splithalfr: Split-Half Reliabilities

Estimates split-half reliabilities for scoring algorithms of reaction time (RT) tasks and questionnaires.

Getting started

We’ve got six short vignettes to help you get started. You can open a vignette bij running the corresponding code snippets vignette(...) in the R console.

Splitting techniques

vignette("splitting_techniques") The splithalfr supports a variety of techniques for splitting your data. The vignette above illustrates five splitting methods you might encounter in cognitive task literature: * first-second and odd-even (Green et al., 2016; Webb, Shavelson, & Haertel, 1996; Williams & Kaufmann, 1996) * stratified (Green et al., 2016) * permutated/bootstrapped/random sample of split halves (Parsons, Kruijt, & Fox, 2019; Williams & Kaufmann, 1996) * Monte Carlo (Williams & Kaufmann, 1996)

Validation of split-half estimations

Part of the splithalfr algorithm has been validated via a set of simulations that are not included in this package. The R script for these simulations can be found here.

These R packages offer bootstrapped split-half reliabilities for specific scoring algorithms and are available via CRAN at the time of this writing: multicon, psych, and splithalf.


I would like to thank Craig Hedge, Eva Schmitz, Fadie Hanna, Helle Larsen, Marilisa Boffo, and Marjolein Zee, for making datasets available for inclusion in the splithalfr. Additionally, I would like to thank Craig Hedge and Benedict Williams for sharing R-scripts with scoring algorithms that were adapted for splithalfr vignettes.