The qualitative versus quantitative decision is not about which approach is more rigorous. Both are rigorous when done well. It is about which approach actually answers the question you are asking.
What quantitative research measures
Quantitative research works with numerical data and is built to measure, compare, and test relationships between variables. It typically involves larger sample sizes, structured instruments such as surveys or experiments, and statistical analysis to determine whether observed patterns are meaningful or simply due to chance. It answers questions that start with how much, how many, or whether a specific relationship exists.
What qualitative research explores
Qualitative research works with non numerical data such as interviews, focus groups, open ended responses, or observations, and is built to explore meaning, experience, and context in depth. It typically involves smaller, purposefully selected samples and analysis methods such as thematic coding. It answers questions that start with how or why, particularly when the answer depends heavily on individual perspective or circumstance.
When quantitative is the stronger choice
Choose quantitative research when your question involves measuring the size or strength of a relationship, comparing groups, testing a specific hypothesis, or when you need results that can be generalized to a wider population with statistical confidence. It also tends to suit topics where established measurement tools already exist, since building your own reliable, validated instrument from scratch is a significant undertaking on its own.
When qualitative is the stronger choice
Choose qualitative research when your question is genuinely exploratory, when you are trying to understand lived experience or the reasoning behind a behavior, or when the topic is too new or too context dependent for existing quantitative instruments to capture well. Qualitative research is also often the better fit when your sample naturally has to be small, such as studying a specific organization, community, or set of unusual cases.
Why some researchers choose both (mixed methods)
A mixed methods design uses quantitative data to establish patterns at scale and qualitative data to explain why those patterns exist, or the reverse, using qualitative work to generate hypotheses that are then tested quantitatively. This can produce a genuinely richer study, but it demands considerably more time and skill, so it is worth choosing only when your question truly needs both lenses rather than as a way to avoid making a decision.
If you go with mixed methods, decide early whether your qualitative and quantitative strands will run sequentially or in parallel. That single decision shapes your entire data collection timeline.
Questions to ask before you decide
Ask what your research question is actually asking for, a measurement or an explanation. Ask what data you can realistically access within your timeline. Ask whether your field has established norms and expectations around one approach over the other, since some disciplines strongly favor one method. And ask honestly which skill set you already have, or are willing to build quickly, since both approaches require real technical competence to execute well.
There is no universally superior method, only the method that fits your specific question. Get that match right, and the rest of your methodology chapter becomes far easier to write.