Chances are, you’ve run into this before. Regression results that change depending on how you handle missing values in your dependent variable. A long item battery where many respondents select the same response option all the way through. A set of survey items that do not seem to measure the concept you had in mind. Or a panel dataset that gradually loses entire demographic subgroups over time. These issues are common in real-world survey data. They may not trigger error messages or stand out immediately in our analyses, but they can quietly and systematically bias our findings if we ignore them.

DOI: 10.34879/gesisblog.2025.114