Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires.
Please cite the software and the compendium paper.
Pronk, T., Molenaar, D., Wiers, R. W., & Murre, J. M. J. (2021). Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-021-01948-3
Pronk, T. (2023). splithalfr: Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires (Version 2.2.2) [Computer software]. https://doi.org/10.5281/zenodo.7777894
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.
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)
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, 2012)
- Monte Carlo (Williams & Kaufmann, 2012)
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. Finally, I would like to thank Mae Nuijs and Sera-Maren Wiechert for spotting bugs in earlier versions of this package.