Writing Studies research has long attended to and included visuals, especially in the sub-fields of visual rhetoric, professional writing, and technical communication. For example, scholars have examined and developed taxonomies of the relationship between words and images, the visual design of documents, and the composition of different kinds of visuals and their impact upon audiences. There appears to be renewed interest in studying and coding visuals in writing beyond the university research, especially when researchers ask writers to draw maps or diagrams representing their writing processes, rhetorical choices, and potential moments of transfer.

The Process of Coding Visuals

In a very general way, coding visuals is similar to coding written text, as this recommended approach to coding and analyzing visual images suggests:

Step 1: Prepare your data for analysis…

Step 2: Code the image by tagging areas of the image and assignment code labels. Some codes might involve meta-details (e.g., the camera angle)

Step 3: Compile all of the codes for the images on a separate sheet

Step 4: Review the codes to eliminate redundancy and overlap. This step also begins to reduce the codes to potential themes.

Step 5: Group codes into themes that represent a common idea

Step 6: Assign the codes/themes to three groups: expected codes/themes, surprising codes/themes, and unusual codes/themes. This step helps to ensure the qualitative ‘findings’ will represent diverse perspectives.

Step 7: Array the codes/themes into a conceptual map that shows the flow of ideas in the ‘findings’ sections. The flow might represent presenting the themes from a more general picture to a more specific picture.

Step 8: Write the narrative for each theme that will go into the ‘findings’ section of a study or for a general summary that will go into the ‘discussion’ section as the overall findings in the study.

Creswell and Creswell 2018, 197-198

While the above model might reassure researchers that the process of coding visuals isn’t all that different from the process of coding alphabetic text, it doesn’t offer much advice about how to approach generating, defining, or applying the actual codes themselves. Interestingly, other disciplines have developed their own approaches to analyzing visuals that are unique to their own fields or topics. Examples include:

• “Coding Visuals in Biology Textbooks” (Parthasarathy and Premalatha 2020),
• “Multimedia Content Coding and Analysis: Unraveling the Content of Jihad Extremist Groups’ Videos” (Salem, Reid, and Chen 2008), and
• Integrative Framing Analysis: Framing Health Through Words and Visuals (Dan 2018).

Approaches to Visual Analysis

Theo Van Leeuwen and Carey Jewitt’s The Handbook of Visual Analysis (2001) provides a review of different approaches to visual analysis in its introduction (with subsequent chapters exploring each approach in more detail), including content analysis, visual anthropology’s use of visuals as records, cultural studies, semiotics and iconography, psychoanalytical image analysis, social semiotic visual analysis, and conversation analysis and ethnomethodology (social practices including non-verbal communication like maps and diagrams). Bridget Turner Kelly and Carrie A. Kortegast’s Engaging Images for Research, Pedagogy, and Practice: Utilizing Visual Methods to Understand and Promote College Student Development (2018) likewise includes a useful review of visual research methods with an eye towards educational research. This edited collection includes an “Overview of the Use of Visual Methods in Research” (Denton, Kortegast, and Miller 2018), which concisely explains several visual analysis approaches, including photo elicitation, participant-generated photo elicitation, photovoice, visual content analysis, arts-based inquiry, and drawing, painting, and graphic elicitation. Finally, Diane Pennington’s “Coding of Non-Text Data” (in The Sage Handbook of Social Media Research Methods, 2017) also offers a similar review of research methods for analyzing and coding non-text data found on social media, such as “content analysis, document analysis, compositional interpretation, musical analysis, cultural studies, visual sociology, visual anthropology, semiotic analysis, iconography/iconology, discourse analysis, visual social semiotics, and multimodal research.” Many of these approaches to coding visuals are similar to those used by scholars when coding alphabetic text: identifying common themes or patterns in the visuals’ content (content analysis), the visuals’ design or layout (compositional interpretation, visual social semiotics), or the meaning that is made given the wider context in which the visual is situated (musical analysis, arts-based inquiry).

Content analysis or analysis of drawing, painting, and graphic elicitation are a few approaches that seem to most closely align with coding writer-drawn mappings or diagrams of writing processes, experiences, and potential moments of writing transfer between writing beyond the university contexts. As Denton, Kortegast, and Miller argue, “Although more prominent in other disciplines, the use of nonphotographic drawings and graphics in higher education research is relatively rare; however, some examples of such scholarship do exist…. Although this small body of scholarship differs in its degree of criticalness (i.e., the degree to which these studies interrogate power structures), these scholars share a common belief that new kinds of knowledge and insight can be accessed through drawings, paintings, and graphics” (2018, 22). This points to the value of writer-drawn mappings to access ideas that might otherwise go unarticulated.

Graphic Elicitation

The concept that interviewee mappings might reveal knowledge that would otherwise go unnoticed is supported by Alison Bravington and Nigel King (2018) in “Putting Graphic Elicitation Into Practice: Tools and Typologies for the Use of Participant-Led Diagrams in Qualitative Research Interviews.” They describe graphic elicitation as “the use of diagrams to stimulate dialogue in research interviews” (506), and caution researchers to consider that the type of diagramming options offered to interviewees impacts their diagramming choices. Bravington and King offer terminologies to help researchers consider the diagramming options available to interviewees (although these terminologies could also be helpful to researchers who have already collected mappings and are in the coding stage).

