Estrategias Territoriales Participadas y su continuidad con las EDUSI como instrumentos de innovación social El caso de la

Data in qualitative research tends to be unstructured, or comprised of both formal and informal information (Jacob, 1982). In social research, data is characterized as a form of expression or communication that is, spoken, written, and/or image-based (Bauer, Gaskell, and Allum, 2000).

Accordingly, the data within this study was complex. Due to my prior experience with LINCS (Porter, et al., 2014), I knew that there would be a large quantity of complex data, so I decided at the start of the study to narrow the main concentration to the manageable number of eight participants. Having a manageable number of participants and a narrowed focus of types of data helped me to navigate the challenges of sorting, processing, and analyzing a large set of complex data. Furthermore, having a manageable number of students positioned me to comprehensively analyze each student’s contribution over time and media.

Furthermore, I used the qualitative data management software NVivo 10 for Mac to aid in my analysis. NVivo allowed me to categorize my information and to discern patterns and relationships within the data. NVivo facilitated the organizing and coding of art productions, artifacts from the meetings, transcriptions from interviews, meetings, and the video project, and photos from the meetings. Different types of information were uploaded differently. For example, information in the form of Microsoft Word documents could be uploaded and coded in its original form.

However, image-based information was not as simple to upload in a codeable form because of the version of NVIvo I used. First, I scanned image-based information into PDF documents, if it could be scanned. I could then upload these PDFs into NVivo and code them as an entire document. Next, I processed differently the other image-based information that could not be scanned, such as digital photos and sculptures. Because my Mac version of NVivo 10 does not upload JPEGs (a core function of other versions of NVivo), I embedded my photos in a Microsoft Word document and uploaded that document, as per the suggestion of QSR International, the producer of NVivo. For example, I photographed sculptures, placed the JPEGs of the sculptures into a table within a Microsoft Word document, and gave descriptive textual

support in the column next to the column containing the photo. Similarly, I placed all other digital photos into a table within a Microsoft Word document and gave descriptive textual support next to each photo.

At this stage I encountered a coding dilemma, because PDFs could only be coded as an entire document within my version of NVivo. I needed to code specific selections of the image for more precise analysis of the data. To navigate this challenge, I relied heavily upon the use of memos, “a type of document that enables [me] to record the ideas, insights, interpretations or growing understanding of the material in [my] project” (QSR International, 2016, n.p.). Linked to each PDF, I created a memo that systematically described information about the document, such as type, theme, title, page number (if part of the Final Portfolio), images, text, layout design, and notes regarding ideas, insights, interpretations, or my growing understanding of the material. Memos allowed me to code information that was un-codable in its original form. Once uploaded, I sorted the information into organized folders within NVivo and began coding.

In NVivo, coding begins with categories or containers that are referred to as nodes, and multiple subcategories can exist as subordinate nodes attached to each node. This is called a hierarchical node tree, and it is used in NVivo for organizing the data into more manageable structures that enabled my analysis of the data. It is a method of branching key elements of data from other key elements and then relating them to other key elements. The formation is similar to a tree with branches that relate to each other. A hierarchical tree node consists of containers for a “theme or topic within your data” (QSR International, 2008, p. 109) that are organized, “moving from a general category at the top (parent node) to more specific categories (child nodes)” (p.111). Large amounts of qualitative data, such as mine, can be very difficult to

manually organize and analyze in a methodical way for easy access and manipulation. NVivo facilitated this process in my study.

The coding scheme contained codes that I generated deductively. Aspects of the study were logically identifiable and were coded accordingly. For example, I coded data according to author or artist through case labeling. I also coded data according to type of practice, such as the modes of artmaking, including photography, drawing, creative writing, etc. I objectively determined such coding schemes deductively, because they logically belonged to particular categories without subjective determinations. This process was facilitated by the construction of classification sheets, a method of organizing that allows me to “see all the items assigned to a particular classification and see the attribute values set for each item” (QSR International, 2016, n.p.). I used classification sheets to see how specific data were linked to specific students in my study. First, I created classifications for each participant by entering attributes in order to create a classification sheet. Next, I coded the data, such as art productions, transcriptions, and memos, according to the student to whom they belonged. This approach allowed me to store relevant data in a specific place for easy access. It further enabled me to analyze data by determining whether particular data was idiosyncratic, or part of larger patterns.

