This paper communicates results of a Monte Carlo simulation study on how the precision of estimates for MaxDiff (best/worst) experiments is affected by:
- Number of items presented per set,
- Number of sets presented to each respondent,
- Number of items in the overall study.
Results show that it may not be useful to ask more than about 5 items per set. The data also suggest that displaying each item 3 or more times per respondent works well for obtaining reasonably precise individual-level estimates with HB. Asking more tasks, such that the number of exposures per item is increased well beyond 3, seems to offer significant benefit, provided respondents don't become fatigued and provide data of reduced quality.