### Serious Stats: Obtaining CIs for Spearman’s rho or Kendall’s tau

I wrote a short blog (with R Code) on how to calculate corrected CIs for rho and tau using the Fisher z transformation.Serious Stats blog post on CIs for rho and tau [Read more...]

I wrote a short blog (with R Code) on how to calculate corrected CIs for rho and tau using the Fisher z transformation.Serious Stats blog post on CIs for rho and tau [Read more...]

There are various methods for obtaining CIs for Kendall’s tau and Spearman’s rho. As the underlying data are unlikely to be bivariate normal (or else Pearson’s r would be used) bootstrapping is often recommended – but it doesn’t always perform that well (Bishara & Hittner, 2017). One could also ... [Read more...]

Looking back at the coverage of the Chi-Square test of independence in the book there are a couple of things I wish I’d gone into greater depth on. First, resolving the debate on the appropriate way to handle small expected values in the test of independence. Second, expanding on ... [Read more...]

In the book I use the car package to get VIF and other multicollinearity diagnostics. I’ve occasionally found this breaks down (usually through mixing different versions of R on different machines at work home or on the move). I recently saw the mctest package and thought it would be ... [Read more...]

In an earlier blog post I provided R code for a CI of a difference in R square for dependent and non-dependent correlations. This was based on a paper by Zou (2007). That paper also provides a method for calculating the CI of a difference in independent R square coefficients based ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 (... [Read more...]

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 (... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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