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complex a level can make analysis impossible.
3. The third step is to integrate the observations into an initial causal behaviour
system.
4. Step four involves designing an experiment to clarify/test a prediction of the
simple causal system. For example, what effect does “X” have on the decision
making process of the CO? Rather than having to engage the entire system, such
a prediction can be tested in a micro-world simulation of C2, such as Networked
Fire Chief (Thomas, 1999), or the Tactical Land C4I Assessment Capability
(TLCAC) ( Bowden, Gaertner & Williams, 2000).
5. The fifth step is to interpret the outcome of the experiment as the result of the
interaction of the pre-existing causal system with the experimental environment,
rather than as a simple result of the independent variable.
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6. The sixth step is an attempt to store the results of the manipulations in the form
of additions or corrections to the model.
These stages need to be repeated until the model is demonstrated to have reliability
and validity. Once this has occurred, it can be employed to guide such areas as
system design and processes for training. The advantage, as was mentioned, is that
it grounds predictions in an ecologically valid framework.
2. Aims and Objectives
As was mentioned, one aim of this report is to refine methodologies that can be used
to evaluate performance in Army command teams. Specific objectives include the
following:
• Refine observational methodologies designed to assess the implementation and
useability of digitised command support systems in HQ.
• Conduct preliminary data collection focussed on developing a map of team and
task processes in HQ. Data generated from the objective observations will be fed
into a behaviour systems model of HQ operation. Ultimately, this will allow
predictions of changes in behaviour/performance that result from changes to the
system input.
3. Collecting Observational Data in the Field
3.1 Overview
As was mentioned in the Introduction, it is necessary to have objective observational
measures, subjective measures, and outcome measures in order to generate an
adequate picture of C2 operation. To date, a large amount of effort has been spent
generating reliable methods of collecting subjective data. For example, the NASA
Task Load Index (TLX) is used as a standard way of assessing subjective workload,
and structured interview techniques are used as a systematic way of collecting data
on a subject’s view of task and team characteristics. Additionally, war-game
simulations generally have outcomes that can be correlated with other performance
measures. In contrast, there has been a low emphasis on behavioural measures. This
is largely because of the difficulties involved in the collection of the basic behavioural
data from a field setting. The labour intensive nature of the work, combined with the
need for an experienced observer has tended to dissuade researchers from
attempting to collect this information. As was discussed, though, it is impossible to
generate a complete picture of C2 operation without objective behavioural data.
Consequently, the current section aims to provide clear guidelines on formal
methods of collecting such data in the field.
It should be noted that the following reflects the authors’ experiences in
observational data collection, as well as an aggregation of concepts from the
published literature (Altman, 1974; Bakeman, 1978; Crockett, 1996; Dunbar, 1976;
Hinde, 1973; Hollenbeck, 1978; Lehner, 1979; Martin & Bateson, 1993; Noldus, 1991).
Thus, it is difficult to provide an exact reference for specific aspects. Instead, the
reader is referred to these papers as a source of information on field research
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techniques. In addition, Appendix A provides a Glossary of standard field research
terminology.
3.2 Formulate the Problem
One of the most important aspects of collecting data in a field setting is formulating
the problem. Under no circumstances should the project attempt to observe
everything. This is one of the most common errors made by inexperienced observers,
and tends to lead to ill-defined observations that risk lapsing into the chaotic. A
general problem we may seek to solve concerns identifying the behavioural
indicators of cognitive functioning, and assessing whether they are correlated with
other measures. A more specific problem may be to ask what tasks the Operations
Cell of a Bde HQ perform, and how the individuals communicate the information
necessary to fulfil their roles.
3.3 Identify the Critical Variables
If it is relevant, it is also important to identify the dependent and independent
　
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