Probability and non-probability samples: Improving regression modeling by using data from different sources

Tutz, Gerhard (2022): Probability and non-probability samples: Improving regression modeling by using data from different sources. In: Information Sciences, Bd. 621: S. 424-436

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 prob-ability 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 obser-vations from other sources that can be considered as stemming from the target population. Measures of accuracy of estimates are obtained by adapted bootstrap techniques.The pro-posed method is evaluated in simulation studies and applied to two data sets, which shows that it is able to yield improved estimates.(c) 2022 Elsevier Inc. All rights reserved.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISSN: 0020-0255
• Sprache: Englisch