Nonprobability sampling and causal analysis

Ulrich Kohler, Frauke Kreuter, Elizabeth Stuart

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)149-172
Number of pages24
JournalAnnual Review of Statistics and Its Application
Volume6
DOIs
StatePublished - Mar 7 2019

Keywords

  • big data
  • causal inference
  • generalizability
  • heterogeneous treatment effects
  • measurement error
  • nonprobability sampling
  • self-selection
  • validity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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