This article throws light upon the three different types of sampling techniques followed in research process. The types are: 1. Cluster Sampling 2. Multistage Sampling 3. Multiphase Sampling.
Type # 1. Cluster Sampling:
In this type of sampling divide the total population, depending upon the problem under study, into some recognizable sub-divisions which are termed as clusters and a simple random sample of n blocks is drawn.
The individuals which have been selected from the blocks constitute the sample. Suppose that a survey is to be done in a large town and that the unit of enquiry is the individual household. Suppose further that the town contains 20,000 households, all listed on convenient records, and a sample of 200 is needed. A simple random sample of 200 could well spread over the whole town incurring high costs and much inconvenience.
However one might decide to concentrate the sample in a few parts of the town, Suppose for simplicity the town can be divides into 400 areas with 50 households in each then one could select at random 4 areas (1/100) and include all households in these areas. Note that, unlike stratified sampling, the clusters are thought of as being typical of the population, rather than subsections.
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In fact clusters need not necessarily be natural aggregates, but can simply be artificial (grids on a map). Also note that a cluster of units in one survey may be a unit in another, e.g. if household is the unit then cluster is group of households, if family member is unit then the cluster is household.
Also in any one design several levels of cluster may be used:
a. Constituencies.
b. Wards.
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c. Polling Districts.
d. Households.
Advantages:
(i) Reduced field costs.
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(ii) Applicable where no complete list of units is available (special lists only need be formed for clusters).
Disadvantages:
(i) Clusters may not be representative of whole population but may be too alike.
(ii) Analysis more complicated than for simple random sampling.
Notations:
N is the total number of clusters; n is the number of sampled: Mi is total number of units in the population.
i = 1 Mi is the number of sampling units in the ith cluster; Yij is jth observation in the ith cluster
(j = 1, 2, 3 Mi i=1 2, 3——— N), yij is jth observation in the ith sampled cluster (j = 1, 2, 3…………… Mi i= 1, 2, 3………….. n).
In some times situations Mi as well as M is not known.
Remarks:
Clusters should be as small as possible consistent with the cost and limitations of the survey.
The number of sampling units in each cluster should be approximately same. Thus cluster sampling is not to be recommended if we are sampling areas in the cities where there are private residential houses, business and industrial complexes, apartment buildings, etc., with widely varying number of persons or households.
Type # 2. Multistage Sampling:
One better way of selecting a sample is to resort to sub-sampling within the clusters, instead of enumerating all the sampling units in the selected cluster. This technique is called two-stage sampling, clusters being termed as primary units and the units within the clusters being termed as primary units and the units within the clusters as secondary units.
This technique can be generalized to multistage sampling. We regard population as a number of primary units each of which is further composed of secondary stage units and so on, till we ultimately reach a stage where desired sampling units are obtained. In multi-stage sampling each stage reduces the sample size.
Merits and Limitations:
(i) Multistage sampling is more flexible as compared to other methods. It is simple to carry out and results in administrative convenience by permitting the field work to be concentrated and yet covering large area.
(ii) It saves a lot of operational cost as we need the second stage frame only for those units which are selected in the first stage sample.
(iii) It is generally less efficient than a suitable single- stage sampling of the same size. This brings an end on today’s discussion on sampling techniques.
Thus in the nut shell we can say that Non probabilistic sampling such as convenience sampling, Judgment Sampling and Quota sampling are sometimes used although representativeness of such a sample cannot be ensured.
Type # 3. Multiphase Sampling:
Multiphase sampling is a further development of the principle of the principal of cluster sampling. Suppose we want to investigate the working efficiency of nationalized banks in India and we want to take a sample of few banks for this purpose.
The first stage is to select large primary sampling unit such as states is a country. Then we may select certain district and interview all banks in the chosen districts. This would represent a two stage sampling design with a ultimate sampling units being clusters of districts.
If instead of taking a census of all banks within the selected districts, we select certain towns and interview all banks in the chosen towns. This would represent a three stage sampling design. If instead of taking a census of all banks within the selected towns, we randomly sample banks from each selected towns, then it is case of using a four stage sampling plan.
Ordinarily multi stage sampling is applied in big inquires extending to considerable large geographical area, say, the entire country.
There are two advantages of this sampling design viz.:
(i) It is easier to administer than most single stage design mainly because of the fact that sampling frame under multistage sampling is developed in partial units.
(ii) A large number of units can be sampled for a given cost under multistage sampling because of sequential clustering, whereas this is not possible in most of the simple design.
Differ from One Stage Sample and a Multiphase Sample:
As we have discussed about one stage sampling, keeping in view only the finite populations, but what about single stage sampling in concept of random sample form a multiphase population. However, a few examples will show the basic characteristic of such a sample. Suppose we consider the so throws of a fair dice as a sample form the hypothetically multiphase population which consists of the results of all possible throws of the dice.
If the probability of getting a particular number, say, is the same for each throw and the so throws are all independent, then we say that the sample is one stage. Similarity, it would be said to be sampling from an multistage population if we sample would be considered as a one stage sample would be considered as a one stage sample if in each draws happen to be independent. In brief one can say that the selection of each item in a random sample form an infinite population is controlled by the same probabilities and those successive selections are independent of one another.