How Do You Analyze Photos and Stories Together? Lessons from a Remote Photovoice Study of Filipino Single Mothers

Article information

Erasga, D. S., Cleofas, J. V., Andrada-Poa, M. R. J., & Jabal, R. F. (2024). Data analytic strategies used in a remote photovoice project of Filipino single mothers during the COVID-19 pandemic. In Á. M. Humble & M. E. Radina (Eds.), How Qualitative Data Analysis Happens (pp. 75–92). Routledge. https://doi.org/10.4324/9781003254041-6

What this chapter is about

This chapter is about how qualitative data analysis actually happens—not in the abstract, but in the messy, practical, real-world conditions of pandemic research.

The chapter describes a remote photovoice project about Filipino single mothers working from home during the COVID-19 pandemic. Photovoice is a qualitative method where participants take photographs to represent parts of their lived experience. These photographs are then discussed through interviews, allowing participants to explain what the images mean in their own words. 

The original study explored how single mothers experienced work-from-home arrangements during pandemic confinement. But as the participants submitted photographs and shared their stories, the analytic focus shifted. The mothers did not only talk about “working from home.” Their photographs and narratives showed something broader: single mothering while working from home during the pandemic. 

That shift is important. It shows one of the strengths of qualitative research: the researcher may begin with one focus, but the participants can redirect the study toward what matters most in their lives.

Why this chapter matters

Many research articles present polished findings: themes, quotes, tables, and conclusions. But they often do not show the backstage work of analysis. How did the team move from raw photos and interview transcripts to themes? How were different types of data brought together? How did researchers collaborate when they could not meet face-to-face?

This chapter answers those questions.

It is especially useful for students, qualitative researchers, and supervisors because it shows how a team can do rigorous analysis even under difficult conditions: no face-to-face meetings, no expensive qualitative software, limited time, and pandemic restrictions. The project was also rapid, frugal, coursework-based, and fully online. 

In other words, this is a practical methods chapter. It shows that good qualitative analysis does not always require expensive tools. It requires careful organization, reflexivity, team communication, analytic discipline, and transparency.

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What the researchers did

The project used remote photovoice. Because of COVID-19 restrictions, traditional in-person photovoice procedures had to be modified. Participants were asked to take photographs using their smartphones and send one photo per day for three days. Each photo responded to the prompt: What was your highlight for the day as a single mother working from home? They also sent short text descriptions with the photographs. 

After the photography phase, the researchers conducted online one-on-one interviews through videoconferencing. The interviews were guided by the SHOWeD method, a common photovoice questioning framework that asks participants what they see, what is happening, how the image relates to their lives, why the situation exists, and what can be done about it. 

The project generated four types of data:

1. background information about the 15 participating single mothers;
2. digital photographs;
3. written photo descriptions; and
4. interview transcripts. 

The researchers managed these data using Google Drive folders, Word files, Google Sheets, Zoom recordings, Messenger conversations, and shared memos. The screenshot of the shared Google Drive folder on page 8 shows how the team organized files and analysis materials in a cloud-based workspace. 

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The main analytic strategy: “following a thread”

The key methodological contribution of the chapter is its explanation of following a thread, a strategy for integrating different qualitative data sources.

The problem was this: the team had photographs, photo captions, and interview narratives. Each type of data offered something different. The photographs showed visual scenes. The captions gave short descriptions. The interviews gave deeper explanations. If the researchers analyzed only the interviews, the photos might become decorative. If they analyzed only the photos, the mothers’ explanations might be weakened.

So the team used “following a thread” to connect related pieces across the different data sources. 

The chapter explains four steps.

Step 1: Analyze each data set first

The team first analyzed each data set using techniques appropriate to it. For interviews and photo descriptions, they used inductive reflective thematic analysis. They read and reread transcripts, listened to recordings, reviewed photographs, wrote memos, and generated codes. 

For photographs, they asked visual analytic questions such as: What does the participant want to tell? What ideas and values are expressed? What is the significance of the photo to the participant?

The sample coding table on page 13 shows how interview extracts and photo descriptions were coded under the theme “more intimate moments with children.” The examples include bonding time, storytelling, watching TV together, celebrating birthdays, and tending to a child’s health needs while working. 

Step 2: Identify promising threads

After initial coding, the team looked for promising threads. These were themes or patterns rich enough to help tell the story of single mothering during pandemic work-from-home arrangements.

Examples of promising threads included:

• more intimate moments with children;
• ability to do more chores;
• sharing parenthood with relatives;
• taking care of other relatives;
• maintaining work-life balance;
• engagement in children’s schooling; and
• coping with distance learning. 

A thread was considered promising if it had rich data and helped explain the main phenomenon. For example, “more intimate moments with children” was promising because many mothers described increased bonding and caregiving during confinement, and many photographs visually showed mothers and children together.

Step 3: Create a data repertoire

The third step was to create a data repertoire for each thread. This meant gathering all related data into one place: interview excerpts, photo descriptions, photographs, codes, and notes.

For example, all materials related to “more intimate moments with children” were placed together. This allowed the researchers to see the pattern across different data types. It also helped them notice whether a theme was strong or weak. If a thread had many interview excerpts but no photographs, the team reconsidered whether it should remain in the analysis. 

