iatgen (pronounced "I A T gen") is an R package and Shiny App that builds and analyzes Qualtrics surveys that contain IATs (Implicit Association Tests; Greenwald et al., 1998) following a procedure developed by Carpenter et al. (2018; preprint available at https://psyarxiv.com/hgy3z/).
Specifically, Carpenter et al. developed procedures for "survey-based" IATs. These are IATs constructed out of modified survey elements that have been edited by adding custom JavaScript and HTML code. The R "iatgen" package was developed as a tool for customizing and pasting this code into Qualtrics so that functional IAT surveys can be rapidly built and analyzed.
Several frequently asked questions about survey-based IATs and iatgen can be found at www.iatgen.wordpress.com. We recommend that people take a look at that web page if questions are not answered here.
First, iatgen is not a software tool for running IATs.
Iatgen does not run IATs. Qualtrics does! Instead, iatgen implements the survey-based IAT method automatically. That is, it configures code files (HTML, JavaScript) based on your input, copies that code into a Qualtrics survey template, and outputs a Qualtrics survey containing your desired IAT–all in just a few seconds. However, importantly, iatgen is not a ‘software tool’ for running the IAT. It is simply a method for implementing the survey-based IAT method. Qualtrics is the software tool that runs the IAT. It would, in theory, be possible for researchers to skip iatgen and configure Qualtrics IATs manually...but that would be laborious and prone to error (e.g., typos when coding).
Another benefit of iatgen is that it provides a suite of data analysis tools for processing the resulting data.
Please note that the iatgen R package is licensed only for non-commercial (e.g., academic) use under a Creative Commons (CC BY-NC 4.0) license. More details are provided under Licsense, below.
Please note that iatgen is not "official" IAT software; that is, it is neither produced nor endorsed by the IAT creators. Although we have painstakingly read the IAT literature and faithfully followed those procedures with our code, the official IAT software remains any software endorsed by the IAT's creators. Although the use of Qualtrics as an IAT tool has been validated by Carpenter et al. (2018), this procedure and its code were generated by Carpenter et al. and were not provided or endorsed by the IAT's creators.
The purpose of this tutorial is to walk you through how to use the
iatgen package to build and analyze IATs.
iatgen can be installed on your computer using the devtools package.
You first need to install this package if you do not have it. In
addition, iatgen will use commands from the stringr package, so this
should be installed as well.
install.packages("devtools")
install.packages("stringr")
Next, iatgen can be installed using the install_github() command from
the devtools package:
devtools::install_github("iatgen/iatgen")
iatgen can be loaded as normal with library():
library(iatgen)
That the primary functions in iatgen have built-in help documentation.
For example, detailed information on writeIATfull() can be obtained
with ?writeIATfull().
Users who do not wish to use the R package can use our Shiny web app, which has the same features, at https://applibs.shinyapps.io/iatui2/.
A brief vignette and summary of IAT build features is provided. Please read our methods paper (https://psyarxiv.com/hgy3z/) for more information.
All iatgen IATs run in Qualtrics. To build an IAT in Qualtrics, users
must (1) configure JavaScript and HTML files, (2) copy them into
Qualtrics, and (3) configure the Qualtrics files. This is all done
automatically by iatgen, via the writeIATfull() function. An example
looks like this.
writeIATfull(IATname="flowins",
posname="Pleasant",
negname="Unpleasant",
Aname="Flowers",
Bname="Insects",
catType="words",
poswords = c("Gentle", "Enjoy", "Heaven", "Cheer", "Happy", "Love", "Friend"),
negwords = c("Poison", "Evil", "Gloom", "Damage", "Vomit", "Ugly", "Hurt"),
tgtType="words",
Awords = c("Orchid", "Tulip", "Rose", "Daffodil", "Daisy", "Lilac", "Lily"),
Bwords = c("Wasp", "Flea", "Roach", "Centipede", "Moth", "Bedbug", "Gnat"),
#advanced options with recommended IAT settings
n=c(20, 20, 20, 40, 40, 20, 40),
qsf=TRUE,
note=TRUE,
correct.error=TRUE,
pause=250,
errorpause=300, #not used if correct.error=TRUE
tgtCol="black",
catCol="green"
)
I walk through each argument here. More detailed information can be
obtained with ?writeIATfull().
