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/
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/
I’ve seen it happens quite often. The sig test. Somebody simply needs to know the p-value and that one number will provide all of the information about the study that they need to know. The dataset is presented and the client/boss/colleague/etc invariably asks the question “is it significant?” and “what’s the correlation?”. To quote R.A.
It’s Memorial Day and my dissertation defense is tomorrow. This week I’m phoning in my blog. I had the opportunity to teach a short course last week that was part of a larger workshop focused on ecosystem restoration. A fellow grad student and I taught a session on Excel and R for basic data analysis. 
In survey research, our datasets nearly always comprise variables with mixed measurement levels – in particular, nominal, ordinal and continuous, or in R-speak, unordered factors, ordered factors and numeric variables. Sometimes it is useful to be able to do blanket tests of one set of variables (possibly of mixed level) against another without having to
I recently made three posts regarding analysis of ordinal data. A post looking at all methods I could find in R, a post with an additional method and a post using JAGS. Common in all three was using the cheese data, a data set where...
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 