I wrote a short blog (with R Code) on how to calculate corrected CIs for rho and tau using the Fisher z transformation.



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I have been thinking to write a paper about MANOVA (and in particular why it should be avoided) for some time, but never got round to it. However, I recently discovered an excellent article by Francis Huang that pretty much sums up most of what I'd cover.

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I wrote a brief introduction to logistic regression aimed at psychology students. You can take a look at the pdf here:  

A more comprehensive introduction in terms of the generalised linear model can be found in my book:

Baguley, T. (2012).

I wrote a short blog (with R Code) on how to calculate corrected CIs for rho and tau using the Fisher z transformation.

I have written a short article on Type II versus Type III SS in ANOVA-like models on my Serious Stats blog:

https://seriousstats.wordpress.com/2020/05/13/type-ii-and-type-iii-sums-of-squares-what-should-i-choose/

I have just published a short blog on the Egon Pearson correction for the chi-square test. This includes links to an R function to run the corrected test (and also provides residual analyses for contingency tables).

The blog is here and the R function here.

Bayesian Data Analysis in the Social Sciences Curriculum

Supported by the ESRC’s Advanced Training Initiative

Venue:           Bowden Room Nottingham Conference Centre

Burton Street, Nottingham, NG1 4BU

Booking information online

Provisional schedule:

Organizers:

Thom Baguley   twitter: @seri

The third and (possibly) final round of the workshops of our introductory workshops was overbooked in April, but we have managed to arrange some additional dates in June.

There are still places left on these.

In my Serious Stats blog I have a new post on providing CIs for a difference between independent R square coefficients.

You can find the post there or go direct to the function hosted on RPubs.

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The third and (possibly) final round of the workshops is open for booking. As with the last round we are planning a free R workshop before hand (reccomended if you need a refresher or have never used R before), but can't offer bursaries for this.

This blog post was written for undergraduate research methods teaching. I have therefore tried to keep everything relatively simple and equation-free. The content is based loosely on more detailed material in my book Serious stats.

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One fascinating thing about working in the area of psychological statistics is how hard it is to move people away from reliance on bad, inefficient or otherwise problematic methods.

It never occurred to me until today to write a post about why faking data is bad. However, I noticed an interesting exchange on Andrew Gelman's blog (see the comments on this post about Marc Hauser).

This article from my other blog may be of interest to readers of this blog: http://seriousstats.wordpress.com/2013/04/18/using-multilevel-models-to-get-accurate-inferences-for-repeated-measures-anova-designs/

There has been quite a bit of buzz recently about the Button et al. Nature Reviews Neuroscience paper on statistical power. Several similar reviews have been published in psychology and other disciplines and come to broadly the same conclusion - that most studies are underpowered.

The British Journal of Mathematical and Statistical Psychology has published a target article (with commentaries and reply) by Andrew Gelman and Cosma Shalizi on philosophy and the practice of Bayesian statistics.

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.

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