.png){kind=icon}
.png){kind=icon}
Return
Nonprobability Sampling and Causal Analysis
.png){kind=icon}
PRE
AI
DOI：10.1146/annurev-statistics-030718-104951.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
causal inference
generalizability
self-selection
nonprobability sampling
validity
measurement error
heterogeneous treatment effects
big data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.7
Papers:
211
Citations:
2.4K
Organization
View more organizations