All marketing practitioners who have taken up consumer research would have encountered the question – how big should the sample size be? For qualitative studies, the answer typically is: “As big a sample size as you can afford to have. The more the better!”. Auris takes the ‘sample size’ out of the equation and makes this question redundant. Imagine qualitative research with significantly large, unprompted views of the consumers, rather than just a handful of views. For those interested in research techniques, here’s some more detail you might find interesting.
When deciding the sample size for a qualitative market research project:
- Do we follow Patton (2002) by accepting that even 1 case is enough for research which does not have theory-building as core focus, and plans only to explore an issue or offer depth to quantitative data? After all, according to Patton (1990), a researcher needs to focus on what is doable or feasible; because sampling to the point of redundancy requires unlimited timelines as well as unlimited resources.
- Or, do we follow Saunders, Sim et al. (2012) who ask us to stop sampling only after we reach theoretical or conceptual saturation, if we aim to build a theory? If saturation makes further data collection unnecessary, we need to ensure that its operationalization is consistent with the research question, the theoretical position and the analytic framework adopted.
Let’s consider the issues involved.
- Qualitative research indicates that it does not rely on any quantitative necessities, like sample size. Typically, it requires smaller sample sizes than quantitative analysis would.
- Sample size in qualitative data analysis is measured by the number of themes or categories identified within the data.
- Saturation is the point where adding new data does not improve the explanations of the themes or the categories or add any new perspectives or information.
- Diminishing returns on qualitative data occur when more data leads to no new information, as one occurrence is enough to add the information to the analytical framework.
- Gathering too much data and too many samples could make a research project impractical and time consuming, as each new piece of data brings in additional complexities.
- It is important to capture all perceptions, but there is no need to do it on a repetitive basis, without adding any additional inputs to the research points being inquired into.
So, we accept that it is prudent for a qualitative researcher to take up enough cases to reach a saturation point where additional efforts will not be able to yield further useful information from the sample.
More about reaching saturation and why the sample size is important:
- The size of a sample and the design of the project determine the time taken for a project to reach saturation. To illustrate, a study covering Waldorf education in India up to the seventh standard would naturally take less time to reach saturation than a study of education in general.
- The amount of information obtained will also depend upon the interviewing techniques and skills of the interviewer.
- Some projects have large sample sizes because:
- The sample has heterogeneous population
- The project uses multiple selection criteria
- The study contains multiple samples
- The project needs an extensive ‘nesting’ of criteria
- The sample contains groups of special interest requiring intensive study.
- There’s no scientific explanation or method for determining that saturation has been reached at a specific point in the survey.
- Saturation is considered to be a matter of degree by some, with the potential for some new data to emerge.
Various factors decide the sample sizes in qualitative studies, and these suggestions range from 5 to 60, with 30, 40 and 50 being other significant reference points. Ultimately, the decision to call a halt to the study can be taken by the researchers, when they finally find that with each new interview they are unable to extract any significant new information to add to their data bank. That would prove to be the ideal sample size for their qualitative research study.
This is where an AI-powered social listening tool like Auris offers a market research team the unique advantage of being able to listen to an unlimited amount of feedback in real time. What’s more, these opinions, views or feedback are as random and unprompted as they can be, yielding highly dependable predictive analyses as the subjectivity which researchers tend to bring in is eliminated completely.