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. 2019 Apr 11;13(1):73-93.

Tree-based Machine Learning Methods for Survey Research

Christoph Kern  1 , Thomas Klausch  2 , Frauke Kreuter  1
Affiliations

Tree-based Machine Learning Methods for Survey Research

Christoph Kern et al. Surv Res Methods. .

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.

Keywords: adaptive design; machine learning; nonresponse; panel attrition; predictive models.

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Figures

Figure 1:
Figure 1:
Coefficient Plots of Terminal Node Models of MOB Tree (y = Refusal in GSOEP Wave 2014)
Figure 2:
Figure 2:
Conditional Inference Tree (y = Refusal in GSOEP Wave 2014)
Figure 3:
Figure 3:
Performance Curves in Test Set (y = Refusal in GSOEP Wave 2014)
Figure 4:
Figure 4:
Top-20 Variable Importance (y = Refusal in GSOEP Wave 2014)
Figure 5:
Figure 5:
Partial Dependence Plots Based on Random Forest Result (y = Refusal in GSOEP Wave 2014)
See this image and copyright information in PMC

References

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