Kern Christoph, Klausch Thomas, Kreuter Frauke
University of Mannheim.
VU University Medical Center.
Surv Res Methods. 2019 Apr 11;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.
机器学习领域的预测建模方法已成为各学科中用于探索和分析各类数据的常用工具。这些方法通常不需要关于所研究关系的函数形式的特定先验知识，并且能够适应结果及其预测变量之间复杂的非线性和非加性相互关系，同时特别关注预测性能。这种建模视角正开始被调查研究人员采用，以调整或改进数据收集和/或调查管理的各个方面。为推动这一研究方向，本文（1）介绍了著名的基于树的机器学习方法，（2）回顾并讨论了基于树的监督学习在调查研究中的先前及（潜在）未来应用，（3）举例说明了这些技术在面板调查中对无回答进行建模和预测的背景下的用法。
