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Advances in the field of machine learning created an array of flexible methods for exploring and analyzing diverse data. These methods often do not require prior knowledge about the functional form of the relationship between the outcome and its predictors while focusing specifically on prediction performance. Machine learning tools thereby offer promising advantages for survey researchers to tackle emerging challenges in data analysis and collection and also open up new research perspectives.

On the one hand, utilizing new forms of data gathering, e.g. via mobile web surveys, sensors or apps, often results in (para)data structures that might be difficult to handle -- or fully utilize -- with traditional modeling methods. This might also be the case for data from other sources such as panel studies, in which the wealth of information that accumulates over time induces challenging modeling tasks. In such situations, data-driven methods can help to extract recurring patterns, detect distinct subgroups or explore non-linear and non-additive effects.

On the other hand, techniques from the field of supervised learning can be used to inform or support the data collection process itself. In this context, various sources of survey errors may be thought of as constituting prediction problems which can be used to develop targeted interventions. This includes e.g. predicting noncontact, nonresponse or break-offs in surveys to inform adaptive designs that aim to prevent these outcomes. Machine learning provides suitable tools for building such prediction models.

This session welcomes contributions that utilize machine learning methods in the context of survey research. The aim of the session is to showcase the potential of machine learning techniques as a complement and extension to the survey researchers' toolkit in an era of new data sources and challenges for survey science.