How do researchers choose the subjects for their experiments? In this lesson, we'll look at what probability sampling is and three different probability sampling methods: cluster, multistage, and multiphase sampling.
Imagine that you are a psychologist and a large school district has hired you to design a study testing the health and wellness of 9th graders in their district because the school district officials want to know how fit their students are.
You know that you need to find out how healthy the 9th graders in the district are, but the problem is that there are so many 9th graders that you won't have the time or budget to measure the fitness of all of them. How should you proceed? How do you choose which 9th graders to test?
The sample of a study is the group of participants in the study, and it is chosen in a process known as sampling. One of the main types of sampling techniques is probability sampling, which involves choosing your sample randomly.
Let's look closer at three different types of probability sampling: cluster, multistage, and multiphase sampling.
Ok, so you want to measure the fitness of 9th graders in a large school district, but first you have to choose which 9th graders to test. One way that you might do this is through cluster sampling, or choosing a random sample of clusters to test.
Clusters are just natural divisions. For example, one natural division in a school district is at the school level: School A, School B, and so on. Each school becomes a cluster.
Let's say that you decide to choose four of the high schools in the district and use all of the 9th graders in those four schools as your sample. You'd choose the high schools randomly, by flipping a coin or drawing names out of a hat or using a computer program. Once you've chosen your clusters, or schools, you have your sample. Every 9th grader at those four schools will be in your study.
Cluster sampling works well wherever there are natural divisions, or clusters, in a population. Towns in a county, counties in a state, churches in a town - all of these are examples of naturally occurring clusters.
Cluster sampling is a simple and elegant way to choose a sample, but it might not always work well. For example, what if the four schools that you happen to choose are made up of kids from the richer side of town? Their parents can afford to buy them organic food and expose them to more extracurricular sports. As a result, they might be healthier than the other schools in the district.
Another way to draw your sample is through multistage sampling, which involves randomly choosing clusters and then randomly choosing subjects from each cluster. It is known as 'multistage' because there are multiple stages, or steps, to creating the sample. The first stage in multistage sampling is the same as cluster sampling. In our example, we would flip a coin to choose which schools we're working with. We can choose more schools than in cluster sampling, so maybe we will choose eight instead of four schools.
But then, instead of measuring every subject in every cluster we choose, we'd randomly choose a sample from each cluster. For example, remember that in cluster sampling, we used every 9th grade student at each of the schools that we chose. In multistage sampling, we will only take a portion of the 9th graders at each school. For example, maybe we randomly choose several 9th grade homeroom classes from each school.
The advantage of multistage sampling is that it allows us to choose more clusters. Instead of four schools, we drew students from eight schools. We wouldn't be measuring every student at each of those schools, so we are free to choose more schools. This, in turn, could lead to a more representative sample.
So far, we've looked at two ways that we can choose a sample - cluster and multistage sampling. Both of those assume that we are using the same measurement tool on all of our subjects. For example, maybe we are distributing a survey asking about fitness activities and nutrition.
But what if we wanted to also take measurements like blood pressure, resting heart rate, or other physical indicators of fitness? Those tests are expensive and more difficult to administer than a simple survey, so we might not be able to do them on a large sample.
One thing we could do is to combine the measurement tools (the survey and the physical tests) in a sampling technique known as multiphase sampling, which involves gathering data from a large sample and then additional data from a subset of that sample.
For example, we could give our survey to a large sample, perhaps every 9th grader at six of the area high schools. Then, we would administer the physical tests on only a small portion of those 9th graders, like two classes per school.
The first phase of the multiphase sampling technique is to randomly choose the large sample. As we did before, we can choose our six schools by flipping a coin or drawing names out of a hat.
The second phase of the multiphase sampling technique is to choose the small sample from the large sample. For example, we might choose the two classes per school based on a computer randomizer tool.
There can be as many phases in multiphase sampling as you want, but each phase nests within the previous ones. For example, we could choose a few students from each class to receive a more complete physical. We started with a large sample made up of schools, moved to a smaller sample made up of classes, and then an even smaller sample made up of students.
Probability sampling involves choosing an experiment's subjects in a random manner. Three examples of probability sampling include: cluster sampling, which involves randomly choosing clusters, or natural divisions; multistage sampling, which involves randomly choosing a sample from each cluster; and multiphase sampling, which involves gathering data from a large sample and then gathering additional data from a smaller sub-sample.
After you've reviewed this video lesson, you should be able to:
- Define probability sampling
- Discuss multistage, cluster and multiphase sampling