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Disability Implicit Association Test (IAT) - with pictures
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Script Author: Sean Draine (seandr@millisecond.com)
last updated: 11-25-2025 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC
Script Copyright © 11-25-2025 Millisecond Software
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TASK VERSION
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Language: German
Translation Credits: German translation provided by K. Borchert for Millisecond Software
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BACKGROUND INFO
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The Implicit Association Task (Greenwald, McGhee, & Schwartz, 1998) is a widely-used cognitive-behavioral paradigm
that measures the strength of automatic (implicit) associations between concepts in people’s minds relying
on latency measures in a simple sorting task.
This IAT measures the strength of automatic (implicit) associations between
the target categories (Abled People vs. Disabled People) and attribute categories (Good/Bad).
The main Dependent Variable is the D-Score ('Difference Score').
//D-Score Interpretation
D-scores obtained with this script:
Positive d-scores: support a stronger association between 'Abled People-Good' and 'Disabled People-Bad' than for the opposite pairings
Negative d-scores: support a stronger association between 'Disabled People-Good' and 'Abled People-Bad' than for the opposite pairings
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Implicit Association Test (IAT) - IAT template with pictures
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Script Author: Sean Draine (seandr@millisecond.com)
last updated: 11-25-2025 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC
Script Copyright © 11-25-2025 Millisecond Software
___________________________________________________________________________________________________________________
BACKGROUND INFO
___________________________________________________________________________________________________________________
The Implicit Association Task (Greenwald, McGhee, & Schwartz, 1998) is a widely-used cognitive-behavioral paradigm
that measures the strength of automatic (implicit) associations between concepts in people’s minds relying
on latency measures in a simple sorting task.
The strength of an association between concepts is measured by the standardized mean difference score of
the 'hypothesis-inconsistent' pairings and 'hypothesis-consistent' pairings (d-score) (Greenwald, Nosek, & Banaji, 2003).
In general, the higher the d-score the stronger is the association between the 'hypothesis-consistent' pairings
(decided by researchers). Negative d-scores suggest a stronger association between the 'hypothesis-inconsistent' pairings.
Inquisit calculates d-scores using the improved scoring algorithm as described in Greenwald et al (2003).
Error trials are handled by requiring respondents to correct their responses according to recommendation (p.214).
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REFERENCES
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Greenwald, A. G., McGhee, D. E., & Schwartz, J. K. L. (1998). Measuring individual differences in implicit cognition:
The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464-1480.
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, 197-216.
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TASK DESCRIPTION
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Participants are asked to categorize attributes (e.g. "joyful"; "tragic") and and target items (e.g "daisy" vs. "wasp")
into predetermined categories via keystroke presses. The basic task is to press a left key (E) if an item (e.g. "joyful")
belongs to the category presented on the left (e.g. "Good") and to press the right key (I) if the word (e.g. "tragic")
belongs to the category ("Bad") presented on the right.
For practice, participants sort items into the target categories "Flowers vs. Insects" and the attribute categories "Good vs. Bad".
For the test, participants are asked to sort categories into the paired/combined categories (e.g.
"Flower OR Good" on the left vs. "Insect OR Bad" on the right). Pairings are reversed for a second test
(e.g. "Insects OR Good" on the left vs. "Flowers OR Bad" on the right). Block order is counterbalanced by groupnumber.
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DURATION
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5.5 minutes
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DATA OUTPUT DICTIONARY
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(1) Raw data file: 'pictureiat_raw*.iqdat'
build: The specific Inquisit version used (the 'build') that was run
computer.platform: the platform the script was run on (win/mac/ios/android)
computer.touch: 0 = device has no touchscreen capabilities; 1 = device has touchscreen capabilities
computer.hasKeyboard: 0 = no external keyboard detected; 1 = external keyboard detected
date: date script was run
time: (local) time script was run
subject: with the current subject id
group: with the current groupnumber
Note: odd/even groupnumbers balance the order in which
hypothesis-compatible/incompatible blocks are run
odd = compatible - incompatible
even = incompatible - compatible
session: with the current session id
blockcode: the name the current block (built-in Inquisit variable)
blocknum: the number of the current block (built-in Inquisit variable)
trialcode: the name of the currently recorded trial (built-in Inquisit variable)
trialnum: the number of the currently recorded trial (built-in Inquisit variable)
Note: trialnum is a built-in Inquisit variable; it counts all trials run;
even those that do not store data to the data file.
