Probability and Non-Probability Samples: Improving Regression Modeling by Using Data from Different Sources

TL;DR

This paper introduces a method to enhance regression estimates by integrating data from non-probability sources with probability samples, using tailored residuals and bootstrap techniques to reduce bias and improve accuracy.

Contribution

The paper proposes a novel approach that combines probability and non-probability data sources for better regression inference, addressing bias issues in non-probability sampling.

Findings

Method improves estimate accuracy across various scenarios

Tailored residuals effectively incorporate non-probability data

Bootstrap techniques provide reliable measure of estimate uncertainty

Abstract

Non-probability sampling, for example in the form of online panels, has become a fast and cheap method to collect data. While reliable inference tools are available for classical probability samples, non-probability samples can yield strongly biased estimates since the selection mechanism is typically unknown. We propose a general method how to improve statistical inference when in addition to a probability sample data from other sources, which have to be considered non-probability samples, are available. The method uses specifically tailored regression residuals to enlarge the original data set by including observations from other sources that can be considered as stemming from the target population. Measures of accuracy of estimates are obtained by adapted bootstrap techniques. It is demonstrated that the method can improve estimates in a wide range of scenarios. For illustrative purposes, the proposed method is applied to two data sets.