Extending theory for user‐centered information services: Diagnosing and learning from error in complex statistical data

Utilization of complex statistical data has come at great cost to individual researchers, the information community, and to the national information infrastructure. Dissatisfaction with the traditional approach to information system design and information services provision, and, by implication, the theoretical bases on which these systems and services have been developed has led librarians and information scientists to propose that information is a user construct and therefore system designs should place greater emphasis on user‐centered approaches. This article extends Dervin's and Morris's theoretical framework for designing effective information services by synthesizing and integrating theory and research derived from multiple approaches in the social and behavioral sciences. These theoretical frameworks are applied to develop general design strategies and principles for information systems and services that rely on complex statistical data. The focus of this article is on factors that contribute to error in the production of high quality scientific output and on failures of communication during the process of data production and data utilization. Such insights provide useful frameworks to design a distributed system of social cognition that will detect, diagnose, communicate, and learn from error. Strategies to design systems that support communicative competence and cognitive competence emphasize the utilization of information systems in a user‐centered learning environment. This includes viewing cognition as a generative process and recognizing the continuing interdependence and active involvement of experts, novices, and technological gatekeepers. © 1997 John Wiley & Sons, Inc.