The KODAQS Toolbox – Assessing and Mitigating Data Quality Issues - Part 2: Digital Behavioral Data

In the first blog post of the KODAQS Toolbox series, we discussed how data quality issues can affect survey data. Similar challenges arise in digital behavioral data (DBD), though they often manifest differently. Researchers may encounter missing or deleted posts, inconsistent annotation schemes across datasets, or preprocessing decisions - such as text cleaning, stopword removal, or automated translation - that alter the data. In addition, digital traces may only imperfectly reflect the social constructs of interest. If ignored, these issues can quietly undermine even the most sophisticated analyses.