-
First, the user specifies
IATname, which becomes part of the file names for the HTML and JavaScript files that are created. Because this is used for file names and folder names, it should be short and avoid any special characters. -
Next, we specify the labels that appear on the screen (in the upper corners of the IAT). The positive category is named with the
posnameargument; the negative category is named with thenegnameargument; Target A is named withAname; and Target B is named withBname. Note that, for our purposes, we assume a "compatible" association isTarget A + Positive, Target B + Negative. In other words, we assume Target A will be more associated with the positive category and Target B will be more associated with the negative category. At the end of the day a positive IAT D-score means that one was faster in theTarget A + Positive, Target B + Negative(compatible) configuration. -
Next, we need to tell R whether either (or both) categories and targets use text or image stimuli. This is done with the
catTypeandtgtTypearguments, which should be set to eitherimagesorwords. If we set them to images (see below), we will specify the stimuli differently than if we set them to words. -
Next, we set the stimuli. If words, we use the arguments
poswords,negwords,Awords, andBwordsas appropriate. Those should be vectors of words or strings of text. If we use images, we instead specifyposimgs/negimgsand/orAimgs/Bimgs(as appropriate), which would be vectors of image URLs. This is illustrated below, so don't worry about this now. This is all you need to set the basic information for the IAT. -
Next, we enter "advanced" options. These are set by default, but it's good to know what your script is doing, so let's discuss them. First, we set
n=c(20, 20, 20, 40, 40, 20, 40). This is the number of trials per block, a numeric vector of length seven. This means we have 20 trials in the first block, 20 in the second block, 20 in the third block, 40 in the fourth block (critical combined block), 40 in the fifth block (washout block with direction reversed; see Nosek et al., 2005 for rationale for setting this to 40), 20 in the sixth block, and 40 in the seventh block. -
The
qsf=TRUEargument is set (as it is by default) to tell R to build a Qualtrics Survey File*.QSF. If you don't want this, set it toFALSEand R will instead create the JavaScript and HTML files manually for you in the user's working directory (saved as .txt files). -
The
note=TRUEincludes a note telling users what the keys are during the task. Some researchers use this to remind users that the task uses the "E" and "I" keys. Some people prefer these not on the screen. -
The
correct.error=TRUEtells R to write the code such that the timer continues until participants enter the correct response. Under this common IAT variant, errors will result in an error message on the screen that persists until the correct response is entered. If set toFALSE, then R will record whichever answer the user enters and display an error message that flashes on the screen only for as long as specified byerrorpause(by default,errorpause=300milliseconds). -
The
pause=250argument tells R to make the duration between trials to be 250 milliseconds, or a quarter of a second. -
The colors of the text stimuli and labels can be set. Typically, in an IAT, they are different for targets and categories (to reduce confusion). By default, they are set with
tgtCol="black"andcatCol="green"but can be set to any CSS color name.
Targets, categories, or both can use images. Images should be sized 250 x 250 pixels in PNG format and hosted via the user's Qualtrics account (tutorial at https://osf.io/ntd97/).
Then, tgtType and/or catType arguments are set to "images" (as
appropriate), and poswords/negwords are replaced with
posimgs/negimgs and/or Awords/Bwords are replaced with
Aimgs/Bimgs (as appropriate). The only difference between the word
stimuli vectors and the image vectors is that the image vectors are
vectors of image URLs. For stability reasons and on the basis of our own
testing, we strongly recommend users of images only host images on
their own Qualtrics accounts and follow the guidelines found in the
tutorial referenced above.
