Choosing between qualitative, quantitative and mixed methods is not a matter of taste. It follows from what you want to find out, what kind of data you can realistically obtain, and who or what you can access — and when those three point in different directions, that is a signal your question needs adjusting.
Answer three questions and this tool suggests an approach, a design, a collection method and an analysis strategy. It shows its reasoning and its caveats every time, because there is no universally correct methodology for a topic and any tool that claims otherwise is misleading you.
Quantitative research measures and counts. It can establish how common something is, whether groups differ, and whether variables move together — and with the right design, whether one causes another. It cannot tell you what something means to the people involved.
Qualitative research works with accounts in people's own words. It can show how a phenomenon is understood and experienced, and surface factors nobody thought to put on a questionnaire. It cannot tell you how widespread anything is, and does not generalise statistically. Mixed methods combines both, at roughly double the workload — which is why one strand should lead.
Say your aim is to test whether one thing causes another, but your data will be interview accounts. Those are incompatible: accounts cannot establish causation. The honest resolutions are to find a measurable indicator, or to reframe the question as exploratory — not to count quotes and present the result as statistics.
The reverse conflict is just as common: an exploratory aim with numeric data. Numbers cannot tell you how something is experienced. This tool flags both cases rather than papering over them, because the conflict is real information about your design.
Under about thirty participants, most statistical tests cannot detect anything reliably — you may find nothing simply because the sample was too small, which tells you nothing about the world. For qualitative work, eight to fifteen interviews is a normal range, and the goal is variety rather than volume.
This is why access constrains design so heavily. If you can reach twelve people, a qualitative design is the honest choice; running a twelve-person survey and reporting percentages invites a straightforward methodological criticism.
The design is the logic of the study: cross-sectional, comparative, correlational, quasi-experimental, case study, evaluation. The method is how data arrives: questionnaire, interview, focus group, observation, secondary records. The analysis is what you do with it: descriptive statistics, tests of difference, regression, thematic analysis.
Students often name a method and call it a methodology. "I am doing a questionnaire" describes the method only. A methodology chapter needs all three, plus the reasoning that connects them to the research question — which is what this tool sets out for you.
Methodological conventions vary by discipline. A design that reads as rigorous in public health may look thin in anthropology, and the reverse. Departments have preferences, and examiners have expectations that are rarely written down anywhere.
Use this output as a structured starting point for a supervision conversation: it shows a defensible option and the reasoning behind it, which is a far better basis for discussion than an open question. Your supervisor knows what your examiners expect, and that knowledge is not something any tool can substitute for.
No. Several designs can be defensible for the same topic, and conventions differ by discipline. What matters is that your design fits your question and that you can justify it.
Only if you genuinely need both kinds of evidence and have time for both. Mixed methods roughly doubles the work, and a weak second strand hurts more than it helps.
For statistical tests, generally at least thirty and often many more. For interview-based work, eight to fifteen is common, selected purposively for variety rather than volume.
No. It can show how participants explain a process and suggest mechanisms worth testing, but a causal claim needs a design that compares conditions.
The method is the technique — questionnaire, interview, observation. The methodology is the whole logic: design, method, analysis, and the reasoning that ties them to your research question.