conditionOrder: c-ic: consistent -> inconsistent
ic-c: inconsistent -> consistent
response: the response key pressed (e.g. 18=E or 23=I)
Note: script saves the final and -by design- correct response for each trial
responseText: the label of the response key (E vs I)
correct: the accuracy of the initial response
0 = initial response was incorrect and needed to be corrected
1 = initial response is correct
latency: the latency of the final (correct) response in ms; measured from onset of stim
stimulusNumber: the number of the current stimulus
stimulusItem: the currently presented item
da: d-score of the first short blocks (Only meaningful for the last row of data in the raw data file - upon completion of IAT)
db: d-score of the second long blocks (Only meaningful for the last row of data in the raw data file - upon completion of IAT)
d: overall d-score (non-weighted mean of the 2 d-scores); main DV (Only meaningful for the last row of data in the raw data file - upon completion of IAT)
Suggested Interpretation:
D-score <= -0.65 => "a strong" preference for hypothesis-NONconforming pairings
D-score < -0.35 => "a moderate" preference for hypothesis-NONconforming pairings
D-score < -0.15 => "a slight" preference for hypothesis-NONforming pairings
-0.15 <= D-score <= 0.15 "little to no" preference
D-score > 0.15 => "a slight" preference for hypothesis-conforming pairings
D-score > 0.35 => "a moderate" preference for hypothesis-conforming pairings
D-score >= 0.65 => "a strong" preference for hypothesis-conforming pairings
percentCorrect: the overall percent correct score of initial responses in test trials of D-score qualifying latencies (Only meaningful for the last row of data in the raw data file - upon completion of IAT)
propRT300: the proportion of response latencies < 300ms (Only meaningful for the last row of data in the raw data file - upon completion of IAT)
excludeCriteriaMet: 1 = yes, exclusion supported per Greenwald et al (2003, p.214, Table 4): (Only meaningful for the last row of data in the raw data file - upon completion of IAT)
More than 10% of all response latencies are faster than 300ms
0 = otherwise
(2) Summary data file: 'pictureiat_summary*.iqdat'
inquisit.version: Inquisit version run
computer.platform: the platform the script was run on (win/mac/ios/android)
computer.touch: 0 = device has no touchscreen capabilities; 1 = device has touchscreen capabilities
computer.hasKeyboard: 0 = no external keyboard detected; 1 = external keyboard detected
startDate: date script was run
startTime: time script was started
subjectId: assigned subject id number
groupId: assigned group id number
sessionId: assigned session id number
elapsedTime: time it took to run script (in ms); measured from onset to offset of script
completed: 0 = script was not completed (prematurely aborted);
1 = script was completed (all conditions run)
conditionOrder: c-ic: consistent -> inconsistent
ic-c: inconsistent -> consistent
da: d-score of the first short blocks
db: d-score of the second long blocks
d: overall d-score (non-weighted mean of the 2 d-scores); main DV
Suggested Interpretation:
D-score <= -0.65 => "a strong" preference for hypothesis-NONconforming pairings
D-score < -0.35 => "a moderate" preference for hypothesis-NONconforming pairings
D-score < -0.15 => "a slight" preference for hypothesis-NONforming pairings
-0.15 <= D-score <= 0.15 "little to no" preference
D-score > 0.15 => "a slight" preference for hypothesis-conforming pairings
D-score > 0.35 => "a moderate" preference for hypothesis-conforming pairings
D-score >= 0.65 => "a strong" preference for hypothesis-conforming pairings
percentCorrect: the overall percent correct score of initial responses in test trials of D-score qualifying latencies
propRT300: the proportion of response latencies < 300ms
excludeCriteriaMet: 1 = yes, exclusion supported per Greenwald et al (2003, p.214, Table 4):
More than 10% of all response latencies are faster than 300ms
0 = otherwise
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EXPERIMENTAL SET-UP
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Hypothesis-consistent pairings vs. hypothesis-inconsistent pairings; tested within-subjects in a blocked format
=> order is counterbalanced by groupnumber assignment
odd groupnumbers run: consistent - inconconsistent pairings
even groupnumbers run: inconsistent - consistent pairings
Block Sequence:
1. Target Category sorting training
2. Attribute sorting training
3. 1. Test Block of hypothesis-consistent* pairings with 20 trials (half the participant start with inconsistent pairings)
4. 2. Test Block of hypothesis-consistent pairings with 40 trials
5. Target Category sorting training with targets switching sides
6. 1. Test Block of hypothesis-inconsistent pairings with 20 trials
7. 2. Test Block of hypothesis-inconsistent pairings with 40 trials
In all Test Blocks:
* attributes and targets alternate
* attributes as well as targets are randomly selected without replacement
Trial Sequence:
Target -> until correct response -> ISI: 250ms (default)-> Target....
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INSTRUCTIONS
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- iat dependent code can be found in script file "xxxiat_stimuli_inc.iqjs" (xxx=specific iat)
- generic instructions can be found in in script file "pictureiat_instructions_inc.iqjs" (language dependent)
In general:
* start instruction page is provided as an html page. It automatically adapts to different images and category labels UNLESS
the number of attributes and/or targets have been changed. In this case, changes have to be
made to file "intro_iat.htm", so that the correct number of items are presented in the overview table.
Example: instead of 8 words for target A, only 5 should be presented:
in file "intro_iat.htm":
change:
<td><%item.targetA.item(0)%>, <%item.targetA.item(1)%>, <%item.targetA.item(2)%>, <%item.targetA.item(3)%>,
<%item.targetA.item(4)%>, <%item.targetA.item(5)%>, <%item.targetA.item(6)%>, <%item.targetA.item(7)%>
</td>
To:
<td><%item.targetA.item(0)%>, <%item.targetA.item(1)%>, <%item.targetA.item(2)%>, <%item.targetA.item(3)%>,
<%item.targetA.item(4)%>
</td>
* The instructions adapt automatically if different attributes and targets are used.
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EDITABLE CODE
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/ showSummaryFeedback = true: set parameter showsummaryfeedback = true to display summary feedback to participants at the end
set parameter showsummaryfeedback = false if no summary feedback should be presented to participants
/ isi = 250: interstimulus interval (in ms)
