Nonprobability Sampling and Causal Analysis

Posted: 3 May 2019

See all articles by Ulrich Kohler

Ulrich Kohler

University of Potsdam

Frauke Kreuter

Joint Program in Survey Methodology

Elizabeth A. Stuart

Johns Hopkins University - Bloomberg School of Public Health

Date Written: March 2019

Abstract

The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.

Suggested Citation

Kohler, Ulrich and Kreuter, Frauke and Stuart, Elizabeth A., Nonprobability Sampling and Causal Analysis (March 2019). Annual Review of Statistics and Its Application, Vol. 6, Issue 1, pp. 149-172, 2019. Available at SSRN: https://ssrn.com/abstract=3382095 or http://dx.doi.org/10.1146/annurev-statistics-030718-104951

Ulrich Kohler (Contact Author)

University of Potsdam ( email )

August-Bebel Strasse 89
Potsdam, 14482
Germany

Frauke Kreuter

Joint Program in Survey Methodology ( email )

College Park, MD 20742
United States

Elizabeth A. Stuart

Johns Hopkins University - Bloomberg School of Public Health ( email )

615 North Wolfe Street
Baltimore, MD 21205
United States

HOME PAGE: http://www.biostat.jhsph.edu/~estuart

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