Because URLs are long, we recommend specifying vectors of images URLs in advance and referencing them in the function call:
goodjpg <- c("www.website.com/gentle.jpg",
"www.website.com/enjoy.jpg",
"www.website.com/Heaven.jpg",
"www.website.com/Cheer.jpg")
badjpg <- c("www.website.com/Poison.jpg",
"www.website.com/Evil.jpg.",
"www.website.com/Vomit.jpg",
"www.website.com/Ugly.jpg")
Ajpg <- c("www.website.com/Orchid.jpg",
"www.website.com/Tulip.jpg",
"www.website.com/Rose.jpg",
"www.website.com/Daisy.jpg")
Bjpg <- c("www.website.com/Wasp.jpg",
"www.website.com/Flea.jpg",
"www.website.com/Moth.jpg",
"www.website.com/Bedbug.jpg")
writeIATfull(IATname="flowins",
posname="Pleasant",
negname="Unpleasant",
Aname="Flowers",
Bname="Insects",
catType="images",
posimgs = goodjpg,
negimgs = badjpg,
tgtType="images",
Aimgs = Ajpg,
Bimgs = Bjpg,
#advanced options with recommended IAT settings
n=c(20, 20, 20, 40, 40, 20, 40),
qsf=TRUE,
note=TRUE,
correct.error=TRUE,
pause=250,
errorpause=300, #not used if correct.error=TRUE
tgtCol="black",
catCol="green"
)
Detailed information about this Qualtrics survey is beyond the scope of this document and is discussed in depth in the Carpenter et al. (2018) preprint found at https://psyarxiv.com/hgy3z/.
Of note, however, is that (1) each IAT block is one question and (2) there are four permutations of the IAT exist, counterbalancing the left/right starting position for both Target A and the positive category. Because each IAT consists of 7 blocks, these occupy 28 survey questions (7 blocks x 4 permutations). These questions are named using both the question number (Q1-Q28) and a 3-digit code identifying which IAT permutation it comes from, based on the starting position of Target A (RP = Target A starts right, initially paired with positive; RN = starts left with negative; LP = starts left with positive; LN = starts left with negative). Thus, “Q9 RN2” is the second block in the IAT where Target A starts on the right side, initially paired with negative (i.e., incompatible block comes first). Researchers should carefully consult our manuscript prior to use.
Once data are collected, iatgen can process the resultant data. Several data-analysis scripts and a user tutorial are provided via https://osf.io/ntd97/. However, a brief analysis vignette is provided here.
In this vignette, users were not asked to correct errors and therefore the "D600" algorithm is used. Note that you need to know how the IAT was conducted with respect to errors in order to select the correct analysis procedure. (This is one great reasons to build using an R script, where you can save a copy or even share your build script on a repository with materials, data, code, etc.).
Note too that data must be in the legacy Qualtrics CSV format. For data loaded to R, the row containing detailed question information is also deleted (per usual, when working with Qualtrics data).
First, the data are loaded:
#### LOAD THE IATGEN PACKAGE ####
library(iatgen)
#### READ YOUR DATA HERE AND SAVE IN R AS "DAT" ####
dat <- read.csv("IAT Flowers Insects.csv", header=T)
To analyse the IAT, the data must be collapsed into four variables
representing practice/critical versions of the compatible and
incompatible blocks. At present, these are scattered across four
hard-coded permutations of the IAT representing left/right
counterbalancing of the starting positions (naming of these variables is
discussed above and in our manuscript at https://psyarxiv.com/hgy3z/).
The next step in the analysis is to collapse this down using
combineIATfourblocks():
### Collapse IAT data down ####
dat$compatible.crit <- combineIATfourblocks(dat$Q4.RP4, dat$Q18.LP4, dat$Q14.RN7, dat$Q28.LN7)
dat$incompatible.crit <- combineIATfourblocks(dat$Q7.RP7, dat$Q21.LP7, dat$Q11.RN4, dat$Q25.LN4)
### Collapse IAT practice blocks ####
dat$compatible.prac<- combineIATfourblocks(dat$Q3.RP3, dat$Q17.LP3, dat$Q13.RN6, dat$Q27.LN6)
dat$incompatible.prac <- combineIATfourblocks(dat$Q6.RP6, dat$Q20.LP6, dat$Q10.RN3, dat$Q24.LN3)
Following this, the researcher runs cleanIAT(). In this case, the
researcher is careful to set an error.penalty=TRUE and
error.penalty.ms=600 milliseconds, given that participants were not
forced to correct errors (making this the D600 algorithm; had
participants been forced to correct errors, this would have been
error.penalty=FALSE, making it the D-built.in.error.penalty
algorithm). This command is done and the result saved to an object for
further use, typically named clean.
