2nd funding period of the DFG project "Statistical modelling using mouse movements to model measurement errors and improve data quality in web surveys".
The DFG funds project Statistical modelling using mouse movements to model measurement errors and improve data quality in web surveys.
Project participants
- Sonja Greven (Humboldt-Universität zu Berlin)
- Frauke Kreuter (Ludwig-Maximilians-Universität München)
First founding period: https://gepris.dfg.de/gepris/projekt/396057129
Abstract
The overarching goal of this project is the improvement of web survey data collection. More specifically, we focus on developing widely applicable paradata measures based on cursor data, including click data produced by mouse movements or equivalents (fingers or a stylus on tablets) but also keyboard input, and changes to the questionnaire field. With these, we seek to show the practical usefulness of paradata in the field, beyond small-scale pilot studies – such as our studies in the first funding period – that have already demonstrated its utility. At the end of the project period, survey practitioners will have an easy-to-use toolkit at their disposal that solves a number of problems the field is facing, including detection of problematic items and participants, bots and inattentive respondents. We will develop best practices surrounding the design and application of capturing paradata in online questionnaires as well as providing guidance on privacy issues.
We will take a two-pronged approach, building on the work and experience of the previous funding period (Figure 1). The first strand of work packages concerns the analysis of the collected paradata, which we will extend and generalize from our previous publications. The second is focused on survey research, working with project partners to implement our advances in large-scale surveys in varied environments and populations, and to make the results available to all.
The project will be carried out by two early career researchers, a postdoctoral fellow with strong statistical and machine learning expertise, and a doctoral researcher with a survey research background. Both will work together closely, with interwoven objectives, but will maintain individual responsibilities and will lead distinct work packages. They will also be supported by a Research Software Engineer who will implement the methods in a robust, user-friendly and widely applicable manner, ensuring the technical quality, scalability, and long-term sustainability of the methods we develop.