Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires.
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 (https://doi.org/10.15288/jsa.1989.50.30White & 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 <10.1080/026999399379050>)
vignette("aat_double_diff_of_medians") Double difference of medians for correct responses on Approach Avoidance Task data (Heuer, Rinck, & Becker, 2007 <10.1016/j.brat.2007.08.010>)
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 <10.3758/BF03205476>)
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 <https://doi.org/10.3758/s13423-021-01948-3>). This vignette illustrates how to apply each splitting method via the splithalfr: vignette("splitting_methods")
first-second and odd-even (Green et al., 2016 <10.3758/s13423-015-0968-3>; Webb, Shavelson, & Haertel, 1996 <10.1016/S0169-7161(06)26004-8>; Williams & Kaufmann, 2012 <10.1016/j.jesp.2012.03.001>)
stratified (Green et al., 2016 <10.3758/s13423-015-0968-3>)
permutated/bootstrapped/random sample of split halves (Kopp, Lange, & Steinke, 2021 <10.1177/1073191119866257>, Parsons, Kruijt, & Fox, 2019 <10.1177/2515245919879695>; Williams & Kaufmann, 2012 <10.1016/j.jesp.2012.03.001>)
Monte Carlo (Williams & Kaufmann, 2012 <10.1016/j.jesp.2012.03.001>)
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 Nuys and Maren Sera for spotting bugs in earlier versions of this package.