Creating categories for databases

Abstract

The value of a database is bounded by the accessibility of the information it contains. The present studies provide a multifaceted approach to designing and evaluating entry-level menus using, as a case in point, the Statistical Abstract of the United States. They consider different ways of organizing material into categories, developing labels for those categories, and presenting them to users. As performance criteria, the studies consider both the transparency of the resulting system, how easily users can identify the location of items, and its metatransparency, how well users can assess the system's transparency. The latter criterion, which measures the realism of users' expectations regarding their success with the system, is relevant to how willing users are to attempt a search, how carefully they scrutinize its products, and how satisfied (or frustrated) they are with their progress. Aside from demonstrating a general method, these studies provide some potentially useful substantive results. One is the persistent superiority of the Statistical Abstract's 33 chapters as an entry-level menu, as compared with various attempts to create superordinate categories. A second is subjects' relatively poor ability to predict success in locating individual items. A third is the relatively good performance obtained with superordinate categories whose internal structure and labels were determined by individuals like the eventual users. These results replicate and amplify results using more restricted and artificial databases, and offer some promise for designing interfaces as well as some insight into subjective categorization processes.