Unfortunately, some sources of online panelists today lead to very large numbers of bad respondents. It's not always easy to avoid this. Though, good panel providers have many ways to detect bots and fraud and remove them before they ever have a chance to take your survey. However, there are many server farms out there with professional respondents who are only in it for the incentives and are very hard to detect.
With CBC, there are statistical ways to try to recognize respondents who are answering randomly (RLH from HB analysis). However, it's not very hard for cheaters/fraudsters to fool CBC by always picking the same brand or always picking the lowest price--two examples of easy-to-implement decision rules that will be rewarded with high RLH.
To use HB's RLH statistic to identify respondents, first you need to have enough CBC questions relative to the complexity of your attribute list to identify them with high reliability. A good rule of thumb is that every level should have an opportunity to be seen at least six times by each respondent. So, if you have a 6-level attribute and you are showing 3 concepts (other than the None) per task, then it takes 12 tasks to show each level six times.
If you have enough tasks to identify random respondents with a high degree of reliability, then you can generate 100s of random responders for identifying an appropriate RLH cutoff level. You can use Lighthouse Studio's random data generator and a copy of your original project. Or, you can import 100s of "paper-and-pencil" respondents that you generate randomly in Excel using the RANDBETWEEN function to make choices to CBC tasks.
Run HB analysis on your random data (perhaps in a copy study of your real one). Sort the HB results by RLH, and find the RLH value below which 90% of random respondents fall. This means only a 10% likelihood that random responders will be able to be lucky enough to get a higher RLH. Setting the value about here (at 90% cutoff) will keep you from accidentally discarding very many "good" human respondents.
