Model-Based Weighting for Nonresponse in the American Community Survey: Evaluation and Visualization

Music City Center 

​Declining response rates and data collection interruptions are resulting in missing data complexity that traditional missing data techniques used in Census Bureau survey processing may not flexibly capture. At the same time, availability and linkability of administrative records and third party data has improved allowing for more informative response propensity models. We present a study of inverse probability weighting (IPW) to adjust for unit nonresponse using traditional statistical models (non-ML) and machine learning (ML) algorithms adapted for complex survey data. We share various measures for model comparisons and for visualizing geographically-differentiated results. This work presents a case study of the value and advantage of ML and non-ML model-based IPW nonresponse adjustment using auxiliary sources with multiple years of American Community Survey data.

missing data

nonresponse

survey data

boosting

mapping visualizations