Transcripts Photographs Field Notes Audio Files Secondary Text
4 152 5 4 1
The above table gives a summary of the data sources that I compiled from my research as described above. I imported all of these sources into Atlas.ti, a qualitative data analysis and research tool. I then began the iterative process of qualitatively coding
my data, which is the process which links data collection to meaning (Charmaz 2001).
Coding is interpretive and adds value to a research story by distilling, summarizing, and condensing data – it’s the beginning of a process that allows you to group ideas into patterns (Saldaña 2012). Saldaña advises researchers to code “anything and everything that was collected” including all the documents related to participant activities and my own reflective documents. I used Atlas.ti to assign, organize, and categorize qualitative codes, as well as extract supporting quotes and visual evidence for the resulting narrative.
I used a coding process recommended by Saldaña in his very helpful book on qualitative coding. He provides choices of coding methods at each step, and I chose those that would be most appropriate to my subject matter and data sources:
1. Attribute coding: Basic descriptive information such as fieldwork setting, participant characteristics, data format, time frame; metadata.
a. In my study, I coded all pieces of data with:
i. Participant ID
ii. Participant Plant ID Skill level
iii. Which of the 5 plants the Participant can positively ID, if any iv. Experiment group (Image/Text, Video, or In person)
v. Data type (Field Notes, Photograph, Transcript, Audio or Secondary Text),
vi. Date vii. Location
viii. Location Type (Grassland, Forest, or Urban Border)
2. Holistic coding: particularly appropriate for analyzing mixed data forms such as media and interview transcripts with bounded parameters of time and place; about grasping the theme of a piece of data as a whole, interpretive. Completed for all data, one code per datum.
3. Descriptive coding: summarizes data using a word or a short phrase, usually a noun. Completed for all data. One per photograph or audio file, many for text files.
4. In Vivo Coding: Provides voice and agency to participants; terms “that participants use in their everyday lives rather than in terms derived from the academic disciplines or professional practice” (Stringer 1999).
Completed only for transcripts. Many per file.
5. Emotion coding: labels the emotions experienced by the participant.
Completed only for photographs. One per photograph.
I finished each coding step for a session before I began the next step of coding. I also coded data from each participant (or group) in its entirety before moving onto the next, so that each session was as internally consistent as possible. Each type of code (Attribute, Holistic, Descriptive, In Vivo, or Emotion) was color coded so I could easily see at a glance what type of code a specific tag was and where I was in the analysis process for a piece of datum at any point in time.
Unlike textual analysis, where some automation can allow a computer to look for quantitative measures such as word frequency and perform complex searches, image coding is entirely interpretive (Banks 2008). Computer vision is not quite advanced enough to recognize conceptual differences between objects such as “house” versus
“box”, at least at the consumer level quite yet. Photographs were central to my data collection and required some extra thought on how to code them with care. Scholars certainly vary in their perspectives on how to tackle coding images. Banks takes two perspectives: one, you can code the “internal narrative”, which is the content of the images and what you can see; or you can code the “external narratives” what you can’t, such as the stories and context in which the image takes place (2008). Saldaña actually does not recommend coding photographs directly as data, but instead coding the
researcher’s “careful scrutiny of and reflection on images” using “rich, dynamic words”
(2012). I decided to use Caldarola’s interpretation of pictures as event-specific
representations (not generalized), which are context-dependent, social events, involving communication and mutual understanding, when I coded my images (Caldarola 1985).
Lichtman recommends that researchers use 80 to 100 codes that fall in
approximately 15-20 categories that later get distilled into 5-7 major themes (Lichtman 2010). While I did not maintain categories or a count as I was coding my data, I did end up aiming to reduce my codes into a small number of major themes using code mapping after the first round of coding was complete. The in vivo coding meant I had significantly more than 100 codes after my first round (1434, to be exact). I used Atlas.ti’s Code Manager to code map my first round into meaningful categories, eliminating codes that did not seem to be as important to my analysis. Code mapping (grouping codes) into categories over a number of iterations helps a researcher “to bring meaning, structure, and order, to the data” (Anfara 2008). I did two rounds of code mapping; first creating 15 categories; then narrowing my analysis to four emergent themes using post-it notes.
While some of categories I eliminated or condensed included incredibly interesting data,
it was outside of the scope of this work (e.g., usability feedback or motivation for participation in citizen science studies).
CHAPTER V EMERGENT THEMES
Plant identification is a difficult task requiring significant expertise, so I was able to investigate how visual media within the Outsmart app did or did not help the students participate in an activity that was often outside of their comfort zone. The app uses text descriptions for plants, with advanced language and jargon, so I was able to compare the utility of visual media to that of scientific language. Additionally, because Outsmart requires citizen scientists to submit photographs of the invasive species when making a report, I also was able to interrogate how the act of creating an image could be
empowering for users. Would it feel engaging to see a photograph you’ve taken of an invasive you’re reporting that is very similar to an image that appears in the app (an image created by experts)? Is the active participation in research something that feels natural or is the process too convoluted to be empowering? The table below summarizes the four separate participant observation sessions used in this case study. From these sessions, I was able to generate four emergent themes about how visual media is used by non-expert Outsmart participants in relation to language, creation of photographs, and participation if a citizen science project.
Table 2: Participant Observation Results Summary ID Date Session
Length
Pictures Skill Level Gender
01 9/23/13 41 min 21 Beginner Woman
02 9/24/13 1 hr, 18 min 45 No experience Men
03 9/24/13 42 min 40 Beginner, No Woman, Man,
(group) experience, Beginner
Woman
04 9/25/13 1 hr, 3 min 46 Intermediate Man