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
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.
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
·
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” 4th edition, Cengage Learning(https://research-methodology.net/sampling-in-primary-data-collection/cluster-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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