Exploratory Sequential Data Analysis: Foundations

Human-computer interaction (HCI) investigators must consider the sequential nature of interaction and must often weigh behavioral, cognitive, and social factors when studying and designing today's increasingly complex systems. In many cases, laboratory experimentation is inappropriate and formal modeling intractable; instead, observational data analysis is frequently the only appropriate empirical approach. Diverse approaches to observational data analysis already exist, which we synthesize as instances of exploratory sequential data analysis (ESDA). In this article, we outline fundamental ESDA characteristics that might help HCI investigators using sequential data make better conceptual and methodological choices. ESDA owes a philosophical debt to exploratory data analysis but focuses on exploring sequential data. Important issues for ESDA are finding an appropriate temporal band for analysis, finding an effective semantics for encoding, and completing an analysis in an acceptable time frame. We survey temporal factors Penelope M. Sanderson is an engineering psychologist who, in her investigations of cognitive engineering and human-computer interaction, seeks to understand cognition in complex settings; she is an Associate Professor of Psychology and of Mechanical and Industrial Engineering at the University of Illinois at UrbanaChampaign. Carolanne Fisher is a cognitive scientist who specializes in designing high-technology products that are easy for people to use; she is Director of Human Sciences at MAYA Design Group. \ 252 SANDERSON AND FISHER