To ensure high quality in survey research, survey instruments are validated with both qualitative and quantitative methods. However, both methods have their limitations. While quantitative evaluations efficiently establish generalizable associations between different concepts and systematic variances between respondents due to large sample sizes, their ability to comprehend the intricate cognitive processes within individuals is restricted. In contrast, qualitative methods such as semi-structured cognitive interviews offer deeper insights into participants' thought patterns. However, their high costs often limit sample sizes and scalability, affecting their overall validity, generalizability, and the ability to account for subgroup differences.
Integrating the strengths of both methods can significantly improve survey research by providing a holistic understanding of phenomena while taking advantage of quantitative validation. However, merging both approaches is a challenge.
The project AI-SIC contributes by developing an AI-supported approach for semi-automatic coding using an active learning framework. Machine coding algorithms are combined with human coding capabilities. Furthermore, new methods for efficient validation of survey instruments will be used to close research gaps in relation to the already established measurement of self-rated health. This will help to bridge the gap between qualitative and quantitative methodology and answer open questions about how children and their parents assess their children's health.