Subvenciones para la movilidad de los estudiantes

In the second analytical layer of this project, all of the data sources – field notes, surveys one and two and the 10 in-depth interview transcripts – were coded using NVivo. The first step was to identify each document with the “zip code (dash) month and day of birth” format in order to ensure that data

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would be correctly and consistently (but anonymously) attributed when coding and analyzing data across the three sources. In the field notes, whenever possible, participants were identified using this code (zip code – month and day of birth); however, when participants’ identity was unknown, they were identified as “participant one [two, three]” … etc.

Of the 10 participants represented in the surveys, many participants did not complete both survey one and survey two; however, I personally interacted with each of these participants at both meetings and was therefore able to verify their attendance (and that they met the requirements I had established for the study population).

After formatting the field notes and in-depth interviews consistently, I conducted initial coding in Microsoft Word, in which I assigned “first impression” phrases to attach descriptive and in vivo codes to nearly every line of text within the notes and interviews. I used a grounded theory design and constant comparative method to code the field notes and in-depth interviews⁷ because this theory provides tools specifically for learning about individuals’ perceptions and feelings regarding a particular subject area.

Grounded theory offered me a powerful methodological framework because the aim of this study was to learn about individuals’ perceptions and feelings about a particular subject (Gorra, 2007). The objectives of grounded theory involve:

 Focusing on everyday life experiences

 Valuing participants’ perspectives

 Approaching inquiry as an interactive process between researcher and respondents

 Preserving respondents’ language (Marshall & Rossman, 1999)

In grounded theory, the researcher goes through multiple stages of collecting, refining, organizing and categorizing the data (Strauss & Corbin, 1990). The “constant comparative method” used in this approach consists of developing concepts from data by coding and analyzing the data at the same time

7 Surveys were coded using NVivo’s “auto-code” tool because these sources were structured consistently;

they asked the same set of questions, the majority of which were multiple choice.

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(Taylor & Bogdan, 1998). To begin this process, first I conducted initial or “open” coding in order to understand the context of the data I was working with; during this phase, I did not search for patterns, or further manage, filter or focus the data. After completing this initial phase, I saved two versions of the field notes and in-depth interviews – an original version and a coded version.⁸

After this first cycle of “broad-brush” initial coding in Microsoft Word, I imported all original (un-coded) versions of the field notes (two documents), COAST surveys (nine documents) and interviews (10 documents) into NVivo and began the second cycle: focused or “selective” coding. In this phase, I manually coded the data using the “drag and drop” function in NVivo to match data sets with their appropriate “nodes.”⁹ In order to focus this process, I created a memo identifying my research questions and the most frequent terms/phrases I had identified during the initial coding phase. As I coded the interviews and field notes in NVivo, I used this memo as a guide to ensure that I was primarily coding data that would enable me to answer the specific research questions of this project. See Appendix C for a list of the 28 codes developed during this phase.

I began the next level of analysis, called “axial coding,” by using NVivo’s word frequency query tool to obtain a list of the top 50 most frequently used words within all of the data sources.¹⁰ In grounded theory, axial coding is “the act of relating categories to subcategories along the lines of their properties and dimensions” (Strauss & Corbin, 1998, p. 123). The purpose of this phase of coding was to add depth and structure to my existing nodes/categories (Gorra, 2007). The 10 most frequently used words, in order from most used to least used, were: floods; level; adaptation; concerned; people; buildings; property;

models; regional; and elevation.¹¹ Although this information wasn’t directly applied in further analyses of

8 See Appendix A for original in-depth interview documents and Appendix B for coded in-depth interviews.

9 A “node” is the term NVivo uses to refer to a collection of references about a specific theme, place, person, etc. which allowed me to view all participants’ references to a particular theme (e.g., all references to “visualization tools”) in one place. Once refined by various phases of analysis, my nodes became my codes.

10 Grounded theory methodology typically does not use quantifying data to obtain meaning; however, counting the frequency was useful for showing me these terms’ importance for interviewees (Gorra, 2007).

