Fishing, Hunting, And Wildlife-Associated Recreation Research Articles

Improving survey research using diagrams of data structure

Using diagrams of data structure, or conceptual models, is important in businesses. Survey research often has if-then data structure, but discussion of diagramming survey data structure is rare. This study uses the U.S. Fishing, Hunting and Wildlife-Associated Recreation (FHWAR) survey data and a Taiwan survey in the analysis of benefits of using data structure diagrams in survey research. Examples of data structure diagram use show how diagramming can support consistent and logical data collection, as well as improved data storage and analysis. Analysis also shows how storing if-then (conditional) data in entities/tables allows simple and intuitively meaningful unconditional variable names and can facilitate consideration of conditions that should/can affect analysis. A general conclusion is that the time has come for tourism and business survey researchers to benefit from using diagrams of data structure in planning data accumulation and to benefit from using modern systems in data collection, storage and analysis.

Structuring Survey Data to Facilitate Analysis and Interpretation

Human dimensions survey data are commonly stored in flat files where the rows correspond to individuals and the columns are variables. As the number of variables increases (e.g., 1,000+) or when compressed variables are used, the complexity of understanding the data increases substantially. This article illustrates how data can be restructured into relational entities to facilitate analyses. Using Sportsperson data from the 2006 National Survey of Fishing, Hunting and Wildlife-Associated Recreation (FHWAR), approximately 1,750 flat file variables were reduced to fewer than 60 relational variables. In contrast to the compressed flat file variables that cannot be directly used in SPSS or SAS, variables in the relational entities can be analyzed. Three examples are given to illustrate using the relational entities. General implications of using relational data structures in analysis and data collection are introduced.

Temporal reliability of willingness to pay from the National Survey of Fishing, Hunting and Wildlife-Associated Recreation1

The US Fish and Wildlife Service's (USFWS) National Survey of Fishing, Hunting and Wildlife-Associated Recreation (FHWAR) has been a source of information on wildlife-related recreation since 1955. The contingent valuation method has been used to estimate willingness to pay for recreation trips in the 1980, 1985, 1991, 1996 and 2001 surveys. However, relatively little comparative analysis over time has been performed. Similar value elicitation formats were used in the 1991 and 1996 surveys for bass and trout fishing, deer hunting and nonconsumptive wildlife recreation. We statistically analyze these data to assess the temporal reliability of the willingness to pay. We control for the effects of trip quality and socioeconomic variables and find that willingness to pay is significantly lower in 1996 for each activity. A subtle, but important, change in the 1996 question format may drive the result of lower willingness to pay. 1 A previous version of this article was presented at the 129th annual meeting of the American Fisheries Society, September 1999, Charlotte, NC. The authors thank two anonymous referees for constructive comments.