I would like to analyze a dataset in which subjects' reaction time and error rate have been recorded. In order to account for potential speed-accuracy trade-off I am planning to make use of the Ratcliff's diffusion model. My data is a within-subject repeated-measures design with two sessions per subject (drug & placebo) with multiple measurements in each session (around 80).
While reading some papers regarding this topic I found a nice report by Wenzlaff et al. (2011). First they fit the diffusion model for every subject and then take the estimated parameters to an ANOVA. However I was confused by their approach: it seems they select the best fitting model for every subject and in this way are fitting models with different parameters for the subjects. My understanding was that it is not a valid approach to put together estimates from models with different parameters?
My questions are:
- Would it be a valid approach to first fit the diffusion model separately for every subject and then take the estimated parameters to a group-level analysis (e.g. repeated-measures ANOVA)?
- Prior to ANOVA of parameter estimates: is it - in the above context - better to fit the same model to all subjects or to select the best fitting model for every subject?
Wenzlaff, H., Bauer, M., Maess, B., & Heekeren, H. R. (2011). Neural characterization of the speed–accuracy tradeoff in a perceptual decision-making task. The Journal of Neuroscience, 31(4), 1254-1266.