Carolanne Fisher
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Index Terms
- Exploratory sequential data analysis: exploring continuous observational data
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Reviews
Eng-Hock Chia
Fisher and Sanderson describe and evaluate different methodologies for handling time-based data collected during investigations of human-computer interaction. The authors call this “exploratory sequential data analysis” (ESDA). Since they refer exclusively to time-based data, the term “temporal data” or “time-based data” would be preferable to “sequential data,” since the latter may also refer to ordered spatial data. This change would also make the terminology more consistent with usage adopted in applied statistical literature as a whole. The paper focuses mainly on methods of data reduction, and especially discovery of pertinent patterns in data, which the authors call “smoothing” operations. They discuss three main ESDA approaches and eight methods of discovery of pertinent patterns in data. The three approaches discussed are those based on the behavioral, the cognitive, and the social traditions in empirical research. This is an important contribution, given that collection of sequential data, as the authors have highlighted, can result in large data sets that are time-consuming to analyze. The paper also provides advice on overcoming common problems encountered in ESDA and on the use of software ESDA. The authors, however, stop short of recommending specific software for each of the smoothing operations discussed earlier in the paper. The paper provides a good overview of time-based data handling that is worth the attention of those engaged in usability research. The presentation does, however, demand a fairly high level of familiarity with both statistical and social research methodology.
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