Two Features Supporting Data Integrity and Quality
We've introduced two new ways to ensure quality data: exclude and flagging. If data are incomplete or not suitable for analysis, it can be excluded from views, analysis, and outputs with a click. If data are potentially misleading, harmful, or inappropriate, it can be flagged for our support team to review. Both of these features are available across dscout's entire suite of tools: Express, Diary, Live, and Recruit.
At times, a scout's submissions might not match the expectations of the designed research. This might look like an off-topic, duplicate, dishonest, or even inappropriate response. These entries can now be "flagged" for review, accessible via the detailed view on the upper-right of an entries page. After marking an entry as flagged, a menu will prompt a reason and offer space for any comments. The dscout support team will investigate and take action if-needed.
Flagging a scout entry:
This feature both supports researchers' needs for high-quality, relevant mission data and helps dscout ensure a high quality scout pool. Scouts will not be notified of flags, but may receive feedback from our support team after investigation.
Research on dscout often supports multiple, ongoing business needs; short-term evaluative concept tests might be paired with horizon-three exploratory interviews. Because of the diversity of goals driving any single study, it's useful to focus on a smaller set or portion of data when analyzing. Individual scout entries—both moderated interviews and unmoderated moments—can now be excluded from a dataset on any dscout mission.
Excluding a scout entry:
Excluding an entry or interview session removes it from the researcher dashboard, in-tool analysis views, as well as exports. Critically, data are not deleted, just hidden from view; any mission data marked as excluded can be undone. You can exclude data from the entry-view of any mission. You can re-include data via the excluded grid view, accessible from the analysis tab. The excluded grid view gives researchers a status of which data are in and out from analysis (and a chance to fine tune the dataset in-flight).