
<!-- README.md is generated from README.Rmd. Please edit that file -->

# implicitMeasures

R package for computing different Implicit Measures scores

[![Build
Status](https://app.travis-ci.com/OttaviaE/implicitMeasures)](https://app.travis-ci.com/OttaviaE/implicitMeasures)

## Aim and Overview

The `implicitMeasures` package aims at providing a tool for easily
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 according to how extreme
fast responses and error responses are treated. Different mistakes can
be made during the computation of the *D* score. Moreover, many
researchers fail to report the exact algorithm they have used for
computing the *D* score. Consequently, the replicability of the results
might be compromised (Ellithorpe, Ewoldsen, and Velez 2015).

`implicitMeasures` includes the following functions:

- `clean_iat()`: Prepare and clean the IAT data.
- `clean_sciat()`: Prepare and clean the SC-IAT data.
- `compute_iat()`: Compute the IAT *D* score.
- `compute_sciat()`: Compute the SC-IAT *D* score.
- `descript_d()`: Descriptive table of the *D* scores (also in LaTeX).
- `d_density()`: Plot IAT or SC-IAT scores (distribution).
- `d_point()`: Plot IAT or SC-IAT scores (points).
- `multi_dscore()`: Compute and plot multiple IAT *D* scores.
- `multi_dsciat()`: Plot SC-IAT *D* scores.
- `IAT_rel()`: computes the IAT reliability (Gawronski et al. 2017)

All the functions for the graphical representation of the results are
based on `ggplot2` (Wickham 2016), and can be further customized by the
users.

## Installation

You can install the released version of `implicitMeasures` from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("implicitMeasures")
```

and the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools") # un-comment to install devtools
devtools::install_github("OttaviaE/implicitMeasures")
```

## Example

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

``` r
library(implicitMeasures)
# load the raw_data dataframe
data("raw_data")

# 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")
```

The `compute_iat()` function 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:

<div class="figure" style="text-align: center">

<img src="man/figures/README-unnamed-chunk-2-1.png" alt="Graphical representation of respondents' individual scores" width="100%" />
<p class="caption">

Graphical representation of respondents’ individual scores
</p>

</div>

or at the sample level:

<div class="figure" style="text-align: center">

<img src="man/figures/README-unnamed-chunk-3-1.png" alt="Density distribution of sample scores" width="100%" />
<p class="caption">

Density distribution of sample scores
</p>

</div>

## Data import

You can import your data in any format you want. If you import 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. The `implicitMeasures` package
recognizes that the data frame is coming from SPSS and handles it.

## Bugs and problems

If you find any bugs or encounter any problems in using this package,
please post a minimal reproducible example on
[github](https://github.com/OttaviaE/implicitMeasures/issues). For
questions and other discussions, you can contact the author and
maintainer of the package at <otta.epifania@gmail.com>.

## Contributing to `implicitMeasures`

If you want to contribute to `implicitMeasures`, by all means! You can
open a new branch on <https://github.com/OttaviaE/implicitMeasures>,
modify the code, and submit your pull request for added features.

## Acknowledgments

A special thank to Filippo Gambarota.

# References

<div id="refs" class="references csl-bib-body hanging-indent"
entry-spacing="0">

<div id="ref-ellithorpe2015" class="csl-entry">

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. <https://doi.org/10.1080/19312458.2015.1096330>.

</div>

<div id="ref-gaw2017" class="csl-entry">

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. <https://doi.org/10.1177/0146167216684131>.

</div>

<div id="ref-Greenwald1998" class="csl-entry">

Greenwald, Anthony G, Debbie E McGhee, and Jordan L K Schwartz. 1998.
“<span class="nocase">Measuring Individual Differences in Implicit
Cognition: The Implicit Association Test</span>.” *Journal of
Personality and Social Psychology* 74 (6): 1464–80.
<https://doi.org/10.1037/0022-3514.74.6.1464>.

</div>

<div id="ref-Greenwald2003" class="csl-entry">

Greenwald, Anthony G, Brian A Nosek, and Mahzarin R Banaji. 2003.
“<span class="nocase">Understanding and Using the Implicit Association
Test: I. An Improved Scoring Algorithm</span>.” *Journal of Personality
and Social Psychology* 85 (2): 197–216.
<https://doi.org/10.1037/0022-3514.85.2.197>.

</div>

<div id="ref-karpinski2006" class="csl-entry">

Karpinski, Andrew, and Ross B. Steinman. 2006. “<span class="nocase">The
Single Category Implicit Association Test as a measure of implicit
social cognition</span>.” *Journal of Personality and Social Psychology*
91 (1): 16–32. <https://doi.org/10.1037/0022-3514.91.1.16>.

</div>

<div id="ref-ggplot2" class="csl-entry">

Wickham, Hadley. 2016. *<span class="nocase">ggplot2: Elegant Graphics
for Data Analysis</span>*. Springer-Verlag New York.
<https://ggplot2.tidyverse.org>.

</div>

</div>
