[R-sig-ME] Multilevel logistic regression guessing parameter

Phillip Alday Phillip.Alday at unisa.edu.au
Fri May 12 04:16:48 CEST 2017


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?


> On 11 May 2017, at 20:30, Dominik Ćepulić <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; b)
> 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 doesn´t
> 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 doesn´t
> predict values lower than 0.5 in variable B.
> Thank you,
> Dominik!
> 	[[alternative HTML version deleted]]
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