Inquisit 4-IAT picture-Attribute and Target mis-matched


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tan
tan
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Thank you very much for responding so quickly Dave. This is very helpful.


Dave
Dave
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To add yet another way of looking at this, as noted at https://millisecond.com/forums/Topic3444.aspx


"D scores can be positive or negative. A positive score indicates an
association of targetA with attributeA  and targetB with attributeB.  A
negative score indicates an association of targetA with attributeB and
targetB with attributeA.
Translating the score into a preference or
attitude thus depends on how you have assigned your real world
categories to these 4 groups. You can determine the mappings by looking
at the topmost section of your IAT script, where you'll see the
<item> definitions for each category."


Thus, if you do not want to reverse D's sign to bring it in line with the stereotypical compatible / incompatible mapping (as suggested in my previous reply), you can leave everything as is as long as you take the above into account when interpreting your data.


Regards,


~Dave


Dave
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It should be obvious that this is simple matter of reversing D's sign. In greatly simplified terms, D is traditionally defined as


incompatible - compatible


which corresponds to


b - a


in the available scripts.


As RTs to incompatible combinations will usually be larger than RTs to compatible combinations, a positive D score indicates an effect in the expected "stereotypical" direction.


Due to your mistake you have effectively computed


compatible - incompatible


This changes nothing whatsoever re. the resulting D's *absolute* numerical value. It only affects its sign. I.e., now a *negative* D score indicates an effect in the expected "stereotypical" direction.



tan
tan
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Hello,


Happy Thanksgiving.


This is the first time that we modified the Picture IAT from Inquisit 4 to gather data. Although we (three of us) had practiced with the program many times before the actual data collection, one of our team members somehow ended up using the following script (I have bolded attributeA and TargetA):


<item attributeAlabel>
/1 = "positive
"
</item>


<item attributeA>
/1 = "Hardworking"
/2 = "Cooperative"
/3 = "Intelligent"
/4 = "Talented"
/5 = "Friendly"
/6 = "Responsible"
/7 = "Social"
/8 = "Attentive"
</item>


<item attributeBlabel>
/1 = "negative"
</item>


<item attributeB>
/1 = "Lazy"
/2 = "Aggressive"
/3 = "Inattentive"
/4 = "Defiant"
/5 = "Disrespectful"
/6 = "Disruptive"
/7 = "Antisocial"
/8 = "Unprepared"
</item>


<item targetAlabel>
/1 = "Black"
</item>


<item targetA>
/1 = "Black_Girl_1.jpg"
/2 = "Black_Girl_2.jpg"
/3 = "Black_Girl_3.jpg"
/4 = "Black_Girl_4.jpg"
/5 = "Black_Boy_1.jpg"
/6 = "Black_Boy_2.jpg"
/7 = "Black_Boy_3.jpg"
/8 = "Black_Boy_4.jpg"
</item>


<item targetBlabel>
/1 = "White"
</item>


<item targetB>
/1 = "White_Girl_1.jpg"
/2 = "White_Girl_2.jpg"
/3 = "White_Girl_3.jpg"
/4 = "White_Girl_4.jpg"
/5 = "White_Boy_1.jpg"
/6 = "White_Boy_2.jpg"
/7 = "White_Boy_3.jpg"
/8 = "White_Boy_4.jpg"
</item>


My question: given that labelA and targetA are inconsistent with the stereotypical association, is there a way that we can corrected the collected data? Thank you.



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