Systematic Sampling vs. Cluster Sampling: An Overview
Systematic sampling and cluster sampling are two different types of statistical measures used by researchers, analysts, and marketers to study samples of a population. The way in which both systematic and cluster sampling pull sample points from the population is different. While systematic sampling uses fixed intervals from the larger population to create the sample, cluster sampling breaks the population down into different clusters. Systematic sampling selects a random starting point from the population, and then a sample is taken from regular fixed intervals of the population depending on its size. Cluster sampling divides the population into clusters and then takes a simple random sample from each cluster.
- Systematic sampling and cluster sampling are both statistical measures used by researchers, analysts, and marketers to study samples of a population.
- Systematic sampling involves selecting fixed intervals from the larger population to create the sample.
- Cluster sampling divides the population into groups, then takes a random sample from each cluster.
Systematic sampling is a random probability sampling method. It's one of the most popular and common methods used by researchers and analysts. This method involves selecting samples from a larger group. While the starting point may be random, the sampling involves the use of fixed intervals between each member.
Here's how it works. The researcher begins by first choosing a starting point from a larger population. This is normally in the form of an integer which must be smaller than the number of subjects in the greater population. The analyst then chooses the interval between each member—that being a consistent difference that lies between each member. Here's a hypothetical example. Let's say there's a population of 100 people in the study. The researcher starts off with the person in the 10th spot. They then decide to choose every seventh person thereafter. This means the people in the following spots are chosen in the sampling: 10, 17, 24, 31, 38, 45, and so on.
This type of statistical sampling is fairly simple, which is why it's generally favored by researchers. It is also very useful for certain purposes in finance. Those who use this method make the assumption that the results represent the majority of normal populations. This process also guarantees the entire population is evenly sampled. But there may be problems with this kind of sampling, though. For instance, the risk of manipulating data may be greater as those using this method may choose subjects and intervals based on a desired outcome.
Systematic and cluster sampling are both considered forms of random sampling.
Cluster sampling is another type of random statistical measure. This method is used when there are different subsets of groups present in a larger population. These groups are known as clusters. Cluster sampling is commonly used by marketing groups and professionals.
Cluster sampling is a two-step procedure. First, the entire population is selected and separated into different clusters. Random samples are then chosen from these subgroups. For example, a researcher may find it difficult to construct the entire population of customers of a grocery store to interview. However, they may be able to create a random subset of stores—this represents the first step in the process. The second step is to interview a random sample of the customers of those stores.
This sampling method may be used when completing a list of the entire population is difficult as demonstrated in the example above. But for it to work and be as accurate as possible, it's very important that the members of each subset or cluster be as similar as possible. The number of members in each group should also be fairly consistent, otherwise, the results may be skewed.
This is a simple, manual process that can save time and money. In fact, using cluster sampling can be fairly cheap when compared to other methods. That's because there are generally fewer associated costs and expenses—especially since the members in the population are easily accessible—and it also takes a larger population into account. But there is a larger sampling error associated with cluster sampling, making it less precise than other methods of sampling.