Written by Victor Yocco
Excerpt from smashingmagazine.com
Jen is presenting her research report to a client, who runs an e-commerce website. She conducted interviews with 12 potential users. Her goal was to understand the conditions under which users choose to shop online versus in store. The client asks Jen why they should trust her research when she has spoken to only 12 people. Jen explains her process to the client. She shares how she determined the sample size and collected and analyzed her data through the lens of data saturation. The client feels comfortable with the explanation. She asks Jen to continue the presentation.
Researchers must justify the sample size of their studies. Clients, colleagues and investors want to know they can trust a study’s recommendations. They base a lot of trust on sample population and size. Did you talk to the right people? Did you talk to the right number of people? Researchers must also know how to make the most of the data they collect. Your sample size won’t matter if you haven’t asked good questions and done thorough analysis.
Quantitative research methods (such as surveys) come with effective statistical techniques for determining a sample size. This is based on the population size you are studying and the level of confidence desired in the results. Many stakeholders are familiar with quantitative methods and terms such as “statistical significance.” These stakeholders tend to carry this understanding across all research projects and are, therefore, expecting to hear similar terms and hear of similar sample sizes across research projects.
Qualitative researchers need to set the context for stakeholders. Qualitative research methods (such as interviews) currently have no similar commonly accepted technique. Yet, there are steps you should take to ensure you have collected and analyzed the right amount of data.
In this article, I will propose a formula for determining qualitative sample sizes in user research. I’ll also discuss how to [read the full gist here]