implicitMeasures: An R Package for Scoring the Implicit Association Test and the Single-Category Implicit Association Test

Abstract

Keywords

implicit measures implicit association test single category implicit association test reproducibility open source

Description

Implicit measures such as the Implicit Association Test (IAT; Greenwald et al., 2003) and the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006) have been widely adopted in psychological research to assess attitudes, preferences, and beliefs (Epifania et al., 2022). Nonetheless, they have been largely criticized, particularly concerning issues of construct validity and result reproducibility. The specific algorithm used for scoring the IAT is often omitted, this further undermining reproducibility.

The currently available R (R Core Team, 2024) packages for scoring implicit measures are designed for the IAT only, while no resources are available for the SC-IAT. These packages either do not include all available algorithms or do not document which one is computed.

implicitMeasures (Epifania et al., 2025) is an R package for scoring the IAT and the SC-IAT, which implements the algorithms by Greenwald et al. (2003) and Karpinski and Steinman (2006), respectively. The original labels are preserved to ensure clear disambiguation and citation. The package provides functions for data preparation, descriptive statistics, and fully customizable graphical representation of the results, allowing for the comparison between the performance of the IAT and the SC-IAT (see, e.g., Epifania, 2020). Finally, the implicitMeasures package allows for comparing the results obtained with different IAT scoring algorithms, so that the consequences of the methodological choices can be directly evaluated, in line with a multiverse approach (Steegen et al., 2016).

Availability

The implicitMeasures package, its manual, and vignettes are available on the Comprehensive R Archive Network (CRAN) repository (https://cran.r-project.org/web/packages/implicitMeasures/). The development version of the package can be installed from GitHub (https://github.com/OttaviaE/implicitMeasures).

Supplemental Material

Supplemental Material - implicitMeasures: An R Package for Scoring the Implicit Association Test and the Single-Category Implicit Association Test

Supplemental Material for implicitMeasures: An R Package for Scoring the Implicit Association Test and the Single-Category Implicit Association Test by Ottavia M. Epifania, Pasquale Anselmi, and Egidio Robusto in Applied Psychological Measurement

Footnotes

ORCID iDs

Ottavia M. Epifania

Pasquale Anselmi

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

References

Epifania

Ottavia M.

Anselmi, P. & Robusto, E . (2020). A Fairer Comparison between the Implicit Association Test and the Single Category Implicit Association Test. TPM - Testing, Psychometrics, Methodology in Applied Psychology, 27(2), 207–220. https://doi.org/10.4473/TPM27.2.4

Epifania

O. M.

Anselmi

Robusto

(2022). Implicit social cognition through the years: The implicit association test at age 21. Psychology of Consciousness: Theory, Research, and Practice, 9(3), 201–217. https://doi.org/10.1037/cns0000305

Epifania

O. M.

Anselmi

Robusto

(2025). implicitMeasures: Compute scores for different implicit measures. R Package Version 0.3.0.

Greenwald

A. G.

Nosek

B. A.

Banaji

M. R.

(2003). Understanding and using the implicit association test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216. https://doi.org/10.1037/0022-3514.85.2.197

Karpinski

Steinman

R. B.

(2006). The single category implicit association test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91(1), 16–32. https://doi.org/10.1037/0022-3514.91.1.16

R Core Team . (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available at: https://www.R-project.org/

Steegen

Tuerlinckx

Gelman

Vanpaemel

(2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 11(5), 702–712. https://doi.org/10.1177/1745691616658637

Supplementary Material

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