CHI 97
Electronic
Publications: Late-Breaking/Interactive Posters
Mind Maps and Causal
Models:
Using Graphical Representations of Field Research Data
David
R. Millen, AT&T
Labs, 600 Mountain Ave., Murray Hill, NJ
07974, 908 582-7743, drm@research.att.com
Audrey
Schriefer, AT&T
Labs,
Diane
Z. Lehder AT&T Labs, 101 Crawfords
Corner Rd., Holmdel, NJ 07733, 908 949-5531, dzlehder@att.com
Susan
M. Dray Dray & Associates,
We
recently completed a series of field visits to
understand how workers use the Internet in their daily work activities.
At each
site, the team used traditional field research methods such as work
observations, artifact walk-throughs, and
contextual
inquiry. An innovative debrief process was developed to understand,
summarize
and document each visit. In addition to a structured debrief
questionnaire, the
team created graphical summary notes using "mind maps." These mind
maps efficiently captured a nonlinear, graphical clustering of key
ideas. A
"causal loop diagram" was also developed to document the team's
understanding of the internal and external driving forces for each
organization. Taken together, the debrief questionnaire, the mind maps,
and the
causal loop diagrams provided a rich multimedia representation of the
field
data.
research
methods,
ethnography, qualitative data analysis
© 1997
Copyright on this material is held by the
authors.
INTRODUCTION
Mind
Mapping
Causal
loop
diagrams
Lessons
learned
REFERENCES
In the
spring of 1996, a project named "Thinking
Spaces" was initiated at AT&T Labs to study how technology,
particularly
the use of the Internet, was changing the way people work and the way
they
would conduct their business in the future. The goal was to develop an
understanding of real customers solving real problems - around the
world. An
ethnographic approach was selected to discover and understand the
nature of
these changes.
The
research plan included visits to 31 organizations
during a three month period. At each location, the survey team
interviewed a
principal manager, toured the facility and interviewed and observed
various
workers. The data
collected
for each location included interview notes,
work process flowcharts, floor plans, and photographs of the work
environment
and work processes. Paper artifacts, such as forms, brochures, and
other
company publications, were also collected and electronic artifacts,
such as web
documents, were indexed.
Given
the number of visits, the short duration of the
project, and the large volume of field data, a significant problem the
team
faced was to summarize the findings from each site in a timely fashion.
The
team needed a framework to easily document, share and archive a
staggering
volume of research notes for future analysis.
After
each visit, a debrief session was held in which
field observations were summarized. In addition to a structured debrief
questionnaire, the research team prepared a mind map and a causal loop
diagram.
The intent was to use more than one analytical approach and
representation
scheme in order to better and more completely record the findings from
each
visit. Our use of mind mapping and causal modeling in the field is
discussed
here.
Mind
maps are nonlinear graphical representations of
information. The research team used mind maps to record key ideas about
three
research interests: the physical work environment, the tools used by
the
workers, and communication patterns using email, fax, pagers,
telephone, etc.
The mind mapping process allows for ideas to be generated in a loosely
structured brainstorming session. As the ideas are generated they are
informally categorized by their placement on one of the major idea
branches.
Simple illustrations were occasionally added for emphasis or to clarify
an
idea. At the end of the brainstorming session, the mind map was
reviewed for
completeness and color highlights were used to visually accentuate
important
ideas.
The development of mind maps is a relatively fast and efficient way to record important ideas about a field observation. The non-linear nature of the record prevents a formal and rigid analysis that may result in blind spots. Important patterns in the field data emerge as related ideas are grouped together. An example of a mind map from one of the field visits is presented in Figure 1.
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Figure 1 . Example of mind map site summary.
Causal
loop diagrams have been used extensively to
analyze qualitative data (see, for example, [1]). In traditional causal
modeling, a network of variables is developed and the causal
relationships
between variables are explicitly delineated. The model is typically
developed
after all of the field data have been collected and some
cross-observation
meta-analysis has been completed.
In
recent years, there has been an enthusiastic use
of casual modeling to understand organizations and businesses [2]. In
our
research, we adopted this perspective, and decided to develop causal
loop
diagrams during the data collection phase of the
research. At each
debrief session, we generated a causal loop diagram to document our
understanding of the internal and external driving forces that were
important
to the organization. An example of such a causal model can be found in
Figure
2.
The
causal models that were developed for each site
were preliminary. For each site we captured important environmental
variables
such as political, social, economic and technology forces. We also
recorded our
understanding of internal variables such as organizational objectives,
financial
goals and human resource attitudes and skills. We often color-coded the
model
variables to help visualize the relationships between groups of people
within
the "system" (e.g., customers of the business, suppliers, or the
customer's customer).
Although
generating each of the causal models was
extremely difficult, we quickly saw the benefits of the process. As we
developed the models, we began to identify gaps in our learning. In
some cases,
we realized that our understanding of the organization needed some
additional
input. Conversely, there were several times when we were surprised at
the scope
of our understanding. As we developed a particular casual relationship,
we
could easily draw upon concrete examples from our visit to fill out the
story.
Indeed, developing the causal model forced us to document some
important
understanding that might otherwise have been forgotten. And finally,
the
individual site models were available to the team when we began the
cross-observation meta-analysis. The individual models served as input
to the
composite, more general models that we developed.
Figure 2. Example of causal loop diagram.
The
field research for this project included site
visits to 31 organizations around the world. The volume of data was
every bit
as large as we expected. At the beginning of the project, we believed
that
multiple representations of each field visit would help capture the
richness of
each site. We feel that we succeeded in that respect. The individual
causal
models have been helpful in constructing four more general models.
While the
mind maps have been less useful so far in our meta-analysis, they
remain valuable
representations of three particular aspects of each visit. Taken
together, the
debrief questionnaire, the mind maps, and the causal models provide a
broad
foundation for our continuing analysis.
[1]
Miles, M. & Huberman,
A. Qualitative Data Analysis. Qualitative Data Analysis.
California:
Sage Publications. 1994.
[2] Senge, P., Kleiner,
A., Roberts, C., Ross, R., & Smith, B. The
Fifth Discipline Fieldbook. New York:
Doubleday,
1994.
CHI 97 Electronic Publications: Late-Breaking/Interactive Posters