Austin Nichols wrote:
Assuming you had no missing data, how would you analyze this? I would
have thought some GMM or stacked approach... I am assuming errors are
correlated across models (one may sacrifice accuracy to improve RT or
vice versa). How many subjects do you lose if you use complete cases?
Yes, there is an RT/accuracy tradeoff and also a substantial amount of
individual heterogeneity. The model would be a path model, essentially,
as all regressors are observed. They are either tests of cognitive
function, perception, etc., or age. There are also variables that change
within subject depending on the stimuli they received. Complete data...
150 subjects out of 300, or thereabouts. Missingness means that an
entire subject has to be excluded because the only missing data are for
tests of cognitive function.
As I said, any two of the three problems in the model are manageable (if
complex).
JV
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