CLUSTER SAMPLING

Cluster sampling

Cluster sampling is defined as a sampling method where multiple clusters of people are created from a population where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population(‘Cluster sampling—Wikipedia’, n.d.) .

Figure 1.cluster sampling

In this sampling technique, analysis is carried out on a sample which consists of multiple sample parameters such as demographics, habits, background – or any other population attribute which may be the focus of conducted research. This method is usually conducted when groups that are similar yet internally diverse form a statistical population. Instead of selecting the entire population of data, cluster sampling allows the researchers to collect data by bifurcating the data into small, more effective groups (Phillips, 2015).

Another example of this would be; let’s consider a scenario where an organization is looking to survey the performance of smartphones across Germany. They can divide the entire country’s population into cities (clusters) and further select cities with the highest population and also filter those using mobile devices. This multiple stage sampling is known as cluster sampling.

Cluster Sampling: Steps And Tips

Some steps and tips to use cluster sampling for market research, are:

Sample.  Decide the target audience and also the size of the sample.

Create and evaluate sampling frames.  Create a sampling frame by using either an existing frame or creating a new one for the target audience. Evaluate frames on the basis of coverage and clustering and make adjustments accordingly.

Determine groups.  Determine the number of groups by including the same average members in each group. Make sure each of these groups are distinct from one another.

Select clusters.  Choose clusters randomly for sampling.

Geographic segmentation.  Geographic segmentation is the most commonly used cluster sample.

Sub-types.Cluster sampling is bifurcated into one-stage and multi-stage subtypes on the basis of the number of steps followed by researchers to form clusters.

Cluster Sampling Methods With Examples

There are two ways to classify cluster sampling. The first way is based on the number of stages followed to obtain the cluster sample and the second way is the representation of the groups in the entire cluster.The first classification is the most used in cluster sampling. In most cases, sampling by clusters happens over multiple stages. A stage is considered to be the steps taken to get to a desired sample and cluster sampling is divided into single-stage, two-stage, and multiple stages.

Single Stage Cluster Sampling.  As the name suggests, sampling will be done just once. An example of Single Stage Cluster Sampling –An NGO wants to create a sample of girls across 5 neighboring towns to provide education. Using single-stage cluster sampling, the NGO can randomly select towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.

Two-Stage Cluster Sampling.   A sample created using two-stages is always better than a sample created using a single stage because more filtered elements can be selected which can lead to improved results from the sample. In two-stage cluster sampling, instead of selecting all the elements of a cluster, only a handful of members are selected from each cluster by implementing systematic or simple random sampling

Multiple Stage Cluster Sampling.   For effective research to be conducted across multiple geographies, one needs to form complicated clusters that can be achieved only using multiple-stage cluster sampling technique. Steps of listing and sampling will be used in this sampling method. An example of Multiple Stage Cluster Sampling –Geographic cluster sampling is one of the most extensively implemented cluster sampling technique. If an organization intends to conduct a survey to analyze the performance of smartphones across Germany. They can divide the entire country’s population into cities (clusters) and further select cities with the highest population and also filter those using mobile devices.

Why Cluster Sampling?

In an ideal world, research practitioners would love to survey the entire population and select their respondents randomly to make sure everyone is accounted for and therefore ensure their research results are as accurate as possible. This is referred to as random sampling. Unfortunately, there are two issues associated with this approach – cost and feasibility. However, by dividing and classifying the population into groups (cluster sampling), this provides the researcher the ability to account for individuals with a common interest, relative to the larger population. By using the cluster sampling technique, the sample data set is smaller, which helps keep research costs reasonable.

When using cluster sampling methods, it is critical to keep in mind that only one variable (element) can be assigned to a cluster. In most cases, clusters are created by geography. For example, if Apple wanted to gauge the performance of the iPad in Spain, the researcher would create clusters by all cities in Spain. The larger cities would be accounted for and cluster analysis would determine the usage of iPad by each city.

Cluster Sampling Advantages And Disadvantages

There are multiple advantages and disadvantages of using cluster sampling, they are:               

Table 1advantages and disadvantages of cluster sampling

Advantages	Disadvantages
consume less time and cost	May not reflect the diversity of the community
convenient access	clusters may share similar characteristics
least loss in accuracy of data	provides less information
ease of implementation	standard error estimates are high compared to others
differentiates into clusters	biased samples

Applications Of Cluster Sampling

·         This sampling technique is used in an area or geographical cluster sampling for market research

·         A widespread geographical area can be expensive to survey in comparison to surveys that are sent to clusters which are divided on the basis of area (Phillips, 2015)

·         The sample numbers have to be increased to achieve accurate results but the cost savings involved make this process of increasing clusters attainable.

Incidental Sampling

Accidental or incidental is that type of sampling in which a researcher pick up data or information’s from those who fall into hand or present at the time of research. It continues the process till the completion of the sample size. It is accidental because it is selected accidentally from all type of people comes to face like, teacher, students, house wife, tailors, workers, etc..Accidental sampling, also known as grab or opportunity sampling, is a form of non-probability sampling that involves taking a population sample that is close at hand, rather than carefully determined and obtained. For instance, a person who is obtaining opinions for a political poll at a shopping mall by randomly selecting passers-by is using a form of accidental sampling. Accidental samples are not as experimentally sound as using random sampling and random assignment.

CLUSTER SAMPLING PPT from kpsilpa

References

1.Adi Bhat, Global VP Sales and Marketing at Question Pro (https://www.questionpro.com/blog/cluster-sampling/)

2.  Jackson, S.L. (2011) “Research Methods and Statistics: A Critical Approach” 4^th edition, Cengage Learning(https://research-methodology.net/sampling-in-primary-data-collection/cluster-sampling/)

3. (https://en.wikipedia.org/wiki/Convenience_sampling)

4.Cluster sampling—Wikipedia. (n.d.). Retrieved 20 August 2019, from https://en.wikipedia.org/wiki/Cluster_sampling

Phillips, G. W. (2015). Impact of Design Effects in Large-Scale District and State Assessments. Applied Measurement in Education, 28(1), 33–47. https://doi.org/10.1080/08957347.2014.973561

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