Okay, let’s imagine a story about a researcher trying to understand something new using the Eisenhardt Method, and specifically focusing on theoretical sampling.
Meet Alex, a researcher. Alex is really curious about why some new tech companies, called startups, grow incredibly fast and become hugely successful, while others that seem similar just… don’t. There isn’t a clear, simple answer out there about how this rapid, successful growth happens, so Alex knows this is a good puzzle for building a new theory.
Now, Alex needs to study some actual startups to figure this out. But there are thousands of startups! Alex can’t study all of them, and just picking random ones might not help answer the question clearly. This is where theoretical sampling comes in.
Alex remembers that theoretical sampling isn’t about random selection. It’s about carefully and deliberately choosing the examples (the cases) to study because they are the most likely to help build the theory.
Alex decides to use a strategy within theoretical sampling called “racing“. Alex finds several startups that all started around the same time, maybe even in the same city or with similar amounts of initial funding. Alex chooses these specific startups because the phenomenon of interest – rapid vs. slow growth – is expected to happen among them. By picking ones that start similarly, it’s like setting up a miniature “race”.
Alex then follows these “racing” startups over a few years. Some start growing incredibly fast, while others just chug along or even fail.
Now, Alex starts comparing the startups that grew fast to the ones that didn’t. Because Alex strategically chose these cases to have similar starting points, any big differences that emerge later are more likely related to what they did or how they operated during the “race,” rather than just differences in their initial funding or who started them.
This careful comparison of the similarities and differences between the “racing” startups, chosen specifically for this study, helps Alex start to build a theory. Alex can begin to see patterns – maybe the fast-growing ones made decisions differently, or changed direction faster, or focused on certain customers early on. The strategic selection of the cases is what allowed Alex to see these patterns clearly and start figuring out why one group succeeded in the “race” while the others didn’t.
So, in Alex’s story, theoretical sampling wasn’t just picking cases; it was a smart, planned way of choosing which startups to study so that the comparison between them would directly help uncover the underlying logic for why rapid growth happens. It’s picking cases with the goal of theory-building firmly in mind.
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