Contents of the Article
INTRODUCTION - SELECTION OF SAMPLE - CLUSTER SAMPLING (Stages/ Requirements for cluster sampling/ Application of cluster sampling/ Methods with example/ Advantages/ Disadvantages/ Significance in anthropological research) - CONCLUSION - REFERENCES
The word ‘Cluster’ was originated from Old English word ‘Clyster’ which means, “a number of things growing naturally together” and the word ‘Sample’ was originated from the Old French word ‘essample’ which means "small quantity (of something) from which the general quality (of the whole) may be inferred". So both the word together as “cluster sample” would definitely may refer to “the clot of variables or units which represents one of the general quality of a domain whole”, where the nature of each of the variables within a cluster are homogeneous but the clusters represent heterogeneous variables within a domain. So within a population or cultural domain the variables of a cluster are homogeneous in nature on the basis of any trait but the groups are heterogeneous with variations in their basic nature and ultimately all the clusters represents the whole structure of a domain.
Now Sampling is basically the process of selecting a sample. To define, “Sampling is the process to acquire a section of the population to perform an experiment or observational study”. Now sample can make an estimate of the average and prediction can be made about the probable outcome. So more accurately, “Sampling is the process of selecting a few ( a sample) from a bigger group (the sampling population) to become the basis for estimation or predicting the prevalence of an unknown piece of information, situation or outcome regarding the bigger group”. (R. Kumar,1999)
It is important that the group selected as a sample should be ‘representative’ of the population, and not biased in a systematic manner. For this, randomization is typically employed to achieve an unbiased sample, where “it is the process of making something random by minimizing the differences among groups by equally distributing people or individuals”.
Therefore, a cluster is a group of observation units or elements and ‘Cluster sampling is defined as a sampling technique in which the population is divided into already existing groupings or clusters on the basis of any particular trait or variable, where the term cluster refers to a natural, but heterogeneous and intact grouping of the members of the population’.
Before starting with cluster sampling it is important to understand, what are the principles of sampling method and where the cluster sampling is belongs to, how and why researchers tend to choose cluster sampling as a method of selection of sample.
SELECTION OF SAMPLE:
As we all know that Research (French words ‘Re’= ‘again’ and ‘cerchier’= ‘to search’ ) is the systematic investigation and study of materials and resources in order to establish facts and reach new conclusion and after deciding what to re-search the research process follows an eight step model-
1. Formulating a research problem
2. Conceptualising a research design
3. Constructing an instrument for data collection
4. Selecting the sample
5. Writing a research proposal
6. Collecting data
7. Processing and displaying data
8. Writing a research proposal
So, here we can see the steps from 1 to 5 is very crucial as it provides the planning of the whole research study to be conducted and conducting the research proposal represents through step 6 to step 8. Selecting of the sample took the 4th position as until the researcher does not chose the sample, the research would not be possible.
Now , The selection of sample varies in quantitative and qualitative research, as in quantitative research researchers attempt to select samples that are unbiased and represents the population where it is selected and it draws inferences about that population while on the other hand in qualitative research number considerations may influence the selection of a sample and tend to gain in-depth knowledge about a situation or event so can be more concentrated within an individual who is assumed to be knowledgeable about that event or episode and thus can be little biased although for the sake of data.
There are some universal principles in sampling method used in anthropological researches as well as for other fields. These are,
Principle 1 – There will be a difference between the sample statistics and the true population mean (in most of the sampling).
Principle 2 – The greater the sample size, the more accurate the estimate of the true population mean.
Principle 3 – The greater the difference in the variable under study in a population for a given sample size, the greater the difference between the sample statistics and the true population mean.
Ø The position of cluster sampling among various sampling strategies is as follows,
So, as it is showing that Cluster sampling is one type of Random or Probability Sampling Design where the random sampling or probability sampling interprets that each element in the population has an equal and independent chance to be selected in the sample, and the choice of a variable in the sample would not be influenced by other considerations such as any personal preference. Here simple random sampling is the method of selecting a probability sample which means each element in the population has an equal chance of selection without any biases and through stratified random sampling the researcher attempts to stratify the population in such a way that the population within a stratum is homogeneous concerning the characteristic on the basis on it is being stratified.Thus, simple random and stratified sampling techniques are based on a researcher’s ability to identify each element in a population. It is easy to do this if the total sampling population is small, but if the population is large, as in the case of a city, state ,or country that means in a large area sample it becomes difficult and expensive to identify each sampling unit. In such cases the use of cluster sampling is more appropriate and the entire set of cases from which the sample is drawn by the researcher is called the population.
