What's the Difference Between Systematic Sampling and Cluster Sampling?

Systematic Sampling vs. Cluster Sampling: An Overview

Systematic and cluster sampling are two types of statistical measures used by researchers, analysts, and marketers to study population samples.

The way in which both systematic and cluster sampling pull sample points from the population is different. While systematic sampling uses fixed intervals from a larger population to create the sample, cluster sampling breaks the population 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 takes a simple random sample from each cluster. Learn more about the differences between these types of samplings, their advantages and disadvantages, when it is best to use one over the other, and see some examples.

Key Takeaways

  • Systematic and cluster sampling are statistical measures used by researchers, analysts, and marketers to study population samples.
  • 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 and cluster sampling are forms of random sampling, known as probability sampling, which stands in contrast to non-probability sampling.
  • Systematic and cluster sampling have advantages and disadvantages, but both can be time- and cost-efficient.

Systematic Sampling

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 using 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 usually in the form of an integer which must be smaller than the number of subjects in the greater population. The analyst then chooses a consistent interval between each member.

Here's an example. Let's say there's a population of 100 people in a study. The researcher starts with the person in the 10th spot. They then decide to choose every seventh person after that. This means the people in the following data points are selected in the sampling: 10, 17, 24, 31, 38, 45, and so on.


Systematic Sampling

Types of Systematic Sampling

Within the systematic sampling method are three types of sampling:

  • Systematic random sampling: This method is the one described earlier, where set intervals are used to choose samples.
  • Linear systematic sampling: In this method, the statistician chooses a random starting sample and uses "skip logic" to choose each following sample, such as k=N/n, where k is the interval, N is the total population, and n is the size of the sample. So, if the population was 500 and the sample size was 3, the interval would be 500/3. There would be 167 samples taken at intervals of three samples.
  • Circular systematic sampling: The sample starts at one point and begins again from the same starting point with a set interval. So, if the total population (N) was {a, b, c, d, e, f} and the sample size was 2, the sample interval (k) would be determined using the sample interval formula N/n (or 6/2=3). Starting at {a}, you'd count three data points and combine the two. So, the first sample would be {ad}, the second {be}, then {cf}, {da}, {eb}, and {fc}.

Advantages and Disadvantages of Systematic Sampling

This type of statistical sampling is pretty simple, so researchers generally favor it over other methods. It is also very useful for certain purposes in finance. Those who use this method assume that the results represent the majority of normal populations.

  • Simple to conduct and easy to understand

  • Advantageous in regards to creating, comparing, and understanding samples

  • Provides an increased degree of control when compared to other sampling methodologies

  • Does away with clustered selection, where randomly selected samples in a population are unnaturally close together

  • Carries a low-risk factor because there is a low chance that the data can be contaminated.

  • Guarantees the entire population is evenly sampled

  • The size of the population is needed. Without the specific number of participants in a population, systematic sampling does not work well

  • The population needs to have a natural amount of randomness

  • The risk of choosing similar instances is increased without randomness, defeating the purpose of the sample

  • The risk of manipulating data may be greater as those using this method may choose subjects and intervals based on a desired outcome

Example of Systematic Sampling

The goal of systematic sampling is to obtain an unbiased sample. The method to achieve this is by assigning a number to every participant in the population and then selecting the same designated interval to create the sample.

For example, you could choose every fifth or twentieth participant, but you must choose the same interval for every population. The process of selecting this nth number is what makes it systematic sampling.

For example, imagine a toothpaste company creates a new flavor of toothpaste and would like to test its reception before selling it to the public. The company gathers a group of 50 volunteers and uses systematic sampling to create a sample of 10 whose opinions 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 chosen a sample size of 10.

The sample size becomes 5, or 50 / 10, 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 chosen for the sample.

Systematic Sampling With Sampling Interval
1 2 3 4 5
6 7 8 9 10
11 12  13 14 15
16 17 18 19 20
21 22 23 24 25
26 27 28 29 30
31 32 33 34 35
36 37 38 39 40
41 42 43 44 45
46 47 48 49 50

Cluster Sampling

Cluster sampling is another type of random statistical measure. This method is used when different subsets of groups are present in a larger population. These groups are known as clusters and are 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 challenging to interview the entire population of a grocery store's customers. However, they may be able to create a subset of stores in clusters; this represents the first step in the process. The second step would be to interview random customers of those stores. Third, data would be collected from the interviews and samples selected.

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 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 also be seen as a subset of one-stage sampling because certain elements from the created clusters are sampled.

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. Like systematic sampling, cluster sampling has advantages and disadvantages.

  • Simple, manual process that can save time and money

  • Allows for increasing the sample size

  • Requires choosing selected clusters at random rather than evaluating entire populations

  • Larger sampling error makes it less precise than other methods of sampling

  • Subjects within a cluster tend to have similar characteristics, meaning that cluster sampling does not include varied demographics of the population

  • Often results in an overrepresentation or underrepresentation within a cluster, resulting in bias

Cluster sampling is relatively cheap compared to other methods because there are generally fewer associated costs and expenses. Additionally, the statistician only chooses from a select group of clusters, so they can increase the number of subjects to sample from within that cluster.

Example of Cluster Sampling

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 employee about their educational background. To achieve that goal, a statistician can employ cluster sampling.

The first step would be to form a cluster of investment banks. Then, 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, another cluster can be formed, including employees from only specific departments such as sales, 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.

Key Differences

Though systematic and cluster sampling are forms of random sampling, they arrive at their sample size differently. Systematic sampling chooses a sample based on fixed intervals in a population, whereas cluster sampling creates clusters from a population.

Cluster sampling is better used when there are different subsets within a specific population. In contrast, systematic sampling is better used when the entire list or a 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.

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 made from the initial clusters 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 with 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

Various sampling methods are available to statisticians who seek to study information within groups. Because groups or populations tend to be large, obtaining data from every subject is tough. 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 ensuring they are selected randomly and accurately represent 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.

Article Sources
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  1. University of Connecticut. "Systematic Sampling."

  2. PennState. "STAT 506 Sampling Theory and Methods | 9.1 - Multi-Stage Sampling: Two Stages with S.R.S at Each Stage."

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