I would like to test if subjects are significantly more or less accurate under some experimental conditions. At first I thought it's a job for ANOVA for repeated measures, but I am not sure anymore.
Experiment goes like this: There are 4 experimental conditions. Every subject perform multiple trials of the task under each condition. For each trial, he can be either correct or not. I would like to see if some conditions make the task harder, so there is a different proportion of correct (C) vs noncorrect (N) trials.
subject condition_1 condition_2 condition_3 condition_4 1 CCCNNNCCCNC CNCNNNCCCNN CCCCNNNCNCN CCCCCNNNCCC 2 CCCNNCNCNCN CNCNCNNCNNN CNNNCNCCNNN CCCNCNCNCNC 3 CCNCNCNCNCN CNNCNCNCNNC CNCNNNCCNNC CNNCNCNCNNC ...
I don't think that ANOVA is a valid test, because the the mean accuracy per subject, per condition comes from binomial/count data. I read about chi squared and Cochran's Q test for binomial data, but I don't have only binary True/False for each subject and condition but accuracy rate. I also read suggestion for a logistic regression, but I don't know if that would be appropriate either and how to use it with repeated measures.
Can I use ANOVA for repeated measures? If not why not, and what would happen if I do? And what test is more appropriate?