Dominik,
You're assuming that test subjects are guessing at random -- it's quite
possible that they believe that they incorrect answer is the correct one,
which would make them less likely than "chance" to select the correct
answer.
"Chance" performance may also not fall at 50% if there are multiple
possible incorrect responses but only one possible correct response.
You could also simply have more incorrect than correct responses for
certain values of your predictor for various reasons related to your
preprocessing steps -- maybe data with a correct response is more likely to
be excluded for various reasons (blinks, timeouts, whatever exclusion
criteria you have for your given methods).
Finally, is B really coded as correct/incorrect? Or is B coded as
response-1/response-2, i.e. without mapping a binary response to
correct-vs-incorrect?
Best,
Phillip
On 11 May 2017, at 20:30, Dominik ?epuli? <dcepulic at gmail.com> wrote:
I have a following situation:
I want to predict variable B (which is dichotomous) from variable A
(continous) controlling for random effects on the level of a) Subjects;
Tasks.
A -> B (1)
The problem is that when I use model to predict the values of B from A,
values below probability of 0.5 get predicted, and in my case that
make sense, because, if you guess at random, the probability of correct
answer on B would be 0.5.
I want to know how I can constrain the model (1) in lme4 so that it
predict values lower than 0.5 in variable B.
Thank you,
Dominik!
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