 |
VOOZH | about |
|
VOOZH | about |
Cluster Random Sampling is a method where we start by picking a whole group and then study everyone within that group. It's not like simple random sampling, where we select people one by one.
It is also known as Cluster Sampling. In cluster random sampling, these groups are what we focus on. This article takes you through cluster sampling, explaining what it is, the different types, how it works, and where it's commonly used.
Cluster sampling is a method of sampling in statistics and research where the entire population is divided into smaller, distinct groups or clusters.
Instead of selecting individual members from the population, researchers randomly choose some of these clusters to include in the study. Then, all members within the selected clusters are surveyed or studied.
Random sampling is a way of picking a group of people or things from a bigger group so that everyone in the big group has the same chance of being picked. The main idea is to make sure the group we pick is a fair and unbiased representation of the big group.
Some of the main benefits of random sampling are:
Read More
Some common examples of Cluster Random Sampling are:
Let's discuss these examples in details as follows:
Imagine assessing the reading skills of fifth-graders in a city. Directly visiting every student across all schools is time-consuming and expensive. Instead, we can:
By analyzing the reading skills of students in the chosen clusters, we can draw inferences about the reading proficiency of fifth-graders across the entire city with improved efficiency and cost-effectiveness.
Consider assessing the effectiveness of a new vaccination program in remote villages. Reaching every village for individual interviews might be challenging. Instead, we can opt for cluster sampling :
By analyzing the data from sampled households in chosen clusters valuable insights into the program's reach and impact across the larger rural population can be studied.
Cluster Random Sampling should be used for when the below scenarios are :
The formula for cluster random sampling involves two stages.
First, calculate the average cluster size (ACS) which is the total number of elements divided by the total number of clusters. Then, multiply ACS with the number of clusters. Formula below:
n = ACS × Number of Clusters
Where n is the desired sample size. This formula ensures a representative and unbiased sample from the entire population through the randomized selection of clusters.
There are various types Cluster Random Sampling such as:
Let's dicuss about these types in detail as follows.
In Single-Stage Cluster Sampling, the entire population is divided into clusters and a random sample of clusters is directly selected for inclusion in the sudy.
After selecting clusters, all individuals within the chosen clusters are included in the sample without further randomization at the individual level. It is relatively simple to implement but may not be as efficient as other methods.
As the name suggests Two-Stage Cluster Sampling includes two stages. In the first stage, a random sample of clusters is selected and in second stage, a random sample of elements is chosen from within each selected cluster.
Two-stage cluster sampling is more efficient than single-stage cluster sampling and is commonly used when there is a hierarchy in the population.
Multi-stage cluster sampling involves more than two stages of sampling. It combines elements of various cluster sampling methods to create a complex sampling design.
This approach is used when there are multiple levels of clustering or when different methods are needed to ensure an efficient and representative sample.
In systematic cluster sampling, clusters are arranged in a list or sequence, and a random starting point is selected. Then, clusters are sampled at regular intervals from the starting point until the desired sample size is achieved.
This method is straightforward and can be more efficient than simple random sampling when clusters are organized in a logical sequence.
To perform cluster random sampling, we ca use the following process:
While performing cluster random sampling, please keep the following points in your mind.
The key difference between Cluster Random Sampling and Simple Random Sampling are:
Aspects | Cluster Random Sampling | Simple Random Sampling |
|---|---|---|
Definition | Divides the population into clusters and selects entire clusters for the sample. | Selects individual elements directly from the entire population with an equal chance of inclusion. |
Process | Population divided into clusters; random clusters selected; all members in chosen clusters included. | Each individual in the population has an equal probability of being directly selected. |
Advantages |
|
|
Efficiency | More efficient for large populations or when natural groupings exist. | Ensures maximum representativeness. |
Cost | Can be cost-effective, particularly when reaching individual elements is expensive. | Resource-intensive for large populations. |
Common advantages of Cluster Random Sampling are:
Common disadvantages of Cluster Random Sampling are:
In conclusion, cluster random sampling is a valuable technique when dealing with large, geographically dispersed populations or naturally occurring groupings. This method offers cost and time efficiency making it logistically convenient, particularly in studies involving administrative units.
Read More
Example 1: Given Total Population: 800 households, Number of Clusters: 40 and Average Cluster Size (ACS) is 20, then determine the sample size using cluster random sampling.
Solution:
As we know, n = ACS × Number of clusters
⇒ n = 20 × 40
⇒ n = 800
Thus, the required sample size is 800 households.
Example 2: Given Schools in District: 60, Average Cluster Size: 15, Desired Sample Size: 300 then determine how many clusters should be randomly selected?
Solution:
Number of Clusters = Desired Sample Size/ ACS
⇒ Number of Clusters = 300 / 15
⇒ Number of Clusters =20
Thus, 20 schools were selected per clusters for the survey.
Example 3: Given hospitals in region is 25, desired sample size is 150 and average Cluster Size is 10 then determine the total number of elements in the sample.
Solution:
Hospitals in Region = 25
Desired Sample Size = 150
Average Cluster Size = 10
Thus, Total Sample Size = Desired Sample Size × ACS
⇒ Total Sample Size = 150 × 10
⇒ Total Sample Size = 1500
Thus, the sample consists of 1500 healthcare elements.
