I wanted to have a look at various REML methods for a long time. The rainfall data seemed a nice example. On top of that, FreshBiostats had a blog post 'Mixed Models in R: lme4, nlme, or both?'. So lme4 it is.The data As described before; th...

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David Hsu writes: I have a (perhaps) simple question about uncertainty in parameter estimates using multilevel models — what is an appropriate threshold for measure parameter uncertainty in a multilevel model? The reason why I ask is that I set out to do a crossed two-way model with two varying intercepts, similar to your flight The post Uncertainty...

I was recently completing some professional development activities that required me to write a report on a self-chosen topic related to diversity in student backgrounds. I chose to use the opportunity to reflect on the potential for using R to teach psychology students research methods. I thought I'd share the report in case it interests anyone. Abstract...

Analyzing Likert scale responses really comes down to what you want to accomplish (e.g. Are you trying to provide a formal report with probabilities or are you trying to simply understand the data better). Sometimes a couple of graphs are sufficient and a formalize statistical test isn’t even necessary. However, with how easy it is

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

Data Analysis technologies such as t-test, ANOVA, regression, conjoint analysis, and factor analysis are widely used in the marketing research areas of A/B Testing, consumer preference analysis, market segmentation, product pricing, sales driver analysis, and sales forecast etc. Traditionally the analysis tools are mainly SPSS and SAS, however, the open source R language is catching