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Sampling
Brooke is a psychologist who is interested in studying how much stress college students face during finals. She works at a university, so she is planning to send out a survey around finals time and ask some students to rank on a scale of 1 to 5 how stressed out they are.
But which students should she survey? All of the students at the university? Only the students in the psychology department? Only freshmen? There are a lot of possibilities for Brooke's sample. The sample of a study is simply the participants in a study. In Brooke's case, her sample will be the students who fill out her survey.
Sampling is the process whereby a researcher chooses her sample. This might seem pretty straightforward: just get some people together, right? But how does Brooke do that? Should she just stand on a corner and start asking people to take her survey? Should she send out an email to every college student in the world? Where does she even begin?
Because sampling isn't as straightforward as it initially seems, there is a set process to help researchers choose a good sample. Let's look closer at the process and importance of sampling.
Process
So Brooke wants to choose a group of college students to take part in her study. To select her sample, she goes through the basic steps of sampling.
1. Identify the population of interest. A population is the group of people that you want to make assumptions about. For example, Brooke wants to know how much stress college students experience during finals. Her population is every college student in the world because that's who she's interested in. Of course, there's no way that Brooke can feasibly study every college student in the world, so she moves on to the next step.
2. Specify a sampling frame. A sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. Notice that a sampling frame is not as large as the population, but it's still a pretty big group of people. Brooke still won't be able to study every single student at her university, but that's a good place from which to draw her sample.
3. Specify a sampling method. There are basically two ways to choose a sample from a sampling frame: randomly or non-randomly. There are benefits to both. Basically, if your sampling frame is approximately the same demographic makeup as your population, you probably want to randomly select your sample, perhaps by flipping a coin or drawing names out of a hat.
But what if your sampling frame does not really represent your population? For example, what if the school where Brooke works has a lot more men than women and a lot more whites than minority races? In the population of every college student in the world, there might be more of a balance, but Brooke's sampling frame (her school) doesn't really represent that well. In that case, she might want to non-randomly select her sample in order to get a demographic makeup that is closer to that of her population.
4. Determine the sample size. In general, larger samples are better, but they also require more time and effort to manage. If Brooke ends up having to go through 1,000 surveys, it will take her more time than if she only has to go through 10 surveys. But the results of her study will be stronger with 1,000 surveys, so she (like all researchers) has to make choices and find a balance between what will give her good data and what is practical.
5. Implement the plan. Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample.
Importance
As you can see, choosing a sample is a complicated process. You might be wondering why it has to be that complicated. Why bother going through all those steps? Why not just go to a class and pull some students out and have them fill out the survey? Why is sampling so important to research?