Systematically Coding Qualitative Data

By Charles Vanover, PhD and Paul Mihas, PhD

In this guest blog post Charles Vanover and Paul Mihas, former SAGE Methodspace Mentors in Residence, discuss qualitative data analysis and and a symposium on featuring three well-known qualitative research teams. Qurikos software’s Daniel Turner; The University of Colorado Colorado Springs’ Andrea J. Bingham and Patricia Witkowsky; and the University of Houston Elsa M. Gonzalez and Texas A & M University’s Yvonna S. Lincoln share insights on how to code and categorize qualitative data. These presentations are part of a session offered at the International Congress of Qualitative Inquiry (ICQI), on Saturday May 22, 2021.

Qualitative data presents researchers with a vast range of choices.

Interviews, observations, photographs, videos, Tweets, works of art, and other content may be collected for qualitative studies. Qualitative researchers must learn how to engage with these data and identify patterns. Coding is a tool to focus attention on the data and create records about important relationships and materials. It is a form of exploration. Coding allows people to read through what might seem to be chaotic and inconsistent aspects of their transcripts and yet find signals that provide shape and coherence.

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Video Contents

Strategies for Coding and Categorizing Data: After the Interview: Edited Zoom Video from ICQI 2021: Saturday May 22, 2021, 9:00 – 10:30 CST, Session # 5.22.10 https://youtu.be/0whjxfvCrjs

00:00 Introduction

02:07 Coding System Design and Management—Daniel Turner

15:01 A Priori and Inductive Approaches—Andrea J. Bingham & Patricia Witkowsky

27:21 Analyzing and Coding Interviews and Focus Groups considering Cross-Cultural and Cross-Language Data--Elsa M. Gonzalez & Yvonna S. Lincoln

45:46 Discussant: Paul Mihas

• Chair: Charles Vanover

• Editor: Trace Taylor

As Mihas (2022) writes, “One of the strengths of coding is that it sustains this period of wonder, of checking and rechecking, naming and renaming, and “diving in and stepping back” (Maietta, Hamilton, Swartout, & Petruzzelli, 2019). Coding creates a conceptual foreground against the larger canvas of copious data. (p. 113).”

There are many ways to code qualitative data, and researchers have described a wide range of practices from many different traditions of qualitative analysis (e. g. Auerbach & Silverstein, 2003; Campbell Galman, 2016; Charmaz, 2014; Lincoln & Denzin, 2003; Saldaña, 2021). Coding helps researchers dwell in the details and bridge cultures, voices, and experiences.

In this blog post we discuss how three qualitative research teams attempt to code and categorize interview data. Our post refers to a set of talks about qualitative data analysis given by contributors to a new book from SAGE, Analyzing and Interpreting Qualitative Research: After the Interview(Vanover, Mihas, & Saldaña, 2022). Readers are welcome to engage with our written discussion of this symposium or go directly to the bottom of the page and click on video from sessions by Daniel Turner (Quirkos software); Andrea J. Bingham and Patricia Witkowsky (University of Colorado-Colorado Springs); and Elsa M. Gonzalez (University of Houston) and Yvonna S. Lincoln (Texas A & M University) at the International Congress of Qualitative Inquiry (ICQI), on Saturday, May 22, 2021.

Collaboration and Coding

In his talk for ICQI 2021, Daniel Turner discusses how research team members might work together and collaborate to code and analyze their data. Qualitive data challenges researchers with materials that offer rich possibilities for meaning making; coding is a tool for responding systematically to this complexity. When done well, coding allows the research team to track members’ unfolding understandings of the patterns, connections, critical content within their data (Turner, 2022). Coding brings order to complexity and helps team members engage with the wealth of meanings. As Daniel emphasizes, the meanings produced by a given coding system are only as good as the design of the system itself. Coding systems may become large, ungainly, and unsystemized without proper forethought and such systems’ operation may inhibit analytic insight. Coding is a way to facilitate the research team’s creative response to the data, but this response must be organized through an intentional process.

The pandemic has increased the number of qualitative research teams who collaborate remotely in online environments. These collaborations require extensive planning and preparation to function effectively, especially without face-to-face meetings. Team members should come to agreement on their methodological approach to the data and decide, for example, whether they will use framework analysis or grounded theory. Members must decide how to engage in the technical details of the analytic process and, for instance, set up a code book that guides the analysis. They must decide how this initial coding system will be used and updated. The research team must choose whether members will engage with full interview transcripts or narrow their focus and examine selected passages. Team members must also decide if everyone will code the same material or focus on different interviews. Another critical issue is who will be authorized to correct or edit the data if members discover errors in the transcripts.

