You’ve probably asked ChatGPT a question. You may even have used it to write a text for you. Artificial intelligence (AI) is pretty good at these things – so, what if we used it to answer survey questions, or help us summarize questionnaire responses? Researchers have started exploring whether AI …
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 …
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 …
Survey data are the most widely used type of data in the quantitative social sciences. They have been used to investigate a huge range of societally relevant topics. However, they also have specific limitations. An effective way of addressing those is linking survey data with other types of data, such …
Mit Verbreitung des Corona-Virus sind etliche Studien und Projekte entstanden, die die Auswirkungen der COVID-19-Pandemie und ihrer Bekämpfung auf die Gesellschaft empirisch erfassen. Die Spannbreite ist groß und reicht von Momentaufnahmen in Befragungen mit selbstselektierten Stichproben, repräsentativ angelegten Surveys, Corona-Tagebüchern bis hin zur Aufnahme in Langzeitstudien. Der RatSWD hat von …
In the finale of season 1 of the blog series, I explore the idea to craft control variables that help us mitigate quality and comparability issues in our source surveys. Embedding such control variables into our ex-post harmonized datasets creates a living documentation of our source material and our harmonization …