Stories, whether in an essay or an interview transcript, don’t exist in isolation. Meaning emerges through collaboration: between writer and editor, researcher and participant, individual voice and collective interpretation, or human and technology. And, with every layer of interpretation comes responsibility.

The real issue isn’t the tool—it’s the ethics guiding its use.

Narrative research, a key methodology in qualitative research, isn’t just about individual stories—it’s also about connecting through shared experience. Narrative research is rigorous because it requires careful attention to context, nuance, and meaning-making—it’s not just recording what is said, it is understanding why it matters and how it connects to the bigger picture. When we listen collectively, we start to notice patterns, shared values, and connections across diverse experiences.

Narrative research, a key methodology in qualitative research, isn’t just about individual stories—it’s also about connecting through shared experience. Narrative research is rigorous because it requires careful attention to context, nuance, and meaning-making—it’s not just recording what is said, it is understanding why it matters and how it connects to the bigger picture. When we listen collectively, we start to notice patterns, shared values, and connections across diverse experiences.

Narrative research, a key methodology in qualitative research, isn’t just about individual stories—it’s also about connecting through shared experience. Narrative research is rigorous because it requires careful attention to context, nuance, and meaning-making—it’s not just recording what is said, it is understanding why it matters and how it connects to the bigger picture. When we listen collectively, we start to notice patterns, shared values, and connections across diverse experiences.

When it comes to choosing qualitative data analysis software (QDAS) and other digital research tools, I like to think of the classic story of Goldilocks and the Three Bears. Stay with me here…

I have wrestled with the concept of the bell curve, a visualization of “normal distribution.” It wasn’t the math that troubled me—it was the underlying message that “normal” equated to the majority. Outliers—those who exist at the fringes of the distribution—are often controlled for or dismissed to make data cleaner and conclusions more generalizable. But what happens when those outliers represent real people whose experiences diverge from the mainstream? When we remove the outliers, we don’t make them cease to exist—we erase them from the conversation.

Since childhood, I have always been an observer and enjoyed asking complicated questions (sometimes, much to my parents' irritation). Fortunately, I was allowed to "not know" the answer and encouraged to search where that answer might exist in the world.