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.