Given a distribution of response times to different categorical variables, what's the best way to test for individual differences?
or more specifically:
There are 100 people pressing buttons in 10 colors, with more than 200k presses collected altogether. I want to test for individual differences in response-time (RT) to the buttons, what's the best way to do this?
(remember there are differences in response time in-between the different colors).
So far I thought of:
- ANOVA - calculate a general (weighted) mean RT and compare to individual people.
The distribution level: compare the mean distribution of RT to individual distributions using:
- chi-squared for goodness of fit
- Kullback–Leibler divergence test
- Kolmogorov–Smirnov test
Find a cool bayesian way to do it (which?)
- Use a mixed-effect regression model, and calculate the marginal contribution of the people (R square test).
please help me decide - what's the best practice here? I'd love to learn about another way I didn't think of that would do the trick.