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.

• 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 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.

Related packages

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.

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.