Qualitative Research
AI-assisted summary
Qualitative research is a small-sample methodology focused on understanding the "why" behind problems rather than proving hypotheses or achieving statistical significance. This approach complements quantitative methods by providing deep insights into user problems, behaviors, and motivations that inform decision-making when statistical generalization isn't possible or necessary.
To maintain rigor and trustworthiness, qualitative research must meet four key criteria established by Lincoln and Guba: credibility (truth of findings), transferability (applicability in other contexts), dependability (consistency), and confirmability (neutrality from researcher bias). These standards are achieved through systematic practices including specific research questions, careful participant selection, open-ended prompts, motivation-focused inquiry, continuous data analysis, and triangulation to validate unusual observations.
- small-n research
- it is not statistically significant
- we can't generalize from small sample sizes, it is just different than quantitative research
- it is a complement to qualitative research
- the goal isn't to disprove hypothesis, we're looking to understand the problem in detail
- helps to understand 'the why' behind the problem and its possible root causes, qualitative research would only allow us to speculate
- the goal is to gather insights to make informed decisions later
- by quantification in our analysis we're opening ourselves up to a quantitative judgement
- "three of six" kind of statements can be easily disregarded
- Credibility - confidence in the 'truth' of the findings
- Transferability - showing that the findings have applicablity in other contexts
- Dependability - showing that the findings are consistent and could be repeated
- Confirmability - a degree of neutraility or the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest.
- done by Yvonna Lincoln and Egon Guba
- we can satisfy those by being systematic
Systematic qualitative research
- formulates a specific research question before research is conducted
- samples carefully by recruiting participants to help to deeply understand the subject matter
- facilitates research sessions with open-ended prompts
- does not take only opinions but tries to understand 'the why' behind them
- analyzes the data continuously by coding and concluding from the data
- attempts to triangulate when facing something extraordinary or unusual to ensure that our conclusions are supported by data
- says what the problem is, quantitative says how bad it is
- helps to build an empathetic understanding of our users
Interview Best Practices (from The Mom Test)
- Be genuinely interested about people to open their hearts
- "Talk me through the last time [it] happened" is a great question for promoting show-don't-tell
- The big mistake is to mention your idea too soon rather than too late
- Watch out for bad data: compliments and fluff
- Banned words: would/could/might - these indicate hypothetical situations
- Fight fluff by asking about concrete past behavior
- Don't prematurely zoom in unless you're sure you're zooming into the right thing
- Plan the three most important things you want to learn from each person
- Look for people expressing deep emotion about problems - they're often your best prospects