11 See Appendix I for the complete list of most frequently used words in the data.

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this data, it provided a useful initial “snapshot” of the most significant themes within the synthesized nodes.

Next, in order to eliminate redundancies and to determine the most appropriate nodes for

answering the research questions of this project, I used NVivo’s “merge” tool to merge content from one or more nodes into an existing node – which eliminated a significant number of nodes. For example, I merged the original node “visualization” into the node, “COAST approach,” and the nodes

“development” and “resiliency” into the node, “development/the real estate market.” Grouping several nodes/codes into categories through the merging process was the first step of theory-building, which is addressed fully in Chapters Three and Four.

As a result of this process, I reduced the number of nodes and identified five nodes/codes to use in my analysis of the in-depth interview and field notes data:

 COAST approach

 governance

 barriers to adaptation

 innovation

 development/the real estate market¹²

Analysis of the survey data provided trending information about the 10 participants’

demographics, level of concern and experience with coastal hazards, preferences for funding of

adaptation strategies, governance of adaptation policy and their perceived local barriers to adaptation. For example, for the Workshop One survey question, “Do you agree or disagree with the following statement:

Implementing projects to reduce potential impacts of climate-related hazards in our community should be a local or regional government priority, even if it will require a slight increase in taxes or new fees?” out of the participants who attended both workshops, five selected “agree strongly”; however, on Survey

12 See Appendix C for a complete codebook, including definitions and examples of the codes used in this project.

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Two, within this same population, two selected “agree strongly” and two selected “agree somewhat.” The reasons for this discrepancy are explored in Chapter Three.

The survey data was used to provide demographic information about participants’ professions (their role in the community), gender, educational background and political affiliation, as well as their individual experiences with coastal hazards (e.g., storm surge, sea level rise), an assessment of their level of concern about short- and long-term hazards, preferences for and barriers to adaptation and support for adaptation funding options. Four of the nine surveys represent matches; participants who completed both Survey One and Survey Two. These matches allowed me to assess any changes in participants’

preferences for adaptation action, funding, barriers to adaptation, level of concern about short- and longer-term hazards and support for their preferred timeframe for action (e.g., now, in the next 10 years, in the next 100 years, or never). This data was primarily useful for answering research question four in this project, “What are the challenges and opportunities of engaging local stakeholders in adaptation planning?” which is taken up in Chapter Four of this project.

The next chapter of this project presents the findings of the data sources described within this chapter, providing answers to research questions one and two:

 What opportunities/barriers do stakeholders deliberate about when responding to the modeling predictions generated by COAST? In relation to these barriers, how does stakeholders’ deliberation reinforce/delimit the significance of the prediction imperative for decision making processes in contexts of deep scientific uncertainty?

 What are the implicit values embedded within stakeholders’ perceptions of coastal vulnerabilities?

Part One of Chapter Three focuses on data collected in the five codes listed above: COAST approach; governance; barriers to adaptation; innovation; and development/the real estate market. It also identifies the specific barriers that stakeholders expressed, as well as their insights into and optimism about potential adaptation opportunities, focusing on the influence of the prediction imperative on their

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values and preferences about modeling data for adaptation planning and decision making. Part Two of Chapter Three provides a comparison of participants’ values with traditional assumptions about climate change communication (e.g., framing climate change as a scientific problem; increasing scientific literacy with the assumption of increasing support for climate change policies; top-down education of

non-scientific publics; and providing information about climate science without providing viable solutions.¹³ Identifying these values provided insight into the specific, situated experiences and preferences of stakeholders in this region, which subsequently informed how to shape communication, messaging and framing about climate change and feasible adaptation options for this region (the purpose of Chapter Four of this project).

13 See Chapter One, “Climate Change Framing and Communication.”

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CHAPTER THREE

FINDINGS: BARRIERS TO ADAPTATION AND THE ROLE OF THE PREDICTION IMPERATIVE IN CLIMATE MODELING AND ADAPTATION PLANNING