Many psychological studies involve getting participants to answer a hundred or more closed ended questions. A standard context would be a personality test with 100 items where each item is answered on a 1 to 5 scale. Items are designed to measure various scales and items vary in whether they are positively or negatively worded.
I often want to quickly identify participants who have answered the test randomly or in some other problematic way. I don't want to remove outliers in the purely statistical sense. For example, participants who are just very low or very high on the psychological scales might be flagged as extreme by some multivariate distance measures. I want to remove participants who have not completed the test conscientiously (e.g., random responding).
In the online environment, item response times can be very effective in identifying item skippers. However, assuming you only have item responses for a sample of participants:
- What is a good basic procedure for flagging potential random responders?
- Once such cases have been identified, what is a good strategy for determining whether they are random responders or just a bit unusual?
- Are there any simple functions in R that implement the proposed approach?