Collaborating on coding and other aspects of data analysis increases the complexity of the analytic process, but, as Daniel emphasizes, teamwork may allow people to share workloads and combine efforts to improve quality. Collaborative analysis is a great way for team members to teach each other important research practices and develop skills to manage the complexity of qualitative inquiry. Daniel emphasizes the most valuable knowledge produced from collaborative qualitative research may be found, not in areas of agreement, but in the ability to make sense of differences of interpretation between team members.

Using Different Types of Reasoning in Analysis

Andrea Bingham’s talk for ICQI 2021 explores some of the core strengths and tensions of qualitative research. Qualitative inquiry allows researchers to investigate emergent phenomenon. To successfully engage in such inquiry, however, researchers must balance their efforts to respond to the data based on perspectives in the research literature with their efforts to respond more directly from research materials. Andrea discusses a five-cycle process she and her colleague Patricia Witkowsky (2022) developed for moving between deductive and inductive practices of qualitative analysis. This process provides sets of practical steps for balancing researchers’ need to work within their academic disciplines with qualitative researchers’ commitment to be open to emergent findings. Andrea describes the technical details of a coding process that allows qualitative researchers to think with theory without imposing prior ideas.

Analyzing Data in Collected in Another Language or Culture

The themes of careful and systematic analytic work continue in the ICQI 2021 presentation by Elsa M. Gonzalez and Yvonna S. Lincoln. The two discuss one of the most challenging methodological issues in the field of qualitative research. They describe how to analyze interview data shared in one language, in this case Spanish, for research written in another language, in this case, English. Elsa and Yvonna emphasize there is no formula to translate culture. Researchers must understand the languages of their inquiry and the contexts of their research.

Elsa interviewed leaders in Mexican institutions of higher education as part of the field work for her dissertation. Yvonna served as the supervisor for this work. As Yvonna discusses, managing the data from the dissertation required the two to develop a unique methodological approach, given Yvonna did not speak Spanish. Elsa developed two sets of transcripts from the Spanish language interviews, one set in Spanish and one in English. The Spanish language transcripts were shown to the interviewees to make sure their speech was accurately transcribed. The English language transcripts were presented to Elsa’s doctoral committee.

A major challenge was translating aspects of the Spanish language transcripts that did not move comfortably or easily into English—such as cultural statements, cultural context, and idioms. This task was complex because Mexican universities do not operate in the same manner as U.S. universities and, as a result, higher education leadership practices may vary between the two countries in ways many practitioners—the Mexican leaders Elsa interviewed—and many U.S. researchers—the members of Elsa’s doctoral committee—do not intuitively understand. Elsa and Yvonna attempted to manage the dilemmas of translating these cross-cultural, cross-language data by working with a colleague who had deep knowledge of Spanish, English, and the two countries’ higher education systems. This colleague worked with Elsa to translate the Spanish transcripts into English language texts that could more effectively communicate speakers’ intended meanings and explain the cultural situations and university leadership practices to which participants referred.

The importance of finding ways to translate research participants’ language and cultural understandings has grown as more qualitative studies are conducted across the globe. What native speakers mean when they discuss their work and other activities may be more important than their narrowly translated speech, especially if their talk refers to cultural and organizational practices without clear analogues in Western contexts. Sensitive and ethnical consideration of cross-language data, however, may create barriers to English language publication—a critical issue for researchers both inside and outside the U.S. The challenges of cross-language analysis may also make it difficult to share knowledge from the inquiry in participants’ native language.

Elsa concludes their talk for ICQI 2021 with a step-by-step description of the analytic process she used in her dissertation. She describes her efforts to translate, categorize, and code research participants’ meanings and share this content to audiences across cultures (see Gonzalez & Lincoln, 2022). Elsa describes the practical details of how she used notecards to code meanings shared by interviewees and her efforts to create memos from the analysis. Elsa’s dissertation was the first bilingual Spanish/English dissertation approved by Texas A & M university. She was also able to communicate her findings about leadership in higher education in Mexico to Spanish speakers and readers.

Analyzing qualitative data thus requires complex and intentional work. Researchers must organize sets of procedures for translating, coding, and writing up data to speak to audiences within their disciplines and to communities that may benefit from what was learned through the investigation. We hope the linked conference presentations from ICQI 2021will interest and inspire.