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Tree-based Machine Learning Methods for Survey Research.

Christoph Kern1, Thomas Klausch2, Frauke Kreuter1

  • 1University of Mannheim.

Survey Research Methods
|August 18, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning, specifically tree-based methods, offers powerful predictive modeling for survey research. These techniques enhance data analysis and can predict survey nonresponse effectively.

Keywords:
adaptive designmachine learningnonresponsepanel attritionpredictive models

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Area of Science:

  • Computational Social Science
  • Survey Methodology
  • Machine Learning

Background:

  • Machine learning (ML) offers advanced predictive modeling, adaptable to complex data relationships without prior assumptions.
  • Survey research is increasingly adopting ML for data collection and management improvements.

Purpose of the Study:

  • Introduce prominent tree-based ML methods.
  • Review ML applications in survey research.
  • Demonstrate ML for predicting panel survey nonresponse.

Main Methods:

  • Exploration of prominent tree-based machine learning algorithms.
  • Review of existing and potential applications of supervised learning in surveys.
  • Application of tree-based methods to model and predict nonresponse in panel surveys.

Main Results:

  • Tree-based ML methods provide flexible and powerful tools for survey data analysis.
  • These methods can effectively model complex interrelations between survey outcomes and predictors.
  • Demonstrated utility in predicting and managing survey nonresponse.

Conclusions:

  • Tree-based machine learning methods are valuable for advancing survey research practices.
  • Their application can lead to improved data quality and survey management.
  • Predictive modeling using ML is a promising avenue for addressing challenges like nonresponse.