An Introduction to Machine Learning Methods in Sample Surveys

Abstract

Machine learning is revolutionizing sample surveys by improving data collection, analysis, and utilization. It combines advanced statistical techniques with computational algorithms to enhance survey sampling methods and data quality. Machine learning algorithms optimize survey sample design by identifying relevant variables, detecting patterns, and constructing efficient sampling strategies. They also assist in preprocessing and cleaning survey data, automatically detecting errors, imputing missing values, and handling outliers. Moreover, machine learning enables predictive modeling and estimation in sample surveys, leveraging large-scale data to generate models that predict outcomes, estimate population parameters, and uncover complex relationships among variables. Integrating machine learning into survey practices leads to more efficient and informative surveys, benefiting decision-making processes across various domains. Overall, machine learning has the potential to transform sample surveys, enabling more accurate predictions and estimations and improving the overall effectiveness of surveys. The application of machine learning in sample surveys and its potential future applications are described in the study.