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. In this article, we will cover the differences of both these types of samplings, their advantages and disadvantages, when it is best to use one over the other, and examples of each.
- 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.
- Both systematic sampling and cluster sampling are forms of random sampling, known as probability sampling, which stands in contrast to non-probability sampling.
- Systematic sampling and cluster sampling both have their advantages and disadvantages, but both can be time- and cost-efficient.
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.
Advantages and Disadvantages of Systematic Sampling
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 sampling is simple to conduct and easy to understand. Statisticians, who might have budget or time constraints, find the use of systematic sampling to be advantageous in regards to creating, comparing, and understanding their samples. In addition, systematic sampling provides an increased degree of control when compared to other sampling methodologies because of its process.
Systematic sampling also does away with clustered selection, where randomly selected samples in a population are unnaturally close together. Random samples, as opposed to systematic ones, are only able to remove this occurrence by conducting multiple surveys or increasing the number of samples; both of which can be time-consuming and costly. Systematic sampling also carries a low-risk factor because there is a low chance that the data can be contaminated.
Despite its many advantages, systematic sampling does come with disadvantages. The primary limitation of systematic sampling is that the size of the population is needed. Without the specific number of participants in a population, systematic sampling does not work well. For example, if a statistician would like to examine the age of homeless people in a specific region but cannot accurately obtain how many homeless people there are, then they won't have a population size or a starting point.
Another disadvantage is that the population needs to have a natural amount of randomness to it. If it does not, the risk of choosing similar instances is increased, defeating the purpose of the sample.
Example of Systematic Sampling
The goal of systematic sampling is to obtain an unbiased sample. The method in which to achieve this is by assigning a number to every participant in the population and then selecting the same designated interval in the population to create the sample.
For example, you could choose every 5th participant or every 20th participant but you must choose the same one in every population. The process of selecting this nth number is systematic sampling.
For example, a toothpaste company creates a new flavor of toothpaste and would like to test it on a sample population before selling it to the public. The test is to determine whether the new flavor is well received or not by the sample. The company puts together a population of 50 people and decides to use systematic sampling to create a sample of 10 people whose opinion regarding the toothpaste they will consider.
First, the marketing team assigns a number to every participant in the population. In this case, it has a population of 50 in the group, so it will assign every participant a number ranging from one to 50. Next, it must determine how large of a sample it wishes to have and it has determined a sample size of 10. Therefore, 50 / 10 = 5. Five will be its sampling digit; meaning it will select every fifth participant in the population to arrive at its sample. This is outlined in the table below where every fifth participant is in bold and the one chosen for the sample.
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.
When attempting to study the demographics of a city, town, or district, it is best to use cluster sampling, due to the large population sizes.
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.
Types of Cluster Sampling
There are two types of cluster sampling: one-stage cluster sampling and two-stage cluster sampling.
One-stage cluster sampling involves choosing a random sample of clusters and gathering data from every single subject within that cluster. Two-stage cluster sampling involves randomly selecting multiple clusters and choosing certain subjects randomly within each cluster to form the final sample. Two-stage sampling can be seen as a subset of one-stage sampling: sampling certain elements from the created clusters.
Advantages and Disadvantages of Cluster Sampling
This sampling method may be used when completing a list of the entire population is difficult as demonstrated in the example above. 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 because cluster sampling requires choosing selected clusters at random rather than evaluating entire populations. This same process also allows for increasing the sample size. As a statistician is only choosing from a select group of clusters, they can increase the number of subjects to sample from within that cluster.
The primary disadvantage of cluster sampling is that there is a larger sampling error associated with it, making it less precise than other methods of sampling. This is because subjects within a cluster tend to have similar characteristics, meaning that cluster sampling does not include varied demographics of the population. This often results in an overrepresentation or underrepresentation within a cluster, and, therefore, can be a biased sample.
Example of Cluster Sampling
For example, say an academic study is being conducted to determine how many employees at investment banks hold MBAs, and of those MBAs, how many are from Ivy League schools. It would be difficult for the statistician to go to every investment bank and ask every single employee their educational background. To achieve the goal, a statistician can employ cluster sampling.
The first step would be to form a cluster of investment banks. Rather than study every investment bank, the statistician can choose to study the top three largest investment banks based on revenue, forming the first cluster. From there, rather than interviewing every employee in all three investment banks, a statistician could form another cluster, which would include employees from only certain departments, for example, sales and trading or mergers and acquisitions.
This method allows the statistician to narrow down the sampling size, making it more efficient and cost-effective, yet still having a varied enough sample to gauge the information being sought.
Though both systematic sampling and cluster sampling are forms of random sampling, they arrive at their sample size in completely different ways. Systematic sampling chooses a sample based on fixed intervals in a population whereas cluster sampling creates a cluster from a population.
Cluster sampling is better suited for when there are different subsets within a specific population, whereas systematic sampling is better used when the entire list or number of a population is known. Both, however, are splitting the population into smaller units to sample.
For systematic sampling it is important to ensure there are no patterns in the group, otherwise, you risk choosing similar subjects without representing the overall population. For cluster sampling, it is important to ensure that each cluster has similar traits to the whole sample.
|Systematic Sampling||Cluster Sampling|
|Chooses a sample by selecting subjects at intervals||Chooses a sample by creating clusters|
|The list or number of the entire population must be known||The entire population is not needed in creating clusters|
|Patterns in the population must be avoided for accuracy||Clusters should have similar characteristics to the entire sample|
Cluster Sampling FAQs
What Is Meant by Cluster Sampling?
Cluster sampling is a form of random sampling that separates a population into clusters to create a sample. Further clusters can be created from the initial clusters as well to narrow down a sample.
Why Would You Use Cluster Sampling?
Cluster sampling is best used to study large, spread out populations, where aiming to interview each subject would be costly, time-consuming, and perhaps impossible. Cluster sampling allows for creating clusters that are a smaller representation of the population being assessed, with similar characteristics.
How Does Cluster Sampling Work?
Cluster sampling simply involves dividing the population being studied into smaller groups. These subgroups can be studied or further randomly divided into other subgroups.
What Is the Difference Between Cluster Sampling and Stratified Sampling?
The primary difference between cluster sampling and stratified sampling is that the clusters created in cluster sampling are heterogeneous whereas the groups for stratified sampling are homogeneous.
The Bottom Line
There are a variety of sampling methods available to statisticians who seek to study information within groups. Because groups or populations tend to be large, it is very difficult to obtain data from every single subject. To overcome this problem, statisticians use sampling, creating smaller groups that are meant to be representative of the larger population.
An important aspect of creating these smaller samples is to ensure they are selected at random and are a true representation of the larger population. Systematic sampling and cluster sampling are two methods that statisticians can use to study populations.
Both are forms of random sampling that can be time- and cost-efficient, separating populations into smaller groups for easier analysis. Systematic sampling works best when the entire population is known while cluster sampling works best when the entire population is difficult to gauge.