文章基本信息

标题：Tree-based Machine Learning Methods for Survey Research
本地全文：下载
作者：Christoph Kern ; Thomas Klausch ; Frauke Kreuter 等
期刊名称：Survey Research Methods
印刷版ISSN：1864-3361
出版年度：2019
卷号：13
期号：1
页码：73-93
DOI：10.18148/srm/2019.v1i1.7395
出版社：European Survey Research Association
摘要：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.
其他摘要：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.
关键词：machine learning;predictive models;panel attrition;nonresponse;adaptive design
其他关键词：machine learning;predictive models;panel attrition;nonresponse;adaptive design