Exploring the parameters of “it depends” for estimating the rate of data saturation in qualitative inquiry

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In an earlier blog post on sample sizes for qualitative inquiry, we discussed the concept of data saturation – the point at which no new information or themes are observed in the data – and how researchers and evaluators often use it as a guideline when designing a study.

In the same post, we provided empirical data from several methodological studies as a starting point for sample size recommendations. We simultaneously qualified our recommendations with the important observation that each research and evaluation context is unique, and that the speed at which data saturation is reached depends on a number of factors. In this post, we explore a little further this “it depends” qualification by outlining five research parameters that most commonly affect how quickly/slowly data saturation is achieved in qualitative inquiry.

A pathway for sampling success

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The credibility and usefulness of our research and evaluation findings are inextricably connected to how we select participants. And, let’s admit it, for many of us, the process of choosing a sampling strategy can be drier than a vermouth-free martini, without any of the fun. So, rather than droning on comparing this or that sampling strategy, we present a relatively simple sampling decision tree.

Emojis convey language, why not a sampling lesson too?

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To help folks build stronger sampling plans for their research and evaluation projects, we present a series of three sampling posts. This first blog post explains sampling terminology and describes the most common sampling approaches using emoji-themed graphics. Ready to get started? Sit down, hold on to your hats and glasses, and enjoy the sampling ride!