CLUSTER SAMPLING:
Cluster sampling is the method of sampling where the researcher divides the sampling population into several groups based on visible or easily identifiable characteristics, which are called clusters and then to select units or elements within each cluster using simple random technique.
Ø Steps of Cluster Sampling:
A cluster sample is constructed by taking a series of simple random samples in stages. This type of sampling is often more practical than simple random sampling for studies requiring "on location" analysis.
1. In this random sample, a large area, such as a country, is first divided into smaller regions (such as states), and a random sample of these regions is collected.
2. In the second stage, a random sample of smaller areas (such as counties) is taken from within each of the regions chosen in the first stage. Then,
3. In the third stage, a random sample of even smaller areas (such as neighbourhoods) is taken from within each of the areas chosen in the second stage.
Cluster sampling as an example of ‘three-stage’ sampling can be understood as following,
.png)
.png)
For
this nature, the cluster sampling may also called as ‘three-stage sampling’
or sometimes as ‘multistage sampling’ where the stages depends upon
the range of the sample population.
Ø Requirements
for cluster sampling:
1) The
groups must be as heterogenous as possible and must be containing distinct subpopulations
within each cluster.
2) Each
group must offer a smaller representation of what the entire population or
demographic whole contains.
3) To prevent data overlapping, groups must be mutually exclusive from one another.
.png)
Thus,
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.
·
The groups that are similar yet internally
diverse form a statistical population came to be studied ; and
·
Instead of selecting whole data from the
entire population, researchers try to collect data by bifurcating the data into
small and more effective groups.
Ø Application
and Methods of Cluster Sampling with examples:
The application of the cluster sampling can be understood with an examples,
.png)
Cluster
sampling methods :
Cluster sampling can be classified on the basis of the number of stages and representation of the groups in the entire cluster. Depending on the level of clustering, sometimes sampling may be done at different levels. These levels constitute the different stages of clustering, which considered to be the steps taken to get to a desired sample. These are as follows,
.png)
§ Single Stage Cluster Sampling:
As the name suggests, sampling will be done just once. An example of Single Stage Cluster Sampling can be –A researcher wants to conduct a comparative assessment of anxiety among the school students. Here the sampling can be done in single stage like at first a college is selected as a sample population on which the researcher wants to conduct the study and then researcher would cluster the student on the basis of their gender and then every individual under those clusters were included in the study. This can be understood as follows,
.png)
§ 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.
An example of Two-Stage Cluster Sampling –A researcher is inclined towards exploring the level of depression among college students. The researcher creates samples of students belonging to different standards to form clusters and then divides it into the size or operation status of the student. A two-level cluster sampling was formed on which other clustering techniques like simple random sampling were applied to proceed with the calculations. This could be done as,
§ Multiple Stage Cluster Sampling:
For effective research
to be conducted across multiple geographical areas or in broader sampling area,
one needs to form complicated clusters that can be achieved only using
multiple-stage cluster sampling technique. So here,
1. A
list of ‘clusters’ is required ;
2. Sample
has to be clustered randomly; and
3. Sample
has to be selected from every branch
randomly.
Thus this
method is little complex and expensive
and time consuming as well. An example of Multiple Stage Cluster Sampling – Suppose , a researcher wants to study the anxiety
levels among the school students of Kolkata (a large geographical area), so for
that it is a huge sample population and it is not possible to study all the
smaller units within this population. So here this type of sampling is most
useful and it works as follows,
The cluster sampling
method is beneficia as it includes all the benefits of randomized sampling as
well as stratified sampling in its processes which helps to reduce the
biasness. Although no data is completely
accurate without involving every individual within a population but cluster
sampling has many advantages, like,
1) Cluster
sampling technique allows for researcher to be conducted with a reduced economy
and gets results within a very low margin of error.
2) As
this type of sample represents the total sampling population as the cluster,
the interpretations drawn from this sample
strategy can be generalised as a characteristics variable for the whole
population of that the cluster belongs to.
3) The
information obtained through this method offered a reduced variability in its
results because it is a more accurate reflection of the group as a whole.
4) Instead
of sampling an entire broad area, it is fine to allocate limited resources with
few clusters. The design of the cluster sampling approach is specifically
intended to take large populations into account.
5) As
a number of small units makes a large whole, cluster sampling thus provide data
about small units and then represents all together as the data of the whole
domain. Thus this sampling technique can create the most large amount of data
on a particular domain.
6) The
procedures used for obtaining information follows the same process whether it
is within a community or among multiple communities or even demographic level.
7) Some
statistical tests based upon the theory of probability can be applied only to data
collected from cluster type of random samples.
