splithalfr: Split-Half Reliabilities

Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals.

Installation

• The latest release is on CRAN at https://cran.r-project.org/package=splithalfr. Install it by running install.packages("splithalfr")
• Releases are also archived at Zenodo under the DOI 10.5281/zenodo.7777894
• The latest development version is available via this repo at https://github.com/tpronk/splithalfr. Beware: this version may contain errors and can change at any time.

Getting started

We've got six short vignettes to help you get started. You can open a vignette bij running the corresponding code snippet vignette(...) in the R console or see them online at rdrr.io

• vignette("rapi_sum") Sum-score for data of the 23-item version of the Rutgers Alcohol Problem Index (White & Labouvie, 1989)
• vignette("vpt_diff_of_means") Difference of mean RTs for correct responses, after removing RTs below 200 ms and above 520 ms, on Visual Probe Task data (Mogg & Bradley, 1999)
• vignette("aat_double_diff_of_medians") Double difference of medians for correct responses on Approach Avoidance Task data (Heuer, Rinck, & Becker, 2007)
• vignette("iat_dscore_ri") Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial (Greenwald, Nosek, & Banaji, 2003)
• vignette("sst_ssrti") Stop-Signal Reaction Time integration method for data of a Stop Signal Task (Logan, 1981)
• vignette("gng_dprime") D-prime for data of a Go/No Go task (Miller, 1996)

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), but based on more recent concerning findings with Monte Carlo splitting (Kahveci et al., 2025; Pronk et al., 2023), I now only recommend permutated splitting and not Monte Carlo splitting. 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, 2012)
• Monte Carlo (Williams & Kaufmann, 2012)

Citing the splithalfr

Please cite the compendium paper (Pronk et al., 2022) and the software. To cite the software, see the CITATION.cff file, type citation("splithalfr") in R, or use the reference below.

Pronk, T. (2025). splithalfr: Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires (Version 3.0.0) [Computer software]. https://doi.org/10.5281/zenodo.7777894

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.

Related packages

These R packages offer resampling-based split-half reliabilities for specific scoring algorithms and are available via CRAN at the time of this writing: multicon, psych, splithalf, and rapidsplithalf. If I missed one, please reach out!

Acknowledgments

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. Finally, I would like to thank Mae Nuijs and Sera-Maren Wiechert for spotting bugs in earlier versions of this package.

Developer guide

Be welcome to modify the source code! If you do, keep the following in mind:

• Please follow the Google R Style Guide.
• Please add some automated unit tests of your code using testthat. Add them to tests/testthat
• If you've got manual tests of your code, add them to tests/