### Clean the IAT ###
clean <- cleanIAT(prac1=dat$compatible.prac,
crit1=dat$compatible.crit,
prac2=dat$incompatible.prac,
crit2=dat$incompatible.crit,
timeout.drop=TRUE,
timeout.ms=10000,
fasttrial.drop=FALSE,
fastprt.drop=TRUE,
fastprt.percent=.10,
fastprt.ms=300,
error.penalty=TRUE,
error.penalty.ms=600)
There are a few things to note in the cleanIAT().
-
First, the first four arguments (
prac1,crit1,prac2, andcrit2) represent the practice and critical versions of the compatible and incompatible blocks, respectively (see above). -
In addition, following Greenwald et al. (2003)'s D-score algorithm, we have set a timeout such that trials above 10,000 ms are removed (
timeout.drop=TRUE,timeout.ms=10000). -
We have not set individual fast trials to be removed in the same manner (
fasttrial.drop=FALSE) and instead follow the Greenwald et al. (2003) procedure of removing all data from participants who have more than 10% of responses under 300 ms (fastprt.drop=TRUE,fastprt.percent=.10,fastprt.ms=300). Note, however, that you could opt to remove fast trials instead of fast participants. In our experience, fast participants tend to have very high error rates (think: a participant pressing buttons randomly and quickly to skip the IAT). Thus, dropping fast participants seems to make sense in most contexts. -
Finally, as noted above, we are adding an error penalty in analysis of 600 ms because participants were not required to correct errors (
error.penalty=TRUE,error.penalty.ms=600). Note that theerror.penaltyargument is special in that it's smart. Greenwald et al. (2003) suggested researchers could use two standard deviations instead of a fixed penalty. We have added that as an option witherror.penalty="2SD". If you want to disable it (e.g., if participants corrected errors), then set it toerror.penalty=FALSE).
The clean object is a list containing many things For detailed
information see the built-in help file (?cleanIAT()). We focus on a
few here.
First, the number of participants who completed the IAT using
$skipped, a logical vector indicating whether each person completed an
IAT or not:
### NUMBER OF PARTICIPANTS WHO COMPLETED THE IAT ###
sum(!clean$skipped)
## [1] 201
We see here that 201 people (which was the sample size) submitted a completed IAT for analysis.
Next, we can see the proportion of trials dropped due to exceeding
10,000 ms (as specified in our function call, above) with
$timeout.rate:
### TIMEOUT DROP RATE (% of TRIALS) ###
clean$timeout.rate
## [1] 0.001285347
As we see here, it is 1/10 of 1% of trials...a very small amount.
Next, we can get diagnostics on the number of participants dropped due
to overly fast responses with $fastprt.count and $fastprt.rate:
### FAST PARTICIPANT 'BUTTON MASHER' DROP COUNT AND RATE (% of SAMPLE) ###
clean$fastprt.count
## [1] 13
clean$fastprt.rate
## [1] 0.06467662
We see this is 13 participants, or approximately 6% of the sample.
If you wanted to know whether individual participants were dropped or
not, simply request clean$drop.participant which returns a logical
vector. This can be used, for example, to inspect those responses in
greater detail.
