August 7

This course explores collaborative research as a purposeful methodological approach empowered by multiple vantage points and perspectives to unearth powerful facets and dimensions of data. Collaborative qualitative research engages multiple analysts to leverage their respective expertise, disciplinary backgrounds, and reading approaches to understand data deeply, then collectively assess what they are learning. Researchers contribute knowledge shaped by their own contexts to create productive dialogue that exposes assumptions, challenges premature interpretations, and generates new analytical directions.

This course addresses the methodological rationales and benefits of collaborative qualitative research, providing strategies for researchers at any stage - whether you're assembling a team, joining one, or reconsidering how your work might benefit from more collaborative approaches.

This course will build a foundation that will prepare participants to better:

1. Understand Data Content: Collaborative reading of qualitative data reveals the complete, authentic story of each data episode. When one team member jumps to interpretation, others might ask: "Does the data give you permission to make that claim?" This ongoing negotiation between what participants expressed and what researcher expertise discerns helps to preserve the authenticity of the data and builds evidence-based claims grounded in participant voices.

2. Move Through Data with Dialogue to Get to Discovery:

As researchers move from individual data collection episodes to understand content and processes that run through the entire dataset, collaboration can and should take many forms. Dialoguing involves conversational exchanges that do not occur only in team meetings. Colleagues think aloud together in emails, text exchanges, and hallway conversations. Researchers should record and reflect on much more than just the substance that emerges from those interactions. The content of the back and forth, including the logic and detail behind conclusions, is critical to discovery. When moving through data with dialogue to get to discovery, you are constructing the language of the study as a group.

To shape relevant and important publishable content, teams should prioritize the importance of “fit” in 3 important ways:

1) How do the ideas within each data collection episode fit with each other?

2) How do core ideas presented in individual data collection episodes compare across data collection episodes?

3) How do ideas, topics, and themes that research teams consider fit with existing literature and practices in the field?

3. Effectively Communicate Research Findings: Ongoing team dialogue fosters the ability for researchers to start writing early, concurrent with discovery and debate about the content and implications of what the research team is learning. In conversational exchanges throughout a project, researchers not only discuss what they discovered, but how they found it and how to write about it in ways that bring readers along for the journey. Using qualitative techniques, like memo writing, is one of many strategies that facilitate this early writing process.