In a break from my usual obsessions and interests here is a guest blog post by Ian Walker. I'm posting it because I think it is rather cool and hope it will be of interest to some of my regular readers. Ian is perhaps best known (in the blogosphere) for his work on transport psychology - particularly cycling - but is also an expert on psychological statistics.

Some time ago, I had some data that lent themselves to a three-dimensional surface plot. The problem was, the plot was quite asymmetrical, and finding the right viewing angle to see it effectively on a computer screen was extremely difficult. I spent ages tweaking angles and every possible view seemed to involve an unacceptable compromise.

I wasn't going to post on this ... but couldn't resist. A recent QJEP paper reports suspicious patterns in p values across three psychology journals.

This has been blogged elsewhere (see here and here), so I haven't got too much to add. Although I generally like the paper and am glad it got published in a decent journal (I'm an EPS member and subscriber so I'm glad they published it), I can't say I find the main finding surprising.

My serious stats book is officially published (in the UK at least). The US release date is next month (August 7th). I'm not sure why the release is later (possibly extra shipping time for the books). The earlier European release date is I suppose compensation for the usual pattern for most books and DVDs (where the US is usually first).

More details on the companion blog for the book. The book blog also contains additional resources related to the book and updates on topics covered in the book.

I've been thinking about p values quite a bit recently - prompted by a other bloggers and by some journal work. One interesting phenomenon in this area is the cliff effect: a supposed abrupt shift in researchers' confidence in an effect when moving from p > .05 to p < .05 (assuming that alpha is set at .05, as it usually is).

The p value cliff effect is interesting for a number of reasons (e.g., as a possible cause of dichotomous thinking about effects - see Rosenthal & Gaito, 1963).

Neuroskeptic has just blogged on a new paper by Judd, Westfall and Kenny on Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. I can't access the original paper (which is supposed to be available via my University but hasn't appeared yet ...) but I know a little bit about the topic and thought I'd write a few words.

This blog and my other main blog (the companion blog for my book) are now syndicated via R-bloggers (posts tagged R only) and statsblogs.com. The latter is a relatively new blog aggregator but looks to have some interesting content. R-bloggers it quite well established and I was already an occasional reader.

Looking at some recent content I noticed an interesting piece by Ben Bolker (author, among other things, of the excellent bbmle package in R) on dynamite plots.

The companion web site for Serious Stats is now live:

http://www.palgrave.com/psychology/baguley/

The web site includes:

- a free sample chapter (Chapter 15: Contrasts)

- data sets

- R scripts

- 5 online supplements (for meta-analysis, multiple imputation, replication probabilities, pseudo-R squared and loglinear models)

Also don't forget the Serious stats blog to accompany the book.

This is a quick follow up to my earlier post that discussed how to graph CIs for within-subjects (repeated measures) ANOVA designs. My forthcoming book Serious stats describes how to do this for between-subjects designs (a much simpler problem).

I finally found some time to take a closer look at p curves. I haven't had a chance to follow-up my simulations (and probably won't for a few weeks if not months), but I have had time to think through the ideas the p curve approach raises based on some of the comments I've received and a brief exchange with Uri Simonsohn (who has answered a few of my questions).

First, I got a couple of things at least partly wrong.
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