___________________________________________________________________________________________________________________
Multi-Factor Implicit Association Test (IAT) - Religion
___________________________________________________________________________________________________________________
Main Inquisit programming: Sean Draine (seandr@millisecond.com)
last updated: 02-23-2022 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC
Script Copyright © 02-23-2022 Millisecond Software
___________________________________________________________________________________________________________________
BACKGROUND INFO: Specific BIAT
___________________________________________________________________________________________________________________
The University of Washington has applied for patent on the BIAT method. The patent
is managed by Project Implicit. Both the University of Washington and Project Implicit
authorize free use of the BIAT method and published stimuli for scholarly research,
provided that reports of the research clearly identify any modifications made to the
BIAT and appropriately cite the present article. Please contact Project Implicit
(E-mail: feedback@projectimplicit.net) to request a license for commercial or other
nonscholarly use of the BIAT.
The brief IAT (BIAT) procedure is explained in detail in:
Sriram, N. & Anthony G. Greenwald, A.G (2009).The Brief Implicit Association Test.
Experimental Psychology, 56, 283–294. (see page. 285, Table 1 for an overview of the procedure)
___________________________________________________________________________________________________________________
BACKGROUND INFO: General IAT/BIAT
___________________________________________________________________________________________________________________
The Implicit Association Task (IAT: Greenwald, McGhee, & Schwartz, 1998) and the brief IAT (BIAT: Sriram & Greenwald, 2009)
are widely-used cognitive-behavioral paradigms that measure the strength of automatic (implicit) associations
between concepts in people’s minds relying on latency measures in simple sorting tasks.
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 contrast 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).
Constrast D-scores obtained with this script:
Positive scores indicate a preference for the lefthand category
Negative scores indicate a preference for the righthand category
Example:
expressions.ABd (A is on the left; B is on the right) => A (=Christianity) vs. B (=Islam)
positive D-score indicates a preference for Christianity over Islam; negative D-score indicates a preference for Islam over Christianity.
Aggregate D-Scores:
Aggregate D-Scores are averaged D-Scores for each religion (see guidelines and explanations in Axt et al, 2014).
An aggregate D-score allows the evaluation of each religion compared to the others.
Positive scores = more favorable evaluation than the average one across all groups
Negative scores = less favorable evaluation than the average one across all groups
References:
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.
Sriram, N. & Anthony G. Greenwald, A.G (2009).The Brief Implicit Association Test.
Experimental Psychology, 56, 283–294. (see page. 285, Table 1 for an overview of the procedure)
Axt, J. R., Ebersole, C. R., & Nosek, B. A. (2014). The rules of implicit evaluation by race, religion, and age.
Psychological Science, Vol. 25(9), 1804–1815.
___________________________________________________________________________________________________________________
TASK DESCRIPTION
___________________________________________________________________________________________________________________
Participants are asked to categorize attributes (e.g. "joyful"; "tragic") and religious target items of 4 different
religious affiliations Christianity, Islam, Judaism, Buddhism (e.g "Church", "Koran", "Synagogue", "Karma")
into predetermined categories via keystroke presses.
For the test, participants are asked to sort categories into paired/combined categories (e.g.
"Christianity OR Good" on the left vs. "Anything else" on the right). The basic task is to press a left key (E) if an item
(e.g. "joyful" or "Church") belongs to the category presented on the left (e.g. "Christianity OR Good") and to press the right key (I)
if the word (e.g. "tragic" or "Koran") does not belong to the category on the left. Pairings are reversed for a second test
(e.g. "Islam OR Good" on the left vs. "Anything else" on the right). Each religious affiliation is tested against each other.
The order of the resulting 12 tests is determined randomly.*
*Axt et al (2014) used 24 different counterbalanced orders. Each racial/ethnic group appeared as a
target once every 4 blocks.
___________________________________________________________________________________________________________________
DURATION
___________________________________________________________________________________________________________________
the default set-up of the script takes appr. 7 minutes to complete
___________________________________________________________________________________________________________________
DATA FILE INFORMATION
___________________________________________________________________________________________________________________
The default data stored in the data files are:
(1) Raw data file: 'religioniat_raw*.iqdat' (a separate file for each participant)
build: The specific Inquisit version used (the 'build') that was run
computer.platform: the platform the script was run on (win/mac/ios/android)
date, time: date and time script was run
subject, group, with the current subject/groupnumber
session: with the current session id
blockcode, blocknum: the name and number of the current block (built-in Inquisit variable)
trialcode, trialnum: the name and 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 such as feedback trials
response: the final trial response (scancodes of the keys pressed)
18 = E
23 = I
57 = spacebar press
Note: script saves the final and -by design- correct response for each trial
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
Only meaningful for the last row of data in the raw data file (upon completion of IAT):
percentcorrect: the overall percent correct score of initial responses of test trials of D-score qualifying latencies
Suggested D-Score Interpretation:
D-score <= -0.65 => "a strong" preference for first letter (e.g. AB => strong preference for A)
D-score < -0.35 => "a moderate" preference for first letter
D-score < -0.15 => "a slight" preference for first letter
-0.15 <= D-score <= 0.15 "little to no" preference
D-score > 0.15 => "a slight" preference for second letter (e.g. AB => slight preference for B)
D-score > 0.35 => "a moderate" preference for second letter
D-score >= 0.65 => "a strong" preference for second letter
D-Scores Category Comparisons:
ABd: positive D-score indicates a preference for Whites over Asians
negative D-score indicates a preference for Asians over Whites
ACd: positive D-score indicates a preference for Whites over Blacks
negative D-score indicates a preference for Blacks over Whites
ADd: positive D-score indicates a preference for Whites over Hispanics
negative D-score indicates a preference for Hispanics over Whites
BCd: positive D-score indicates a preference for Asians over Blacks
negative D-score indicates a preference for Blacks over Asians
BDd: positive D-score indicates a preference for Asians over Hispanics
negative D-score indicates a preference for Hispanics over Asians
CDd: positive D-score indicates a preference for Blacks over Hispanics
negative D-score indicates a preference for Hispanics over Blacks
Aggregate D-Scores are averaged D-Scores for each offspring group (see guidelines and explanations in Axt et al, 2014).
