Skip to content. | Skip to navigation

Personal tools


You are here: Home / MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications / Tree-based Machine Learning Methods for Survey Research

C. Kern, T. Klausch, and F. Kreuter (2019)

Tree-based Machine Learning Methods for Survey Research

Survey Research Methods, 13(1):73-93.

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.

Social and Economic Inequality, Survey methodology, Data, Panel, Machine Learning, Featured, Data collection, Kreuter
adaptive design, machine learning, panel attrition, nonresponse, predictive models
Published on April 11, 2019

Document Actions