When is cluster sampling appropriate

Share on facebook. Share on twitter. Share on linkedin. Table of Contents. What is sampling? Sampling Methods Guide. What is cluster sampling? Reduce Sampling Errors with Voxco. Types of Cluster Sampling. We will now go over the three main categories under cluster sampling: One-Stage Sampling One-stage sampling, also known as single-stage cluster sampling, is a method where every element within the selected clusters will become a part of the sample group.

This is oftentimes not feasible if the target population is vast, and the clusters are too large to include fully. For example , if you were to conduct a study on the consumption of soda in a particular city, you could use area sampling to divide the city into different areas, called clusters, and then select certain clusters to be a part of the sample group.

Use One Stage Sampling Effectively. Two-Stage Sampling Two-stage sampling is a more feasible and realistic method of sampling in cases where the population is too large or is scattered over a large geographical area. In this method, simple random sampling sometimes other sampling methods like systematic sampling are also used is used to select elements from the selected clusters , further narrowing down to the desired sample size.

With two-stage sampling, you can use simple random sampling to select elements from each one of the selected clusters. The units of the narrowed down sample group will be the selected respondents for the study on soda consumption. Multistage Sampling Multistage sampling takes two-stage sampling further by adding a step, or a few more steps, to the process of obtaining the desired sample group.

This means that the researchers use multiple steps to obtain the desired sample , and at each stage they are left with a smaller and smaller sample group.

Cluster sampling also known as one-stage cluster sampling is a technique in which clusters of participants that represent the population are identified and included in the sample [1].

Cluster sampling involves identification of cluster of participants representing the population and their inclusion in the sample group. This is a popular method in conducting marketing researches. The main aim of cluster sampling can be specified as cost reduction and increasing the levels of efficiency of sampling. Multiple-stage cluster sampling takes a step or a few steps further than two-stage sampling. For conducting effective research across multiple geographies, one needs to form complicated clusters that can be achieved only using the multiple-stage sampling technique.

An example of Multiple stage sampling by clusters — An organization intends to survey to analyze the performance of smartphones across Germany. This sampling technique is used in an area or geographical cluster sampling for market research. A broad geographic area can be expensive to survey in comparison to surveys that are sent to clusters that are divided based on region. The sample numbers have to be increased to achieve accurate results, but the cost savings involved make this process of rising clusters attainable.

It is the most economical and practical solution for statisticians doing research. Take the example of a researcher who is looking to understand the smartphone usage in Germany. In this case, the cities of Germany will form clusters. This sampling method is also used in situations like wars and natural calamities to draw inferences of a population, where collecting data from every individual residing in the population is impossible.

There are multiple advantages to using cluster sampling. Here they are:. In comparison to simple random sampling, tis technique can be useful in deciding the characteristics of a group such as population, and researchers can implement it without having a sampling frame for all the elements for the entire population.

Since cluster sampling and stratified sampling are pretty similar, there could be issues with understanding their finer nuances. Hence, the major differences between cluster sampling and stratified sampling , are:. Though you're welcome to continue on your mobile screen, we'd suggest a desktop or notebook experience for optimal results. Survey software Leading survey software to help you turn data into decisions. Research Edition Intelligent market research surveys that uncover actionable insights.

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Cluster Sampling: Definition, Method and Examples. What is cluster sampling? 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 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. 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. 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 Next, it must determine how large of a sample it wishes to have and it has determined a sample size of 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.