Hello,

Let me start by briefly explaining my dataset and the question I'm trying
to answer:
Subjects saw pairs of letters and responded either "same" or "different"
(e.g. they saw "L L" and responded "same" or "L R" and responded
"different") and I have measures for both accuracy and response time.

.I want to predict their reaction time on correct "different" responses
based off of several factors:
pixels - the pixel overlap of the two letters
alphabet - their proximity in the alphabet
identity - whether or not they have the same name, like "r R" or "g G"

Each pair has been coded with a value on all of these measures, for example:
"r R" gets a 0.13 on pixels, a 0 on alphabet, and a 1 on identity,
while
"E F" gets 0.64 on pixels, a 1 on alphabet, and a 0 on identity

So of course I have this large data set of 24*990 observations (less
incorrect responses). But I've gotten confused now about how best to
predict the reaction time on these factors, accounting for the random
effects... I was thinking the R model would be:

fit = lmer ( rt ~ pixels + alphabet + identity + (1|Subject) + (1|Pair))

The model does run, and in fact all factors come out significant. But I
have two concerns:
1. That entering the data/defining the model this way is not correct.
2. That I need  to determine some goodness of fit for the model and
contribution of these factors, the MCMC p-value not being sufficient to
defend the inclusion of all of these variables

Any advice or pointing out of my mistakes will be much appreciated. Thank
you!

Bob, JHU

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