On Learning to Play with Other Kids′ Toys: A Response to Sanderson and Benda

In their critique of our research, Sanderson and Benda (1998, this issue) suggest several concerns with our characterization and utilization of the Exploratory Sequential Data Analysis (ESDA) approach. In this response, we consider each of the concerns in the context of training needs analysis. We conclude that the ESDA framework appears to hold promise as a training needs analysis tool. However, further dialogue between the experts in the ESDA approach and those in training is required to realize this potential.

Exploring Sequential Data: Commentary on Bowers, Jentsch, Salas, and Braun (1998)

In an investigation intended to determine training needs of flight crews, Bowers et al. (1998, this issue) report two studies showing that the patterning of communication is a better discriminator of good and poor crews than is the content of communication. Bowers et al. characterize their studies as intended to generate hypotheses for training needs and draw connections with Exploratory Sequential Data Analysis (ESDA). Although applauding the intentions of Bowers et al., we point out some concerns with their characterization and implementation of ESDA. Our principal concern is that the Bowers et al. exploration of the data does not convincingly lead them back to a better fundamental understanding of the original phenomena they are investigating.

Macshapa: Software Support for Exploratory Sequential Data Analysis

This paper outlines the need for better conceptual and methodological tools for performing observational data analysis in support of cognitive engineering research and practice and presents a tool, MacSHAPA, that has been designed to support such work. MacSHAPA is particularly suited for cognitive engineering studies of complex real-world decisionmaking. MacSHAPA lets users (1) enter or import data into a spreadsheet-like viewing medium, (2) annotate, manipulate, and visualize data in various ways, (3) carry out statistical analyses of various kinds, and (4) export data and results to other applications. MacSHAPA controls video devices, capturing timecode and inserting it into the database, and using timestamps in the database to locate events of interest on videotape. MacSHAPA's statistical routines include content and duration analysis, transition analysis (with some Markov statistics), lag sequential analysis, cycles reports, and some kinds of sequential pattern matching. The paper concludes with several examples of how MacSHAPA has been used to obtain useful results from observational data collected in laboratory and field settings.