Fully Synthetic Data for Complex Surveys

TL;DR

This paper introduces a method for creating fully synthetic survey data from complex sampling designs, ensuring privacy while maintaining data utility, using Bayesian bootstrap and multiple imputation techniques.

Contribution

It develops a novel approach combining Bayesian bootstrap and multiple imputation for synthetic data generation from complex surveys, with practical implementation and variance estimation methods.

Findings

Effective synthetic data generation for complex surveys demonstrated.

Comparison shows advantages over pseudo-likelihood methods.

Method applied successfully to American Community Survey data.

Abstract

When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993). Specifically, we generate pseudo-populations by applying the weighted finite population Bayesian bootstrap to account for survey weights, take simple random samples from those pseudo-populations, estimate synthesis models using these simple random samples, and release simulated data drawn from the models as public use files. To facilitate variance estimation, we use the framework of multiple imputation with two data generation strategies. In the first, we generate multiple data sets from each simple random sample. In the second, we generate a single synthetic data set from each simple random sample. We present multiple imputation combining rules for each setting. We illustrate the repeated sampling properties of the combining rules via simulation studies, including comparisons with synthetic data generation based on pseudo-likelihood methods. We apply the proposed methods to a subset of data from the American Community Survey.