It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can 
One of the main attractions of R (for me) is the ability to produce high quality graphics that look just the way you want them to. The basic plot functions are generally excellent for exploratory work and for getting to know your data. Most packages have additional functions for appropriate exploratory work or for summarizing 
In a previous post I showed how to plot difference-adjusted CIs for between-subjects (independent measures) ANOVA designs (see here). The rationale behind this kind of graphical display is introduced in Chapter 3 of Serious stats (and summarized in my earlier blog post). In a between-subjects – or in indeed in a within-subjects (repeated measures) – design 
When starting out with R, getting data in and out can be a bit of a pain. It should take long to work out a convenient method – depending on what OS you use and what other packages you work with. In my case I prefer to work with Excel spreadsheets (which are versatile and 
Whilst writing the book the latest version of R changed several times. Although I started on an earlier version, the bulk of the book was written with 2.11 and it was finished under R 2.12. The final version of the R scripts were therefore run and checked using R 2.12 and, in the main, the most recent 
UPDATE: Some problems arose with my previous host so I have now updated the links here and elsewhere on the blog. The companion web site for Serious Stats has a zip file with R scripts for each chapter. This contains examples of R code and and all my functions from the book (and a few extras). 
The companion web site for Serious stats is now live: http://www.palgrave.com/psychology/Baguley/ It includes a sample chapter (Chapter 15: Contrasts), data sets, R scripts for all the examples and supplementary material. Filed under: news, R code, ser...
In Chapter 2 (Confidence Intervals) of Serious stats I consider the problem of displaying confidence intervals (CIs) of a set of means (which I illustrate with the simple case of two independent means). Later, in Chapter 16 (Repeated Measures ANOVA), I consider the trickier problem of displaying of two or more means from paired or 
In section 10.4.4 of Serious stats (Baguley, 2012) I discuss the rank transformation and suggest that it often makes sense to rank transform data prior to application of conventional ‘parametric’ least squares procedures such as t tests or one-way ANOVA. There are several advantages to this approach over the usual approach (which involves learning and applying a new test such as Mann-Whitney U, 
In Chapter 6 (correlation and covariance) I consider how to construct a confidence interval (CI) for the difference between two independent correlations. The standard approach uses the Fisher z transformation to deal with boundary effects (the squashing of the distribution and increasing asymmetry as r approaches -1 or 1). As zr is approximately normally distributed 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
