasoning and decision making. She is also interested in language and cultural evolution, cognitive development, and statistical methods in the behavioural sciences