clean$drop.participant
## 1 2 3 4 5 6 7 8 9 10 11 12
## TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## 13 14 15 16 17 18 19 20 21 22 23 24
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 25 26 27 28 29 30 31 32 33 34 35 36
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 37 38 39 40 41 42 43 44 45 46 47 48
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 49 50 51 52 53 54 55 56 57 58 59 60
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 61 62 63 64 65 66 67 68 69 70 71 72
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 73 74 75 76 77 78 79 80 81 82 83 84
## FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## 85 86 87 88 89 90 91 92 93 94 95 96
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 97 98 99 100 101 102 103 104 105 106 107 108
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 109 110 111 112 113 114 115 116 117 118 119 120
## TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## 121 122 123 124 125 126 127 128 129 130 131 132
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## 133 134 135 136 137 138 139 140 141 142 143 144
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 145 146 147 148 149 150 151 152 153 154 155 156
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 157 158 159 160 161 162 163 164 165 166 167 168
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 169 170 171 172 173 174 175 176 177 178 179 180
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
## 181 182 183 184 185 186 187 188 189 190 191 192
## FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## 193 194 195 196 197 198 199 200 201
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
It's easy to index in this way. For example, imagine we wanted age statistics on these people:
dat$age[clean$drop.participant]
## [1] 27 29 28 19 39 26 23 28 23 23 26 28 28
Next, How accurate were our retained participants? This is given with
$error.rate:
### ERROR RATE ###
clean$error.rate
## [1] 0.07467388
We are less than 10%, which is considered typical for an IAT. The error rates of individual participants can also be viewed with:
clean$error.rate.prt
## 1 2 3 4 5 6
## NA NA NA 0.016666667 0.025000000 NA
## 7 8 9 10 11 12
## 0.075000000 0.150000000 0.058333333 0.166666667 0.050000000 0.033333333
## 13 14 15 16 17 18
## 0.288135593 0.033333333 0.025000000 0.133333333 0.091666667 0.041666667
## 19 20 21 22 23 24
## 0.133333333 0.058333333 0.041666667 0.025000000 0.008333333 0.068965517
## 25 26 27 28 29 30
## 0.050000000 0.016666667 0.091666667 0.050000000 0.091666667 0.050000000
## 31 32 33 34 35 36
## 0.058333333 0.025000000 0.066666667 0.141666667 0.116666667 0.016666667
## 37 38 39 40 41 42
## 0.183333333 0.050000000 0.041666667 0.041666667 0.091666667 0.116666667
## 43 44 45 46 47 48
## 0.066666667 0.066666667 0.175000000 0.166666667 0.183333333 0.050000000
## 49 50 51 52 53 54
## 0.041666667 0.066666667 0.168067227 0.066666667 0.050000000 0.041666667
## 55 56 57 58 59 60
## 0.116666667 0.058333333 0.033333333 0.058333333 0.050000000 0.041666667
## 61 62 63 64 65 66
## 0.125000000 0.000000000 0.083333333 0.275000000 0.125000000 0.041666667
## 67 68 69 70 71 72
## 0.075000000 0.058333333 0.033333333 0.058333333 0.100000000 0.000000000
## 73 74 75 76 77 78
## 0.116666667 0.041666667 0.041666667 0.058333333 0.025000000 NA
## 79 80 81 82 83 84
## NA 0.067226891 0.200000000 0.091666667 0.016666667 0.158333333
## 85 86 87 88 89 90
## 0.158333333 0.008333333 0.100000000 0.116666667 0.041666667 0.216666667
## 91 92 93 94 95 96
## 0.116666667 0.041666667 0.025000000 0.066666667 0.091666667 0.083333333
## 97 98 99 100 101 102
## 0.025000000 0.116666667 0.041666667 0.050000000 0.100000000 0.050420168
## 103 104 105 106 107 108
## 0.033333333 0.133333333 0.058333333 0.008333333 0.008333333 0.033333333
## 109 110 111 112 113 114
## NA 0.041666667 0.158333333 0.025000000 0.041666667 0.109243697
## 115 116 117 118 119 120
## 0.025210084 