The photograph examples on page 15 show how images were coded. One image was coded as mother and child playing and watching together; another as embracing the child and accompanying the child in bed. These visuals helped strengthen the theme because they did not merely illustrate the interview data—they contributed evidence of their own. 

Step 4: Synthesize the followed threads

Finally, the team connected related threads into higher-level themes. For instance, the thread “more intimate moments with children” was combined with “increased engagement with housework” to form the superordinate theme “increased presence at home.”

The flowchart on page 21 visually summarizes the four-step process: initial data analysis, identifying promising threads, creating a data repertoire, and synthesizing followed threads into a superordinate theme and core category. The core category was “performing overlapping roles of motherhood in confinement during a pandemic.”

This is the heart of the chapter: qualitative analysis is not simply about “themes emerging.” It is active, iterative, collaborative, and interpretive.

How the team ensured trustworthiness

The chapter also explains how the team maintained rigor. They used multiple data sources, repeated engagement with participants, member checking, reflexive memos, peer review, digital audit trails, and careful documentation of analytic decisions. 

They also worked as a remote team using Zoom, Messenger, and cloud-based office tools. The screenshot on page 26shows how analytic discussions happened through instant messaging. These conversations helped the team refine subthemes, challenge interpretations, and make decisions when they could not meet in person. 

Bottom line

This chapter shows that qualitative analysis is a craft. It involves organizing messy data, listening to participants, comparing sources, negotiating meanings as a team, and documenting how interpretations are built. It also shows that remote qualitative research can be rigorous when researchers are transparent, reflexive, systematic, and creative. 

For researchers working with photographs and narratives, the lesson is clear: do not let images become mere illustrations. Let them speak with the narratives. Follow the threads across data sources.

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Policy/practice/methods recommendations

1. Treat photographs as data, not decoration
   In photovoice, images should not only be used to “support” quotes. They should be analyzed as meaningful participant-generated data.
2. Use “following a thread” when working with multiple qualitative data types
   This strategy helps researchers integrate photos, captions, interview narratives, and memos without privileging only one type of data.
3. Build a data repertoire for each theme
   Putting all related photographs, excerpts, descriptions, and codes in one folder or spreadsheet can make analysis more transparent and manageable.
4. Use low-cost digital tools carefully
   Google Drive, Google Sheets, Docs, Zoom, and Messenger can support rigorous qualitative work when paired with clear file naming, version control, audit trails, and data protection.
5. Document analytic decisions as they happen
   Memos, meeting notes, comments, screenshots, and coding logs can help establish dependability and confirmability.
6. Let participants redirect the analytic focus
   The study began with work-from-home experiences, but participants’ photos and stories pushed the analysis toward single mothering. This responsiveness is a strength of qualitative inquiry.
7. Support remote qualitative teams with both synchronous and asynchronous communication
   Videoconferencing helps with discussion and consensus. Shared documents and instant messaging help with ongoing feedback and analytic continuity.

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Glossary of key terms

• Photovoice — A qualitative method where participants take photographs to represent their lived experiences and discuss the meanings of those images.
• Remote photovoice — A modified photovoice approach where photography, submission of images, interviews, and analysis happen online or at a distance.
• Visual data — Images used as research data, such as participant-generated photographs.
• Narrative data — Stories, explanations, interview excerpts, or written descriptions that communicate participants’ meanings and experiences.
• Photo description — A short written explanation submitted by the participant alongside a photograph.
• SHOWeD method — A questioning framework for photovoice that asks what participants see, what is happening, how it relates to their lives, why the situation exists, and what can be done.
• Thematic analysis — A qualitative method for identifying, organizing, and interpreting patterns of meaning in data.
• Reflective thematic analysis — A flexible, interpretive approach to thematic analysis that recognizes the researcher’s active role in generating themes.
• Code — A short label that captures an important idea, action, or meaning in a piece of data.
• Theme — A broader pattern of meaning built from related codes and data segments.
• Promising thread — A theme or analytic lead that is rich enough to be followed across multiple data sources.
• Following a thread — A qualitative integration strategy where researchers trace a promising finding across different data sets to build a deeper interpretation.
• Data repertoire — A curated collection of related data segments, such as photographs, quotes, descriptions, and codes, organized under a thread.
• Superordinate theme — A higher-level theme that brings together related lower-level themes or threads.
• Core category — The main analytic idea that condenses the central insight of a qualitative study.
• Digital audit trail — A record of analytic decisions, files, memos, codes, comments, and revisions stored digitally.
• Trustworthiness — The quality and rigor of qualitative research, often discussed through credibility, transferability, dependability, and confirmability.
• Credibility — The extent to which findings are believable and grounded in participants’ experiences.
• Transferability — The extent to which readers can judge whether findings may apply to other contexts.
• Dependability — The consistency and transparency of the research process.
• Confirmability — The extent to which findings are shaped by the data rather than only researcher bias.
• Reflexivity memo — A written reflection where researchers examine their assumptions, positions, and possible influence on the study.
• Member checking — Asking participants to review or validate findings, interpretations, or summaries.
• Peer review in qualitative research — Having another researcher examine and critique the analytic process and outputs to improve rigor.