8) This
process is useful for market research when there is no feasible way to find
information about a population or demographic as a whole.
9) It
is the most time-efficient and cost-efficient probability design for large
geographical areas and is easy to be used from practicality viewpoint.
10) Larger
sample size can be used due to increased level of accessibility of perspective
sample group members.
Ø Disadvantages of cluster sampling:
Although cluster
sampling technique has less chance to be biased in choosing sample but it is
quite easier to create biased data
within this sampling. Apart from this it has many disadvantages regarding many
aspects like,
·
From all the different type of probability
sampling, this technique is the least representative of the selected population
as whole.
·
As it combines a number of small clusters,
sampling error can be a major problem and would appeared to be hard to solve and
can be an inaccurate reflection of the general population. In some instances,
the sampling error could be large enough to reduce the representative nature of
the data, invalidating the conclusions.
·
There can be a tendency of
overrepresentation or underrepresentation of clusters which can skew the
results of the study.
·
Cluster sampling has very high chance of
error if the differential rate is higher among the clusters.
·
This sample is not desirable if the
clusters are different so could not be used in every field or every study and
as it is the unit specific sampling it
could not be feasible with the undivided sample population.
·
It has been proven that researchers often
determine cluster placement based on self-identifying information for that
influence of individuals can be seen in data which can be misinterpreted as
well.
·
Every demographic, community, or
population group will have some level of overlap on an individual level so
overlapping of the same data can be a major issue while analysing those.
·
This sampling requires large amount of
sample size as well as variable to be affective as it is limited to small
amount of sample size and less number of variables.
·
The findings from cluster sampling only
apply to those population groups on which the study have been done, it may not
be acceptable as a generalization in a broader geographical region.
· There is also a higher risk of obtaining one-sided data through this process if fewer examples are taken from each cluster.
Ø Importance of cluster sampling in anthropological research:
Anthropologists seeks to find out the real fact about a particular
community or society by conducting researches
on the behaviours of the individuals
in different human population. They are more interested on the deeper
findings of the aim of the study rather than increased number of samples. So
therefore, the cluster sampling method provides an opportunity to go deeper
within clustered samples which later all together provides an anthropologist, the general structure of the aim, even with
more deeper and with holistic dimension.
By dividing and classifying the population into groups (cluster samples), this provides the researcher the ability to account for individuals with a common interest, relative to the larger population. Most importantly, by using the cluster sampling technique, the sample data set is smaller, which helps keep research costs reasonable as well as clear to understand. Apart from this cluster sampling can be a very effective technique to determine the characteristics of a group and can be implemented without the need of other elements of the population.
CONCLUSION:
So it can be understood that clusters
are natural groupings of people—for example, any community, general practices,
private sector or schools. Cluster sampling involves obtaining a random sample
of clusters from the population, where all the participants has equal and
independent chance to participate in the study without any bias. It is
necessary to construct a sampling frame listing all clusters in the population.
A sample of a fixed number of clusters is selected at random from this list.
Each cluster should have the same probability of being selected, independently
of all others. However, if the size of clusters varies then the probability
of selection may be proportional to the size of the cluster, with larger
clusters having a larger probability of selection. Cluster sampling sometimes uses a random sample of
clusters from a conveniently selected geographical region as obtaining a random
sample of clusters from diverse geographical region can be time consuming,
expensive, and impractical.
1. Kumar,
R. (2019). Research methodology: A step-by-step guide for beginners.
2. Bernard,
H. R. (2018). Research methods in anthropology: Qualitative and quantitative
approaches.
3. Sahu,
N. K., & Swain, D. (2013). Research methodology. New Delhi: SSDN
Publishers.
4. Clough,
P., & Nutbrown, C. (2012). A student's guide to methodology: Justifying
enquiry.
5. Taherdoost,
H. (January 01, 2016). Sampling Methods in Research Methodology; How to Choose
a Sampling Technique for Research. Ssr Electronic Journal.
6. Thompson,
S. K. (2013). Sampling. Hoboken, N.J: Wiley.
7. Sedgwick,
P. (July 0 Thompson, S. K. (2013). Sampling. Hoboken, N.J: Wiley.1, 2016).
Cluster sampling. Bmj : British Medical Journal, 348.
8. Pagano,
M., & Gauvreau, K. (2000). Principles of biostatistics. Pacific Grove, CA:
Duxbury.
9. Singh,
R., & Mangat, N. S. (1996). Elements of Survey Sampling. Dordrecht:
Springer Netherlands.
10. http://www.stat.yale.edu/Courses/1997-98/101/sample.htm
No comments:
Post a Comment