0.033333333 0.083333333 NA 0.083333333 0.091666667
## 121 122 123 124 125 126
## 0.058333333 0.042735043 0.075000000 0.066666667 0.141666667 0.075000000
## 127 128 129 130 131 132
## 0.025000000 0.083333333 NA 0.158333333 0.066666667 NA
## 133 134 135 136 137 138
## 0.075000000 0.133333333 0.000000000 0.091666667 0.058333333 0.066666667
## 139 140 141 142 143 144
## 0.066666667 0.058333333 0.208333333 0.075000000 0.141666667 0.008333333
## 145 146 147 148 149 150
## 0.025000000 0.175000000 0.116666667 0.200000000 0.050000000 0.000000000
## 151 152 153 154 155 156
## 0.116666667 0.116666667 0.116666667 0.066666667 0.016666667 0.066666667
## 157 158 159 160 161 162
## 0.085470085 0.041666667 0.016666667 0.041666667 0.041666667 0.025000000
## 163 164 165 166 167 168
## 0.075000000 0.100000000 0.025000000 0.108333333 0.133333333 0.016666667
## 169 170 171 172 173 174
## 0.016666667 0.066666667 0.083333333 0.058333333 0.050000000 0.041666667
## 175 176 177 178 179 180
## 0.058333333 0.058333333 NA 0.100000000 NA 0.025000000
## 181 182 183 184 185 186
## 0.066666667 0.050000000 0.033333333 0.041666667 0.050000000 NA
## 187 188 189 190 191 192
## 0.100000000 0.050000000 0.041666667 0.050000000 0.119658120 0.033333333
## 193 194 195 196 197 198
## 0.100000000 0.194915254 0.025000000 0.066666667 0.066666667 0.116666667
## 199 200 201
## 0.025000000 0.058333333 0.108333333
We can see here that dropped participants have an NA. However, if you
wanted to know the error rate of dropped participants, you could re-run
cleanIAT() without dropping (e.g., saved as clean.nodrop) and then
request the error rates from that
(e.g.,clean.nodrop$error.rate.prt[clean$drop.participant]).
There are many other elements in this clean object. Take a look at the
help file ?cleanIAT() to see what you can get. Or, look at the
names():
names(clean)
## [1] "skipped" "raw.latencies.prac1"
## [3] "raw.latencies.crit1" "raw.latencies.prac2"
## [5] "raw.latencies.crit2" "raw.stim.number.prac1"
## [7] "raw.stim.number.crit1" "raw.stim.number.prac2"
## [9] "raw.stim.number.crit2" "raw.correct.prac1"
## [11] "raw.correct.crit1" "raw.correct.prac2"
## [13] "raw.correct.crit2" "timeout.drop"
## [15] "timeout.ms" "num.timeout.removed"
## [17] "timeout.rate" "num.timeout.removed.prac1"
## [19] "num.timeout.removed.crit1" "num.timeout.removed.prac2"
## [21] "num.timeout.removed.crit2" "fasttrial.drop"
## [23] "fasttrial.ms" "num.fasttrial.removed"
## [25] "fasttrial.rate" "num.fasttrial.removed.prac1"
## [27] "num.fasttrial.removed.crit1" "num.fasttrial.removed.prac2"
## [29] "num.fasttrial.removed.crit2" "fastprt.drop"
## [31] "fastprt.ms" "fastprt.percent"
## [33] "drop.participant" "fastprt.count"
## [35] "fastprt.rate" "error.penalty"
## [37] "error.num.prt" "error.rate.prt"
## [39] "error.rate" "clean.latencies.prac1"
## [41] "clean.latencies.crit1" "clean.latencies.prac2"
## [43] "clean.latencies.crit2" "clean.stim.number.prac1"
## [45] "clean.stim.number.crit1" "clean.stim.number.prac2"
## [47] "clean.stim.number.crit2" "clean.correct.prac1"
## [49] "clean.correct.crit1" "clean.correct.prac2"
## [51] "clean.correct.crit2" "clean.means.prac1"
## [53] "clean.means.crit1" "clean.means.prac2"
## [55] "clean.means.crit2" "diff.prac"
## [57] "diff.crit" "inclulsive.sd.prac"
## [59] "inclusive.sd.crit" "D"
As you can see, there is a lot here (some of it is simply the arguments you specified as inputs, which is nice to have in the final object in case you can't remember what you specified).
We can estimate internal consistency using a procedure described in the
Carpenter et al. (2016) manuscript and De Houwer and De Bruycker (2007)
using the IATreliability() command. This returns a number of things,
including $reliability which is a split-half reliability estimate:
### RELIABILITY ANALYSIS ###
IATreliability(clean)$reliability
## [1] 0.8058219
We see this IAT is estimated at .80, which is great for an IAT.
