CleanIAT function in R IAT package

CleanIAT function in R IAT package

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Has anyone used cleanIAT (function in IAT package) to compute D-scores for their IAT data? Have you noticed any discrepancies between your D-scores and those of colleagues that used another program (e.g., SPSS) to clean the same data? Can anyone describe each column in cleanIAT s output?

While the manual says:

"Outputs a dataframe that must be saved to an object. The variable IAT is the calculated D-Score for each individual. SUBEXCL notes any exclusion criteria, with 0 being inclusion data, 1 for exclusion due to fast response, and 2 for exclusion due to missing blocks. C indicates standard deviation for combined blocks (correct trial only), while A indicates standard deviations for combined blocks (all trials). M (mean), E (percent error), N (number of trials used), and F (percent fast responses), are reported for each block included in the original dataframe."

There are a number of columns that are in the output that aren't explained in the documentation. For example, there are three IAT scores (3 D-scores?). And there are 2 S scores and 2 C scores.

I'd like to start an ongoing discussion about this because the package isn't mentioned at all in forums and "cleanIAT" gives zero results in Google Scholar.

So I'm the author of the IAT package in R. It's based on the original scoring algorithm outlined in this publication. Because the scoring algorithm is based on a standard 7-block IAT, the score IAT1 corresponds to the D score relative to the difference between blocks 6 and 3, while IAT2 corresponds to the D score relative to blocks 7 and 4. IAT is then simply the mean of IAT1 and IAT2, and represents the overall D-score of the test. In a simple applied example, you might use this variable as the outcome in a simple t-test to see if there is an effect of some treatment group versus a control group.

One thing to keep in mind when using the IATData loaded with the package is that there are order effects due to randomization of whether participants received stereotype congruent sorting first or stereotype incongruent sorting first. Thus, the D score needs to be calculated separately between these two groups, otherwise the D score would be flipped for half of the participants (see the example in ?cleanIAT to see what I mean).

As for discrepancies, I never tested against SPSS code, but it was designed to replicate the SAS macro used by the Nosek lab exactly.

Finally, a faster version using dplyr is available for download off of my github if you're interested:

library(devtools) install_github("dpmartin42/IAT")

library(IAT) library(psych) cleaned_IAT <- cleanIAT(myData = IATData, blockName = "BLOCK_NAME_S", trialBlocks = c("BLOCK2", "BLOCK3", "BLOCK5", "BLOCK6"), sessionID = "SESSION_ID", trialLatency = "TRIAL_LATENCY", trialError = "TRIAL_ERROR", vError = 1, vExtreme = 2, vStd = 1) head(cleaned_IAT[,c("IAT1","IAT2","IAT")]) IAT1 IAT2 IAT 1 0.8555243 0.95158711 0.9035557 2 0.7539783 0.69624171 0.7251100 3 0.7563726 0.97124441 0.8638085 4 0.4719366 -0.01613619 0.2279002 5 0.3891048 0.88868891 0.6388969 6 -1.0321507 -0.59671619 -0.8144334 describeBy(cleaned_IAT[,c("IAT1","IAT2","IAT")]) vars n mean sd median trimmed mad min max range skew kurtosis se IAT1 1 88 0.26 0.57 0.32 0.28 0.64 -1.03 1.28 2.31 -0.39 -0.63 0.06 IAT2 2 88 0.28 0.62 0.29 0.29 0.73 -1.09 1.41 2.50 -0.19 -1.04 0.07 IAT 3 88 0.27 0.54 0.27 0.29 0.67 -0.82 1.22 2.04 -0.19 -0.96 0.06

Watch the video: E-Prime 3 Webinar: Building an IAT Implicit Association Task (August 2022).