Currently there is a lack of national scale, consistent and robust data on the motivations and behaviours of recreational fishers, and lack of robust data on the social and economic contribution of recreational fishing. Where many commercial fisheries have regular collection and estimation process for economic data, this is not the case for recreational fishing in most of Australia. This lack of data also includes behaviour and motivations and how they are changing. These data are useful for informing discussions on resource allocation and in understanding and managing recreational fisheries more generally. If these data are to be used to inform governments and the general public, there is a need to make sure it is collected in a robust way that is representative of the Australian population. Large scale representative data sets are often expensive to acquire and as a result do not get undertaken regularly. One off surveys only provide useful data for any particular point in time, but understanding trends can often be more useful. This study aims to implement and test methodologies to provide a robust and representative sample, while trying to reduce costs to allow for more regular data gathering. To do this requires addressing another need: that of testing new survey methodologies for collecting data from recreational fishers that enables assessment of social and economic contribution. Recreational fishing surveys traditionally use probability based phone or mail surveys, however both methods are experiencing rapid decline in response rates and representativeness. It is expected that going into the future, online surveys that use a range of appropriate recruitment methods will be the most common survey method. There is a need to invest in establishing robust approaches to using these methods, and in understanding how their findings differ to those of traditional probability based surveys.
Final report
- The purpose/objectives of data collection
- Data collection methods, including design of survey instruments and survey recruitment materials, survey sample recruitment methods and sample achieved
- Data processing methods, including data coding and cleaning, and weighting methods.