Next, we can examine the scores. The IAT scores are stored as $D. It
is common to put them back into one's datafile, but they can also be
saved and exported to other software (e.g., SPSS; they will line up with
the rows of the source datafile.) A positive score indicates one had a
preference for the compatible block:
# place back into dat
dat$D <- clean$D
# test for IAT effect
mean(clean$D, na.rm=T)
## [1] 0.6137453
sd(clean$D, na.rm=T)
## [1] 0.3622714
t.test(clean$D)
##
## One Sample t-test
##
## data: clean$D
## t = 23.229, df = 187, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.5616230 0.6658675
## sample estimates:
## mean of x
## 0.6137453
#cohen d
mean(clean$D, na.rm=T) / sd(clean$D, na.rm=T)
## [1] 1.694159
Here we see that the mean IAT score was M = 0.61, SD = 0.36, t(187) = 23.23, p < .001, 95% CI [0.56, 0.67], d = 1.69. This represents a rather large implicit preference for flowers over insects.
There's much we can do at this point. For example, we could make a density plot, which shows us that the distribution is fairly symmetrical but centered well above zero (indicating that the 'compatible' block was indeed easier for participants).
library(ggplot2)
ggplot(dat, aes(x=D))+
geom_density(color="black", fill="light blue")+
theme_light()
## Warning: Removed 13 rows containing non-finite values (stat_density).
At this point, these responses could be exported to excel with a command
such as write.csv() and pasted into another program such as SPSS
(NOTE: user may need to delete NA text in missing responses prior to
pasting into SPSS):
write.csv(clean$D, "iatOUTPUT.csv")
At this point, these D-scores can be correlated with other measures or otherwise analyzed. If uses wish to report the block means by participant, these can be found as well:
### RT DESCRIPTIVES BY BLOCK
mean(clean$clean.means.crit1, na.rm=T)
## [1] 882.2707
mean(clean$clean.means.crit2, na.rm=T)
## [1] 1065.307
mean(clean$clean.means.prac1, na.rm=T)
## [1] 899.6229
mean(clean$clean.means.prac2, na.rm=T)
## [1] 1165.644
sd(clean$clean.means.crit1, na.rm=T)
## [1] 230.9385
sd(clean$clean.means.crit2, na.rm=T)
## [1] 253.1042
sd(clean$clean.means.prac1, na.rm=T)
## [1] 221.7494
sd(clean$clean.means.prac2, na.rm=T)
## [1] 293.3292
The iatgen R package (and associated Shiny App) is licensed only for non-commercial (e.g., academic) use under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The tool was created with the intent of making the IAT accessible to academic researchers who use Qualtrics for online research.
No warranty is offered and we assume no liability of any kind for any consequences that may result from using iatgen. This tool can be modified and distributed with attribution to us, but cannot be used for commercial purposes. More details are given in the full text of the license.
Although we believe the IAT can be validly run via Qualtrics (e.g., as set up via iatgen) and the use of Qualtrics as an IAT tool has been validated by Carpenter et al. (2018), this procedure and its code is not provided or endorsed by the IAT’s creators, and all code for this project was generated by iatgen’s creators. The official IAT sofware remains any software endorsed by the IAT's creators. We hold no copyright to the IAT itself. We are extremely grateful to the IAT's creators, especially Tony Greenwald, for inspiring a cohort of young scientists such as ourselves to study implicit biases and understand why people do, think, and feel what they do.
This
work is licensed under a
Creative
Commons Attribution-NonCommercial 4.0 International License.
The iatgen package was built and maintained by Tom Carpenter (tcarpenter@spu.edu), Michal Kouril, Ruth Pogacar, and Chris Pullig. An early prototype of the HTML and JavaScript were built by Aleksandr Chakroff. Questions regarding iatgen should be directed to Tom Carpenter.
We would like to express our profound gratitude to Tony Greenwald and all other IAT scholars who have come before for inspiring our interest in this project. We also thank Jordan LaBouff and Stephen Aguilar for contributing validation data and to Naomi Isenberg for help setting up our website and user tutorials.