about cluster randomization analysis
maris at NICI.RU.NL
Tue Nov 8 15:54:22 CET 2005
Another related question: I computed a post-hoc non kosher tuning of the window around the most significative cluster in my data, and I saw that it is significative (p<0.05) if the window edges exceed of about 50 ms the cluster edges (since the cluster is about 70 ms long, the whole window is about 170 ms long); but if I take longer windows, the p-value increases quite rapidly (I'm running at least 500 random draws for each window, and checking that the result does not depend on the number of draws). Do you have such instabilities in your data or should I think that the effect relative to my cluster is definitely too weak? Or maybe my data are not clean enough?
This phenomenon is not an instability, it is what I would expect. Imagine your trials are 10 seconds long and there is an effect in the latency window between 1.3 and 1.35 seconds (i.e., less than 1 percent of trial length). If you ask clusterrandanalysis to compare the conditions over the complete trial length, it may very well miss the effect in the window between 1.3 and 1.35 seconds, because it has to use a large critical value in order to control for false positives in the time window where there is no effect (i.e., 99 percent of the 10 second trial).
I also expected the significativity to decrease while increasing the time window for the same reason, but I was surprised to see the p-value increase so rapidly. I may pose the question more clearly: from your experience, would you say that the effect I described can be considered significative or not? (a few other details: I have 128 electrodes, 8 subjects, and the window I'm choosing is the window where I expect an effect from the literature) A related question is: how much do artifacts influence this kind of test?
The question of significance can only answered on the basis of probability calculations. My own experience is irrelevant in this respect.
With respect to the artifacts, you must be aware of the fact that the power of statistical tests is adversely affected by eye-blinks and all other non-neuronal factors in the signal.
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