R package for computing different Implicit Measures scores

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Aim and Overview

The goal of implicitMeasures package is to provide a tool for easily cleaning and computing the scores for the Implicit Association Test (IAT; Greenwald, McGhee, and Schwartz 1998) and the Single Category Implicit Association Test (SC-IAT; Karpinski and Steinman 2006).

Six different algorithms for the computation of the IAT effect, the so-called D-score, are available (Greenwald, Nosek, and Banaji 2003). The six algorithms differentiate themselves for the treatment of the extreme fast responses and for the strategy used for replacing the error responses. Different mistakes can be made during the computation of the D-score, which can raise replicability issues of the results. On top of that, many researchers fail to report the exact algorithm they have used for computing the D-score, raising again replicability issues (Ellithorpe, Ewoldsen, and Velez 2015).

implicitMeasures includes the following functions:

All the functions for graphically representing the results are based on ggplot2 (Wickham 2016), and can be further customized by the users.


You can install the released version of implicitMeasures from CRAN with:


and the development version from GitHub with:

# install.packages("devtools") # un-comment to install devtools


This is a basic example which shows you how to compute the IAT D-score. More detailed examples are given in the package vignettes.

# load the raw_data dataframe

# prepare the dataset for the computation
iat_data <- clean_iat(raw_data, 
                          sbj_id = "Participant",
                          block_id = "blockcode",
                          mapA_practice = "practice.iat.Milkbad",
                          mapA_test = "test.iat.Milkbad",
                          mapB_practice = "practice.iat.Milkgood",
                          mapB_test = "test.iat.Milkgood",
                          latency_id = "latency",
                          accuracy_id = "correct",
                          trial_id = "trialcode",
                          trial_eliminate = c("reminder", "reminder1"))

# store the dataset for computing the D-score
iat <- iat_data[[1]]

# Compute the D-score
dscore <- compute_iat(iat, D = "d3")

compute_iat() results in a data frame with class dscore. This data frame can be passed to other functions, for example for plotting the results, either at the individual level:

Graphical representation of respondents' individual scores

Graphical representation of respondents’ individual scores

or at the sample level:

Density distribution of sample scores

Density distribution of sample scores

Data import

You can import your data in any format you want. If you are importing data sets from SPSS, please use either haven::read_sav("~/path/to/mydata.sav") or foreign::read.spss("~/path/to/mydata.sav") without changing the default options of the functions. implicitMeasures will recognize that the data frame is coming from SPSS and will handle that.

Bugs and problems

If you find any bugs or encounter any problems in using this package, please post a minimal reproducible example on github. For questions and other discussions, you can contact the author and maintainer of the package at

Contributing to implicitMeasures

If you want to contribute to implicitMeasures, by all means! You can open a new branch on, modify the code, and submit your pull request for added features.


Ellithorpe, Morgan E, David R Ewoldsen, and John A Velez. 2015. “Preparation and Analyses of Implicit Attitude Measures: Challenges, Pitfalls, and Recommendations.” Communication Methods and Measures 9 (4): 233–52.

Gawronski, Bertram, Mike Morrison, Curtis E Phills, and Silvia Galdi. 2017. “Temporal Stability of Implicit and Explicit Measures: A Longitudinal Analysis.” Personality and Social Psychology Bulletin 43 (3): 300–312.

Greenwald, Anthony G, Debbie E McGhee, and Jordan L K Schwartz. 1998. “Measuring Individual Differences in Implicit Cognition: The Implicit Association Test.” Journal of Personality and Social Psychology 74 (6): 1464–80.

Greenwald, Anthony G, Brian A Nosek, and Mahzarin R Banaji. 2003. “Understanding and Using the Implicit Association Test: I. An Improved Scoring Algorithm.” Journal of Personality and Social Psychology 85 (2): 197–216.

Karpinski, Andrew, and Ross B. Steinman. 2006. “The Single Category Implicit Association Test as a measure of implicit social cognition.” Journal of Personality and Social Psychology 91 (1): 16–32.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.