Relevance & Research Question: Profile data in online panels consists mainly of self-disclosure data by the panelists. Unfortunately there are some general problems with self-disclosure data, e.g. data quality (Are you willing to provide information with high quality?), the identification of special target groups (Are you a LOHAS / Early Adopter?) or specific response behaviour (Are you always one of the first panelist who react to our invitations?) This contribution deals with the question, which simple metrics could be used to profile externally the panelists and what's the impact of these additional profile data on sampling.
Methods & Data: In a first exploratory study, we collected a variety of data for response behavior, data quality and special target groups. Based on this data we developed a short profiling questionnaire to predict the panelists response behavior. In a second study we evaluated the accuracy of our profiling method, comparing the response behavior of profiled and not-profiled panelists.
Results: In general, the additional profile data can be used to identify panelists better according to the specific requirements in studies, especially the recruitment for qualitative online studies, where the willingness to provide information voluntarily is crucial.
Added Value: Profiling in online panels usually only aims at the information itself and not at the performance when giving the information. With our contribution we'd like to show, that profile data can also be used for sampling, when studies have special requirements on response behavior. By that, it's possible to improve data quality. None of the less, we don't want to discuss only the possibilities of this method, but also its limits in our experience.