Neural Networks for Survey Researchers

Abstract

Neural networks are currently one of the most popular and fastest growing approaches to machine learning, driving advances in deep learning for difficult real-world applications ranging from image recognition to speech understanding in personal assistant agents to automatic language translation. Although not yet as commonly employed in survey research as other types of machine learning, neural networks offer natural extensions of well-known linear and logistic regression techniques in order to learn non-linear functions predicting or describing nearly any real-world process or problem (provided there are sufficient data and an appropriate set of parameters). Moreover, neural networks offer great potential towards more intelligent surveys in the future (e.g., adaptive design tailored to individual respondents’ characteristics and behavior, automated digital interviewers, analysis of rich multimedia data provided by respondents). Neural networks can learn for both regression and classification tasks without requiring assumptions about the underlying relationships between predictive variables and outcomes. In this article, we describe what neural networks are and how they learn (with tips for setting up a neural network), consider their strengths and weaknesses as a machine learning approach, and illustrate how they perform on a classification task predicting survey response from respondents’ (and nonrespondents’) prior known demographics.