Tree-based Machine Learning Methods for Survey Research

  • Christoph Kern University of Mannheim
  • Thomas Klausch VU University Medical Center
  • Frauke Kreuter University of Mannheim University of Maryland Institute for Employment Research (IAB)
Keywords: machine learning, predictive models, panel attrition, nonresponse, adaptive design

Abstract

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.
Published
2019-04-11
How to Cite
Kern, C., Klausch, T., & Kreuter, F. (2019). Tree-based Machine Learning Methods for Survey Research. Survey Research Methods, 13(1), 73-93. https://doi.org/10.18148/srm/2019.v1i1.7395
Section
Articles