Interestingly, Bravington and King note that it’s not just the diagram that matters and conveys meaning, but the process of creating that diagram as well: “The current literature on diagramming covers a limited range of techniques, and takes a top-down approach, focusing on fixed diagram structures used by specific disciplines rather than examining the process of diagram creation and how this might capture social phenomena and personal experience” (2018, 518). They recommend that researchers “consider how the use of diagramming impacts on and is affected by the participant-researcher relationship, and how the diagrams that are produced might go beyond their role as an elicitation tool and contribute to data analysis” (518). According to Bravington and King, graphic elicitation can be particularly helpful for researchers in studying the personal and social lives of participants: “Participant-led diagramming may be worth considering for research studies focused on social interaction, events and/or processes, or meaning-making. Relational diagrams can help participants to articulate their experiences of social or professional networks and interactions, and can be used as a framework for exploring support or collaboration based around a specific task or event” (517).  They note that diagraming as a methodology has potential limitations (which may need to be taken into account when coding them as well). These limitations include participant distress when reflecting on certain events, struggles to remember, difficulty thinking about events in a linear way, or trouble conceptualizing ideas that may be impacted by cultural background (517). These limitations point to considerations researchers might take into account when coding as well.

Concept Mapping in Educational Research

An example of concept mapping as a methodology in educational research is found in Kinchin et al.’s “Researcher-Led Academic Development.” They explain the value of their map-mediated participant interviews, a strategy that closely aligns with the writer-drawn mappings increasingly popular in writing beyond the university research: “The maps and the associated narratives… form the data for this study from the six co-authors who acted as interviewees for this analysis. The narratives were analysed for themes relating to values and beliefs about teaching and learning.…” (2018, 342), and “Due to the exploratory nature of this study (looking at identifying connections between concepts), the use of concept maps to represent individual perceptions was considered to be particularly appropriate here. They are dynamic constructs rather than static representations” (341).

Scholars conducting writing beyond the university research using writer-drawn mappings of writing processes, writing behaviors, and potential moments of writing transfer will likely find themselves faced with questions about coding and analyzing visuals, a method which is newer to writing studies compared to some other fields. However, we may draw upon more generalized processes for coding visuals, as well as strategies developed in disciplines such as education, to develop procedures and strategies for coding visuals within our own field.

Paper vs. Software Coding

When coding visuals, researchers might code the visuals and track code occurrences in an “old-fashioned” way, using paper, colored highlighters, sticky notes, and a simple Word document. Qualitative coding software might also be used, facilitating more sophisticated data analysis. Some software allows different image formats to be imported and coded, while others permit only PDFs, in which images or visuals could be digitized and saved as a PDF for coding.

Helpful Resources

Fee-based software options that accommodate coding visuals: 

• https://www.dedoose.com/ (only pay for the months you use)
• https://www.qualtrics.com/
• https://www.tableau.com/

Free software options that accommodate coding visuals:

• http://www.connectedtext.com/
• https://www.qcamap.org/ui/home
• https://tamsys.sourceforge.io/

Articles like the “Best Qualitative Data Analysis Software | 2020 Reviews of the Most Popular Tools & Systems​” (select “free” in the pricing options filter at the top) might be helpful for researchers seeking free software options for coding visuals, however please note that the software options, and their status as free, often changes quickly.

Works Cited

Bravington, Alison, and Nigel King. 2018. “Putting Graphic Elicitation Into Practice: Tools and Typologies for the Use of Participant-Led Diagrams in Qualitative Research Interviews.” Qualitative Research 19, no. 5: 506-523.

Creswell, John W., and J. David Creswell. 2018. 5^th ed. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA: Sage Publications, Inc.

Dan, Viorela. 2018. Integrative Framing Analysis: Framing Health Through Words and Visuals. New York: Routledge.

Denton, J. Michael, Carrie A. Kortegast, and Carrie Miller. 2018. “Overview of the Use of Visual Methods in Research” In Engaging Images for Research, Pedagogy, and Practice: Utilizing Visual Methods to Understand and Promote College Student Development edited by Bridget Turner Kelly and Carrie A. Kortegast. Sterling, VA: Stylus Publishing, LLC.

Kelly, Bridget Turner, and Carrie A. Kortegast. 2018.  Engaging Images for Research, Pedagogy, and Practice: Utilizing Visual Methods to Understand and Promote College Student Development. Sterling, VA: Stylus Publishing, LLC.

Kinchin, Ian, Marion Heron, Anesa Hosein, Simon Lygo-Baker, Emma Medland, Dawn Morley, and Naomi Winstone. 2018. “Researcher-Led Academic Development.” International Journal for Academic Development 23, no. 4: 339-354.

Parthasarathy, J., and T. Premalatha. 2020. “Coding Visuals in Biology Textbooks.” The International Journal of Analytical and Experimental Modal Analysis 12, no. 10: 790-794.

Pennington, Diane. 2017. “Coding of Non-Text Data.” In The Sage Handbook of Social Media Research Methods, edited by Luke Sloan and Anabel Quan-Haase, 232-250. London: SAGE Publications, LTD.

Salem, Arab, Edna Reid, and Hsinchun Chen. 2008. “Multimedia Content Coding and Analysis: Unraveling the Content of Jihadi Extremist Groups’ Videos.” Studies in Conflict & Terrorism 31, no.7: 605-626.

Van Leeuwen, Theo, and Carey Jewitt, eds. 2001. The Handbook of Visual Analysis. London: Sage Publications Ltd.

Paula Rosinski is Director of Writing Across the University in the Center for Writing Excellence and Professor of English: Professional Writing & Rhetoric at Elon University. She is co-leading the 2019-2021 research seminar on Writing Beyond the University: Fostering Writers’ Lifelong Learning and Agency.

How to Cite this Post

Rosinski, Paula. (2021, March 11). Coding Visuals in Writing Beyond the University Research [Blog Post]. Retrieved from http://www.centerforengagedlearning.org/coding-visuals-in-writing-beyond-the-university-research