We analyze within-subjects designs with repeated-measures regressions, aka random-effects models. Learn how to set up such models in R. This concerns analyzing data with grouping, clustering, aka. hierarchical data, data with correlated errors, or data with violations of sphericity.

Do you need some functions over and over again? Tired of sourcing them to multiple projects? Wrap functions in an R package (Windows)

Inter rater reliability If you want to obtain inter-rater reliability measures for dichotomous ratings, by more than two raters, but not all raters rated all items, Fleiss and Cuzick (1979) will be the referece you’ll find. For example, we asked researchers which model they consider a process model (dichotomous rating), and we asked about 60 researchers (more than two rater), of whom not everyone was familiar with every model (not all raters rated all items). They proposed a measure between […]

In R’s default round() functions, odd numbers ending with .5 are rounded up (1.5 becomes 2), but even numbers are rounded down (2.5 becomes 2, rather than 3). This is compliant with a norm on rounding as outlined in the R documentation, but sometimes inconvenient.

R script implementing the tutorial on maximum likelihood estimation in Myung (2003). It optimizes a parametrized retention function via maximum likelihood and the sum of squared errors.