Artificial Intelligence Perspectives in Survey Data Analysis

Abstract Surveys are based on questionnaires for generating data that are statistically analysed. The analysis can be performed descriptively and/or with log linear models, structural equations, Poisson regressions, decision trees, Bayesian networks or other such models. Artificial intelligence (AI) and machine learning (ML) methods are gaining presence in research and data analysis. These methods are based on splitting data into training and validation sets and not on stochastic assumptions. Predictive models are learned on the training data and assessed with validation data. This paper provides a perspective on applications of analytics, AI and ML to survey data analysis. By definition, ML is a subfield of AI focused on the algorithms that allow computer systems to perform specific tasks without explicit instructions. We use AI, statistical analysis and analytics as interchangeable terms. From a strategic perspective, the paper presents an information quality framework that is relevant to survey data analytics. The objective is to encourage a transition of survey data analysis, from the traditional enumerative context to future looking analytics.