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.
| Verfasserangaben: | Ulrich KohlerORCiDGND, Frauke KreuterORCiD, Elizabeth A. Stuart |
|---|---|
| DOI: | https://doi.org/10.1146/annurev-statistics-030718-104951 |
| ISSN: | 2326-8298 |
| ISSN: | 2326-831X |
| Titel des übergeordneten Werks (Englisch): | Annual review of statistics and its application |
| Verlag: | Annual Reviews |
| Verlagsort: | Palo Alto |
| Publikationstyp: | Wissenschaftlicher Artikel |
| Sprache: | Englisch |
| Datum der Erstveröffentlichung: | 12.09.2018 |
| Erscheinungsjahr: | 2018 |
| Datum der Freischaltung: | 19.05.2021 |
| Freies Schlagwort / Tag: | big data; causal inference; generalizability; heterogeneous treatment effects; measurement error; nonprobability sampling; self-selection; validity |
| Band: | 6 |
| Seitenanzahl: | 24 |
| Erste Seite: | 149 |
| Letzte Seite: | 172 |
| Organisationseinheiten: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Sozialwissenschaften |
| DDC-Klassifikation: | 3 Sozialwissenschaften / 30 Sozialwissenschaften, Soziologie |
| Peer Review: | Referiert |
