I am delighted to announce the publications of my statistics and data analysis book, ‘From Questions to Knowledge: Data Analysis for Psychology and Behavioural Science Using R’. This book represents a combination of: a statistics course; an introduction to coding in R; and a guide to good practice (as I see it) for study design and open science. It presents a series of practical tutorials, all of which use real data sets from recently published papers in psychology or behavioural science.
Chapters cover (among other things): the basics of R; the General Linear Model; statistical tests including equivalence testing; Generalized Linear Models and Linear Mixed Models; ANOVA; making figures with ggplot2; AIC-based model selection; specification curve analysis; meta-analysis; simulation; statistical power; pre-registration; and developing an analysis strategy.
You can either access the free web version; or you can buy yourself a natty, inexpensive paperback through Blackwells (ships globally); Amazon (likewise); bookshop.org (USA only); or other book retailers. Or why not do both?
The book consolidates the data analysis teaching I have developed over the last decade; the training I have tried to give graduate students who have worked with me; and the lessons I have drawn from having done the data analysis for over a hundred studies (and having tried to do it better and better as I learned more)
If I had to summarise what I was aiming for in writing this book, I would compare its intent to that of the hand-books for the mechanical arts that were published by skilled artisans in early modern Europe. These are described by Lorraine Daston in her 2022 book Rules. You could find such hand-books for all kinds of skills: painting with perspective; mining; starting a pig farm; dredging a canal; dyeing; cookery; musical composition; surveying; and besieging a town.
These hand-books have some characteristic features, which seem applicable to the present book too. They cover an area which is not an exact science, but not a free art or unsystematic craft either. Rather, the area contains some exact underpinning principles, and rules of good practice that can be formulated and transmitted, but must be applied with judgement and discretion. The hand-books were written by experienced practitioners in the domain, often autodidacts, who had a yearning to coalesce and pass on the lessons they had taught themselves over the years. I am a working empirical researcher who does his own data analysis, rather than a statistical mathematician or computer scientist. I am sure this shows in the book, for better or worse.
The hand-books were, above all, designed for readers who wished to practice the skill. They were written with the intention that the consumer would alternate between reading on the page, and trying out the suggestions for themselves. That is true for this book. It provides links to datasets from real studies, and code suggestions for how to go about analysing them. People will get more out of the chapters if they try out the code examples themselves and play around with variations. And, I encourage readers to analyse their own datasets as they go through. You make most rapid progress with data analysis once you have data of your own to analyse. That is when the art starts to seem most relevant, and developing the skill most gratifying. The point when you are doing data analysis on your own data is the point where you understand how many different options there are, and the obstacles you can come up against. It is the point where you are most likely to want to refer back a hand-book like this.