Hi Thomas et. al.,I'm a new user of your package and would like to use the HDDMRegressor function to fit a between-person effect. Essentially, what my advisor and I would like to do is consider whether DDM model parameters vary as a function of within-subject stimulus condition (congruent vs incongruent on standard flanker and go/no-go tasks) and a continuous self-report measure of personality (which is between-subject). We found some discussion of related questions on the group discussion, but are uncertain whether we’ve identified the correct solution. A number of the HDDMRegressor examples use a continuous measure of interest that is within-person (e.g., from your group, frontal theta on a trial-by-trial basis). In our case, however, it makes sense to treat our continuous variable of interest (e.g., level of disinhibition) at the between-person level. In order to implement this in your package, we setup the following syntax:model_reg = hddm.HDDMRegressor(data, "a ~ DISINH", depends_on={'a': 'stim'}, group_only_nodes=['a_DISINH'])Does this seem correct to you if we want to see how disinhibition (self-report) influences the threshold parameter?After sampling, a plot of the DISINH posterior indicates a negative parametric relationship between disinhibition and threshold, which is what we had predicted (lower threshold in more impulsive individuals). Are we on the right track here or does our syntax need to be reworked? Related: is the intercept of the regression implicitly a within-person effect (akin to a random intercept per subject in standard multilevel models)?
Thanks,
Nate
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Thank you.
Was any study done specifically on the trade off # of participants x # of trials?
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So, basically – the key is to specify the groups/covariates well, right? Then large samples are likely to be helpful.
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Yes, thank you. I understand that.
We are in the process of finalizing design for a large scale data collection and aiming at ~ 700-800 people. My goal is to also reduce the subjects’ fatigue. We started with 80 trials per each of 4 blocks (slightly different conditions in each of them) in an initial sample of 125 people from the Census matching demographics (aiming to get a heterogeneous sample).
I run several models to derive estimates from 50 trials per block, 60, 70, and full 80 right now – this probably can give some sense of how robust the estimates are. But I would feel more comfortable also using something more theoretically supported.
Do I use a usual power analysis to estimate effect sizes in group differences? What is a better approach with the Bayesian estimation?
Thank you.
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Thank you!
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