A theory for the measurement of Internet information retrieval.

The purpose of this study was to develop and evaluate a measurement model for Internet information retrieval strategy performance evaluation whose theoretical basis is a modification of the classical measurement model embodied in the Cranfield studies and their progeny. Though not the first, the Cranfield studies were the most influential of the early evaluation experiments. The general problem with this model was and continues to be the subjectivity of the concept of relevance. In cyberspace, information scientists are using quantitative measurement models for evaluating information retrieval performance that are based on the Cranfield model. This research modified this model by incorporating enduser relevance judgment rather than using objective relevance judgments, and by adopting a fundamental unit of measure developed for the cyberspace of Internet information retrieval rather than using recall and precision-type measures. The proposed measure, the Content-bearing Click (CBC) Ratio, was developed as a quantitative measure reflecting the performance of an Internet IR strategy. Since the hypertext "click" is common to many Internet IR strategies, it was chosen as the fundamental unit of measure rather than the "document." The CBC Ratio is a ratio of hypertext click counts that can be viewed as a false drop measure that determines the average number of irrelevant content-bearing clicks that an enduser check before retrieving relevant information. After measurement data were collected, they were used to evaluate the reliability of several methods for aggregating relevance judgments. After reliability coefficients were calculated, measurement model was used to compare web catalog and web database performance in an experimental setting. Conclusions were the reached concerning the reliability of the proposed measurement model and its ability to measure Internet IR performance, as well as implications for clinical use of the Internet and for future research in Information Science.