Interpreting data for IAT test


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stingystooge
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Hello,




I am a student running an IAT test for a Intro Psych class. In other words, I
have no experience in IAT tests. I just had a subject take a test and I have a
.dat file, but I have no idea how to read/interpret the data.



The columns of data are as follows from left to right:





date      



time     



subject



blockcode         



blocknum      



trialcode          



trialnum          



response     



correct



latency         



stimulusnumber1  



stimulusitem1



expressions.da



expressions.db



expressions.d



 



I have
taken basic stats so I do know how to run basic z tests.



Please
help! I'm in way over my head.


stingystooge
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I have no idea why there is the paragraph of random words above my post. I don't know how to get rid of it either. sorry! just start from the "Hello"

stephonomon
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You'll need to score the file using SPSS.

Here is the syntax
http://www.millisecond.com/download/samples/v3/IAT/IAT.sps

For more information go here:
http://www.millisecond.com/download/samples/v3/IAT/default.aspx

Stephon
seandr
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Note that the column "expressions.d" contains the Cohen's D score that indicates the direction and magnitude of the association. Inquisit keeps a running tally of this score for each trial. The last row of data for each participant therefore has the final score for that participant.


So, you could just take the last row of data for each particpant and get the value of expressions.d to get each participant's score.


-Sean


Kaarinen
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Hi,


Just one clarification. Isn't the D score the IAT score which is similar to Cohen's d but not the same thing? Most studies using the IAT report both the D score and Cohen's d. Or am I just confusing things :)?


Best,


Kaarinen


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My understanding was that these are the same thing. Cohen's D is a general measure of effect size, and is computed as the difference between conditions divided by the standard deviation. That's the same way the IAT D score is computed.


However, there may be some subtle difference I'm not aware of.


-Sean


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Dave
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In fact there is a subtle difference between the two measures. Quoting Greenwald, Banaji and Nosek (2003, p.201):


"Division of a difference between means by a standard deviation is quite
similar to the well-known effect-size measure, d (Cohen, 1977). The
difference between the present D measure and the d measure of effect size
is that the standard deviation in the denominator of D is computed from the
scores in both conditions, ignoring the condition membership of each
score. By contrast, the standard deviation used in computing the effect size
d is a pooled within-treatment standard deviation. To acknowledge both
this measure’s similarity to d and its difference, the present measure is
identified with an italicized uppercase letter (D) rather than an italicized
lowercase letter."


Hope this helps,


~Dave


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Awesome, Dave, thanks for setting things straight.


-Sean


Dave
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No problem. Now here's a question for you, Sean: Do the IAT templates provide d or D? I'm just too lazy to find out myself right now. Besides, any further investigation on my part would severly conflict with my personal 'No (more) IAT' policy...;-)


~Dave


Kaarinen
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Hi,



Maybe this one's for Sean, then, if Dave has had it with the method :).



There's some huge difference between D and d, since many studies report both figures and these seem to differ a lot. It could be that the effect size d is calculated somewhat differently from your idea here, or there's some other differing thing to it, but the figures might be really far apart when reporting the study mean effects. For example, the first three studies in a methodological IAT paper* give the figures D=0.49/d=1.23; D=0.37/d=0.86; D=0.30/d=0.73. Cohen's d can also have a number greater than 1, whereas D cannot.



This is kinda giving me the impression that I should think about how to calculate d in addition to D for my results...



In the simplest version, Cohen's d is just the difference between two means divided by standard deviation. The IAT D, however, takes a lot more into account - at least when the improved scoring algorithm is used. This is what I was going after in a previous question concerning the need for making a SPSS syntax for my ST-IAT; is your ST-IAT algorithm simply comparing the absolute latencies in the two relevant tasks or does it, for example, also calculate the mean latencies for some practice trials to get the latency scale for an individual user. The improved scoring algo does this, among other things, but I think the ST-IAT template doesn't. Does it, however, take too long latencies (+10000ms) or great number of errors into account?



Best,


Kaarinen



* Lane et al. (2007). Understanding and Using
the Implicit Association Test: IV




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