Figure 2: Participant 33139-0615’s responses to the in-depth interview questions. The concepts with the most links are concept A, “conversations about economics and funding” and concept B, “assuming that high-income buildings are of most concern” as opposed to “considering human aspect.”
After this phase was complete, I drew links between the concepts on the map using the software’s arrow tool, which designates the cause-and-effect relationship, “may lead to” (e.g., sea wall [may lead to] localized protection during storm event). The links are extremely important in that they illustrate the interrelatedness and interdependencies between concepts, which helped to clarify and define, both visually and logically, the specific problem of the situation (i.e., how to respond to coastal risks given local stakeholders’ various perceptions of socio-economic vulnerability).
2.4.1 Methods of Analysis
Most of the analytical functions in Decision Explorer are designed for very large amounts of data (maps that contain 150 concepts or more) as well as for determining the means (i.e., solutions) by which a concept or goal can be achieved. The maps created for this project consist of an average of five concepts;
therefore, many of analytical functions available weren’t necessary or appropriate for analyzing this data concept A
concept B
36
set. Additionally, the purpose of this dissertation was to provide an empirical example (and not a framework) as insight into options in a range of alternatives that can be utilized in developing usable science policy for regional adaptation. This project’s purpose was not to recommend solutions for coastal adaptation in this region because doing so would not respond to the need for attention to the process of how policy making occurs in contexts of deep scientific uncertainty. The value of this project is its insight into a regional policymaking approach (Tompkins, Few & Brown, 2008). Furthermore, recommending solutions would suggest that there are universal barriers to adaptation and “best practices” for how to overcome them. Rather, the purpose of this project is to offer insight into the process of deliberation as a part of informing “usable” science. Therefore, of the 16 analytical functions available in Decision Explorer, the only functions used in this project were those that matched this purpose and these objectives.
After exploring the available functions, I identified four of the 16 that provided a more explicit picture of stakeholders’ deliberation about their preferences regarding adaptation options for their region, insight about barriers to adaptation, opportunities and ideas for innovative solutions to coastal
vulnerability and values about their local economy (in particular, development/real estate market). These four functions were:
Heads
Cluster
Domain
Centrality
One of the most basic analytical methods functions in Decision Explorer is the “heads” function, which I used to identify concepts on the map that did not have any links either coming into or going out of them. These concepts were the outcomes, goals, or targets of decision making. Another analytical function, called “cluster analysis,” was used to identify groups of related ideas by highlighting relatively isolated “islands” of concepts where there were a minimum of connections between the islands; resulting
37
in clusters that were mutually exclusive. This mode of analysis was based upon the structure – and not the content – of the map, showing the intensity of linkages between concepts.
Using domain analysis, another analytical function, I considered the link structure immediately surrounding a particular concept and identified highly linked concepts, focusing on the connectivity between those concepts. This analytic function was important because it allowed me to see the “busiest”
concepts on the map; the concepts that were key issues.
The centrality analysis function considered the structure of the map by analyzing the whole map and designating a score for each concept. Concepts that were very influential (concepts that had the most links coming into and going out of them) were scored highly, revealing the most significant concepts within the map. Scores were calculated according to the number of concepts within a particular concept’s
“band,” which is the term Decision Explorer uses to refer to the concepts deviating from the central concept. See Figure Three below.
Figure 3: Diagram of a Decision Explorer central analysis function showing three bands around a central concept.
band
concept A
concept B
concept C
38
Each concept was weighted according to how many subsequent concepts were traversed in its band levels. All concepts at level one were divided by one, all concepts in level two were divided by two, etc. Each band score was then added together to give a total overall score for the concept. This mode of analysis allowed for insight into the significance of the various layers of meaning within a concept on the map; therefore providing further understanding of the reasoning motivating the concept (Decision Explorer Online Reference, 2014). For example, one of the concepts on participant’s 33139-0615’s map is: “conversations about economics and funding.” (For this example, I call this “concept A”.) One of the concepts related to concept A, “assuming high-income buildings are of most concern … considering human aspect” (concept B) had the most links coming into and going out of it; it had numerous concepts in its band. Another concept, “money is the primary issue … humans/residents are the primary issue”
(concept C) is related to concept A, but doesn’t have as many links coming into and going out of it as concept B; its band wasn’t as “heavy” as the band in Concept B. As a result, concept B, “assuming high-income buildings are of most concern … considering human aspect,” is weighted more heavily than concept C and therefore represents the priority for a majority of stakeholders.
These heavily weighted, priority concepts represented participants’ values and beliefs, which informed the development of frames that were suggested to have strong resonance with local stakeholders (the purpose of research question three, “What frames for environmental change engage stakeholders in decision making about adaptation actions in this region?”).
Lastly, the “printing lists” function of Decision Explorer produced a list of concepts/map contents in a text view, which I then scanned again for relevant codes to inform the coding terminology and definitions I then created in NVivo, the second layer of qualitative analysis used to analyze my data.