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.
The new post on the GESIS blog shows how the KODAQS Toolbox helps to improve the quality of survey data.
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