An aggregate D-score allows the evaluation of each offspring group compared to the others.
Positive scores = more favorable evaluation than the average one across all groups
Negative scores = less favorable evaluation than the average one across all groups
d_A-
d_D: aggregate D-scores for the 4 groups
Note: if fewer or more than 4 groups were run with this script,
the calculations for the aggregate scores need to be adjusted
(see section EXPRESSIONS)
values.aggregateCheck: check to ensure that the sum of all 4 aggregates is 0
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
(2) Summary data file: 'religioniat_summary*.iqdat' (a separate file for each participant)
inquisit.version: Inquisit version run
computer.platform: the platform the script was run on (win/mac/ios/android)
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)
percentcorrect: the overall percent correct score of initial responses of test trials of D-score qualifying latencies
Suggested D-Score Interpretation:
D-score <= -0.65 => "a strong" preference for first letter (e.g. AB => strong preference for A)
D-score < -0.35 => "a moderate" preference for first letter
D-score < -0.15 => "a slight" preference for first letter
-0.15 <= D-score <= 0.15 "little to no" preference
D-score > 0.15 => "a slight" preference for second letter (e.g. AB => slight preference for B)
D-score > 0.35 => "a moderate" preference for second letter
D-score >= 0.65 => "a strong" preference for second letter
D-Scores Category Comparisons:
ABd: positive D-score indicates a preference for Whites over Asians
negative D-score indicates a preference for Asians over Whites
ACd: positive D-score indicates a preference for Whites over Blacks
negative D-score indicates a preference for Blacks over Whites
ADd: positive D-score indicates a preference for Whites over Hispanics
negative D-score indicates a preference for Hispanics over Whites
BCd: positive D-score indicates a preference for Asians over Blacks
negative D-score indicates a preference for Blacks over Asians
BDd: positive D-score indicates a preference for Asians over Hispanics
negative D-score indicates a preference for Hispanics over Asians
CDd: positive D-score indicates a preference for Blacks over Hispanics
negative D-score indicates a preference for Hispanics over Blacks
Aggregate D-Scores are averaged D-Scores for each offspring group (see guidelines and explanations in Axt et al, 2014).
An aggregate D-score allows the evaluation of each offspring group compared to the others.
Positive scores = more favorable evaluation than the average one across all groups
Negative scores = less favorable evaluation than the average one across all groups
d_A-
d_D: aggregate D-scores for the 4 groups
Note: if fewer or more than 4 groups were run with this script,
the calculations for the aggregate scores need to be adjusted
(see section EXPRESSIONS)
values.aggregateCheck: check to ensure that the sum of all 4 aggregates is 0
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
___________________________________________________________________________________________________________________
EXPERIMENTAL SET-UP
___________________________________________________________________________________________________________________
4 types of religions (A=Christianity, B=Islam, C=Judaism, D=Buddhism) tested against each other => 12 test blocks
e.g. A (Christinity); B (Islam)
Block AB: "Christinity OR Good" vs. "anything else" (aka Islam OR bad words)
Block BA: "Islam OR Good" vs. "anything else" (aka Christinity OR bad words)
Block AC: "Christinity OR Good" vs. "anything else" (aka Judaism OR bad words)
Block CA: "Judaism OR Good" vs. "anything else" (aka Christinity OR bad words)
Block AD: "Christinity OR Good" vs. "anything else" (aka Buddhism OR bad words)
Block DA: "Buddhism OR Good" vs. "anything else" (aka Christinity OR bad words)
Block BC: "Islam OR Good" vs. "anything else" (aka Judaism OR bad words)
Block CB: "Non-binary gender presentation OR Good" vs. "anything else" (aka Islam OR bad words)
Block BD: "Islam OR Good" vs. "anything else" (aka Buddhism OR bad words)
Block DB: "Buddhism OR Good" vs. "anything else" (aka Islam OR bad words)
Block CD: "Judaism OR Good" vs. "anything else" (aka Buddhism OR bad words)
Block DC: "Buddhism OR Good" vs. "anything else" (aka Judaism OR bad words)
Sequence (odd groupnumbers):
1. Attribute Training: 20 trials; 10 positive attributes, 10 negative attributes; order is randomly determined
2-13: random order of the 12 pairing blocks
In all Test Blocks:
* attributes and targets alternate
* attributes as well as targets are randomly selected without replacement
* test blocks run 20 trials by default
* the first 4 trials = prefatory trials that are not included into subsequent analyses
___________________________________________________________________________________________________________________
STIMULI
___________________________________________________________________________________________________________________
Stimuli can be edited under section Editable Stimuli
___________________________________________________________________________________________________________________
INSTRUCTIONS
___________________________________________________________________________________________________________________
Instructions can be edited under section Editable Instructions
___________________________________________________________________________________________________________________
EDITABLE CODE
___________________________________________________________________________________________________________________
check below for (relatively) easily editable parameters, stimuli, instructions etc.
Keep in mind that you can use this script as a template and therefore always "mess" with the entire code
to further customize your experiment.
The parameters you can change are:
The "skipsummary" variable in the values tag can be set to true to skip the final
summary page or false to display the page.
