splithalfr: Split-Half Reliabilities

Estimates split-half reliabilities for scoring algorithms of cognitive 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 Methods

The splithalfr supports a variety of methods for splitting your data. We review and assess each method in the compendium paper (Pronk et al., 2021). This vignette illustrates how to apply each splitting method via the splithalfr: vignette("splitting_methods") * 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 (Kopp, Lange, & Steinke, 2021, 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.