Use of Machine Learning in Questionnaire Development and Evaluation |
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| Coordinator 1 | Professor Natalja Menold (Dresden University of Technology) |
Development and validation of measurement instruments can pose challenges of item selection or instrument modifications, which should meet different quality criteria, such as minimizing systematic measurement error and maximizing reliability, validity and comparability among population groups. Machine learning methods have become relevant to solve such problems and can foster innovations in the area of questionnaire development and evaluation. Moreover, machine learning methods can help to investigate numerous potential sources of bias in the measurement and can help to improve questionnaire design with the aim of maximizing measurement quality and data comparability.
The session aims to foster discussions among the researchers who use machine learning as a tool of optimizing questionnaire design process, evaluation of systematic and non-systematic bias in measurement, as well as for validation purposes. Besides such application examples, papers are invited that provide conceptual basis for the use of machine learning in measurement, compare machine learning methods with previously established methods, or discuss limitations and further developments.