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