Nonprobability Sampling and Causal Analysis
- 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.
| Author details: | Ulrich KohlerORCiDGND, Frauke KreuterORCiD, Elizabeth A. Stuart |
|---|---|
| DOI: | https://doi.org/10.1146/annurev-statistics-030718-104951 |
| ISSN: | 2326-8298 |
| ISSN: | 2326-831X |
| Title of parent work (English): | Annual review of statistics and its application |
| Publisher: | Annual Reviews |
| Place of publishing: | Palo Alto |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2018/09/12 |
| Publication year: | 2018 |
| Release date: | 2021/05/19 |
| Tag: | big data; causal inference; generalizability; heterogeneous treatment effects; measurement error; nonprobability sampling; self-selection; validity |
| Volume: | 6 |
| Number of pages: | 24 |
| First page: | 149 |
| Last Page: | 172 |
| Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Sozialwissenschaften |
| DDC classification: | 3 Sozialwissenschaften / 30 Sozialwissenschaften, Soziologie |
| Peer review: | Referiert |
