Cluster Sampling & Incidental Sampling
Learning Outcomes
Cluster sampling
In cluster sampling the sample unit contains groups of
elements ( clusters ) instead of individual members or items in the population.
A cluster sample usually produces a larger sampling error than a simple random
sample of the same size , for each cluster – such as a block in given
neighborhood – may be composed of units that are like one another , which
reduces the representativeness of the sample. ( van delan , deobold b, 1978)
The area or cluster sample is a variation of the
simple random sample that is a particularly appropriate when the population of
interest is infinite, when a list of the members of the population does not
exist, or when the geographical distribution of the individuals is widely
scattered. Only when a simple random sample would be impracticable is this
method recommended. ( david dooley 2001)
Figure 1
Cluster Sampling
Definition Of Cluster Sampling
‘A
Cluster sample as a random sample in which each sample unit is a collection, or
elements’.
- Mendenhall, Ott, and
Scheaffer (1971, p. 121)
Cluster sampling, sometimes called area sampling , is
generally used when it is impossible or impractical to construct a sampling
frame in which the sampling units are the sampling elements themselves. ( C R Kothari , Gaurav Garg 2019)
Area Sampling
In sampling of this kind small areas are designed as
sampling units and the households interviewed include all or a specified
fraction of those found in a canvas of these designated small areas. The basic sampling units or segments chosen
may be relatively large or relatively small depending on such factors as the
type of area being studied, population distribution, the availability of
suitable maps and other information and the nature and desired accuracy of the
data being collected. ( Nandan K. Mandal 2012)
Multi- Stage Sampling
Sampling may be done as one process or in stages ,
known as multi-stage sampling. Multi- stage designs are common when populations
are widely dispersed. ( Nandan K. Mandal 2012)
In
multi-stage cluster sampling the researcher chooses a sample in two or more
stages because either the researchers cannot easily identify the population or
the population is extremely large. If this case, it can be difficulty to obtain
a complete list of the members of the population. However, getting a complete
list of groups or clusters in the population might be possible (Vogt, 2005)
Multi-stage
area sampling involves two or more steps that combine some of the probability
techniques already described. Typically , progressively smaller ( lower-
population) geographic areas will be randomly selected in a series of steps . (
C R Kothari . Gaurav Garg 2019)
Multi-stage sampling is used in large scale surveys for more comprehensive investigation. The researcher may have to use two, three or four stage. This method also helps the researcher to check on the consistency of the information obtained from the first sample. This sampling is comparatively convenient, less time-consuming and less expensive method of sampling. (Lokesh Kaul , 1984)
Figure 2
Muti Stage sampling
How To Do Cluster Sampling
1. 1. Define the population and clusters
2. 2. Randomly select clusters
3. 3. Determine cluster size
4. 4. Sample elements within clusters
5. 5. Collect data
Figure
3
Cluster
Sampling
Advantages Of
Cluster Sampling
In
cluster sampling, when larger geographical areas are to be covered , it is
easier to use area sampling than any other method of probability sampling .it
is easier in the sense that the investigator need not have the list of
individuals inhabiting a given area. In area sampling respondents can readily
be substituted for other respondents within the same random section. Area sampling
save both time and money. And area or cluster sampling possesses the trait of
flexibility.
Disadvantages Of
Cluster Sampling
In
cluster sampling , there are increased risk of sampling errors. Less accurate
compared to simple random sampling .can lead to biased results if clusters are
not homogeneous. Analysis can be more complex. Requires a larger sample size to
achieve the same level of precision . May require multiple stages of sampling .
Figure 4
Incidental
Sampling Or Convenience Sampling
Learning outcomes
Incidental Sampling
Let’s Look At An Example
Of Sampling.
A
researcher conducting a study involving native american students finds that a
large percentage of students in one school are native americans. The researcher decides to study this group at
this one school because they are available and because the researcher has the
permission of the principal and can gain consent from the native american
students to participate in the study. This is a convenience sample because the
participants are convenient to the researcher and are available for the study.
( John W. Creswell 2012) .
Figure
4
Convenience
Sampling
Figure 5
Convenience Sampling
Advantages Of
Incidental Sampling
Incidental
sampling is the most convenient method of sampling. This method of sampling
possesses the trait of economy. This method saves time, money , labour of the
investigator.
Disadvantages Of
Incidental Sampling
In
incidental samples remains no longer representative of the population. It can
be biased. In incidental sampling the probability of sampling errors is high.
Figure
6
Convenience
Sampling
Table 1
Difference Between
Cluster And Incidental
|
Cluster Sampling |
Incidental Sampling |
|
Cost reduction |
Easy to use |
|
Selected clusters |
Quick |
|
Division naturally formed |
Cost- effective |
|
More errors |
Useful for small populations |
|
Homogeneity extremely |
Accurate results |
References
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