
doi: 10.1002/tafs.10342
AbstractTechnology and computational advancements have caused us to rethink data collection and sampling methods that have been the standard for gathering information to guide policy decisions. The availability of new technology such as mobile apps has made using nonprobability samples more attractive because of the speed and low expense that are associated with this approach. We review how the use of nonprobability sampling using mobile apps affects the quality of inferences in fishing effort and catch surveys. We present an approach for evaluating the potential biases that arise from both probability and nonprobability sampling methods. The approach shows that well‐conducted probability samples have major advantages compared with nonprobability samples. We conclude that the application of nonprobability sampling for fishing surveys faces serious challenges and should prove that it is fit for use before being adopted more widely.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
