We have a CBC with 8 choice tasks with 3 choice option each (5 attributes).
We believe that several of the respondents have just clicked through the survey and would like to exclude them from the analysis.
We came across the following article which describes using RLH for excluding "bad respondents" from a Max-Diff study:https://www.sawtoothsoftware.com/help/lighthouse-studio/manual/hid_web_maxdiff_badrespondents.html
We were wondering whether there are any suggestions for such a "Fit Statistic to Identify Random Responders with 95% Correct Classification" for a CBC study.
Obviously respondents below a RLH value of 0.33 should be excluded given that their value is even below the chancel model. However, we believe that respondents with a RLH value close to 0.33 (e.g. 0.4) still have respondent rather randomly.
Any advice from your side would be very much appreciated.