Deductively-generated coding was the predecessor to inductively-generated coding, wand it provided clarity. For example, the modes of artmaking were deductively coded according to the mode of the piece, such as a Final Portfolio page. The capacities that were exhibited in the page were determined inductively through my positionality, understanding the artist, the purpose of the page, the on-site service experience behind the photos and journaling on the page, the available materials for page construction, and also by observing the artist as he or she created the page. Through the combination of my expertise on the topic and my positionality as participant

observer, I was able to recognize the categories within the data. For example, the Capacities for Imaginative Learning (Holzer, 2009) describe attributes that define arts-based learning, such as embodying, creating meaning, living with ambiguity, noticing deeply, and identifying patterns. Recognizing these attributes among my students required a keen understanding of each one, as well as careful consideration of the students’ personalities, interests, expressions, personal backgrounds, home life, school life, strengths, and weaknesses. This was a benefit to having a sample size of eight students. It simultaneously required careful consideration of the learning processes under review. Thus, as the participant observer, I was uniquely positioned to take into consideration the primary aspects of the entire study and the nuances therein, which allowed me to inductively generate codes.

Integration of deductive and inductive coding through matrices was a method of “cross- tabulating coding intersections” to aid in making “comparisons and see patterns” (QSR International, 2016, n.p.). Figure 4 below is an example of a matrix:

Figure 4: Example of matrix query from NVivo 10 for Mac

In the following chapter (of which this matrix is exhibited), I use a matrix to compare the capacities with the modes of artmaking, which facilitates my understanding regarding which

kinds of capacities the students gained in arts-based learning. In chapter five, I use another matrix is to compare the capacities with the service-learning, specifically the projects and components that became a mapped-out sequence of stages that students experienced. The stages were an amalgamation of the common stages in the literature, specifically Kolb’s (1984) Experiential Learning Cycle and Brown and Leavitt’s (2009) stages of service-learning. This matrix helps me to analyze what kinds of skills, awareness, or capacities the students gained in service-learning. In chapter six, I use a third matrix to compare the modes of artmaking with the meta-categories that I constructed. Here, I consider the project life cycle more intensely, and analyze how these relationships (i.e., these three matrices) illuminate the development of sense of community. Thus, my coding scheme allowed me to see which types of attributes co-occurred with which sets of outcomes.

I also investigate with the use of coding stripes, “colored bars in the margin of NVivo sources” (QSR International, 2016, n.p.). See Figure 5 below:

Figure 5: Example of coding stripes in NVivo 10 for Mac

Likened to highlighting, coding stripes allow researchers to manage the data through viewing how they were categorized and to ensure that they were indeed categorized accurately. Coding stripes show the overlapping presence of coding for each conceptual node. Through the ability to readily see what sections of data were coded within which categories, I was better equipped to practice coding consistency.

Qualitative data analysis computer programs allow researchers to structure large amounts of qualitative research data “in meaningful and systematic ways, code that data with an extensive concept and variable scheme, and retrieve the data in ways that allow the user to evaluate patterns in the data” (Abramson, 2009, p. 71). The purpose of using a program such as NVivo was not to turn my qualitative data into quantifiable data to be used for statistical analysis.

Instead, NVivo allowed me to “reference and cross-reference occurrences in ways that make the analysis of patterns more systematic and less anecdotal” (Abramson, 2009, p. 71). Coding stripes helped to visualize patterns. For example, I could see how themes overlapped and related to one another. Upon reviews of the meeting transcripts, interview transcripts, art productions, and field notes, I analyzed the data further to determine if my themes and patterns were consistent with the data.

I was better able to make that determination through queries, analytical tests that “provide a flexible way to gather and explore subsets of [...] data” (QSR International, 2016, n.p.). More specifically, I created queries that can “find and analyze the words or phrases in [the] sources, annotations and nodes,” “find specific words or those that occur most frequently,” or “ask questions and find patterns based on [the] coding and check for coding consistency among team members.” In this manner, I selected, set up, and ran queries.