I'd like to know what would be the less biased measure of central tendency to compare RTs in two conditions with a lot of trials (~700). Mean is really sensitive to outliers, and median really robust, so I always used this last one. However it seems to me that I'm not really catching what's happening on the tail of the distribution with this measure. I'm not familiar with diffusion model though.
You can find an accessible overview of some of the issues in Whelan (2008) which contains further references discussing the issue. Note that from a statistical perspective, the sample mean and median are unbiased estimators of their population equivalents. That said, with outliers, skew and the like, the standard error of the sample mean can be quite a bit larger than the sample median. Furthermore, you typically the population distribution should be seen as a multiple component mixture distribution (e.g., fast guess trials, "real trials", distracted trials). In such cases, you may be particularly interested in the central tendency of the "real trial" distribution. In that case, sample mean and median may be biased estimates of "real trial" equivalent population distributions.
Whelan (2008) suggests the following in order of preference when estimating a measure of central tendency:
- Mean after trimming the raw data for outliers (i.e., taking the middle 85% to 95% of RT data)
- Mean of log or negative inverse transformation of raw reaction time
- Median of raw RT
That said, the justification for a given method depends on the underlying distribution of the data and the nature of the effect of condition. For example, if condition is mainly influencing prevalence of outliers or skew, this will effect the mean more than the median in the population.
A good general strategy for exploring these issues and justifying your decision is to estimate central tendency in reaction times using a range of methods. Then correlate the scores you get for each individual using the different methods. If you are getting very high correlations (e.g., r>.95 or .99) then you will know that the method makes little difference.
Whelan (2008) also discusses strategies for estimating all parameters in the RT distribution.
If you are studying choice reaction time, then LBA or Ratcliff's diffusion model may be more appropriate.
- Whelan, R. (2008). Effective analysis of reaction time data. The Psychological Record, 58, 475-482. PDF