Cluster sampling and
stratified sampling are two different sampling methods. The main
difference between them is that a cluster is treated as sampling
unit. Hence, in the first stage, analysis is done on a population of
clusters. In stratified sampling, the elements within the strata are
analysed.
Cluster Sampling
In this mode of sampling, the naturally occurring groups are selected for being included in the sample.
Its main use is in market research. In this method, the total population is divided into samples or groups after which, a sample of the groups is selected.
After this process, relevant and required data from all the elements of all the groups is collected.
At times, instead of collecting information from each group, information can be collected from a sub-sample of the elements.
If the variation is between the members of the groups and not between the actual groups, then this technique will work the best.
Before you start using this methods on clusters, make sure that the clusters are collectively exhaustive and mutually exclusive.
Stratified Sampling
In this technique, a sample is divided into stratum and on random basis.
Different stratum are created, which will allow the usage of different sampling percentage in each stratum.
These stratum are nothing but simple groups, which consists of a number of elements.
On these stratum, simple random selection is performed.
Make sure that every element is assigned only one stratum. This method is known to produce weighted mean whose variability is less than that of arithmetic mean of a simple random sample of the population.
Even in stratified sampling, the strata should be collectively exhaustive and mutually exclusive.
This will help in applying random or systematic sampling in each of the stratum. This will also help in the reduction of errors.
Cluster Vs. Stratified
Cluster Sampling
Application: It is used when natural groupings are evident in a statistical population.
Choice: It can be chosen if the group consists of homogeneous members.
Advantage: The method is cheaper as compared to the other methods.
Disadvantage: The main disadvantage is that it introduces higher errors.
Stratified Sampling
Application: In this method, the members are grouped into relatively homogeneous groups. This allows greater balancing of statistical power of tests.
Choice: It is a good option for heterogeneous members.
Advantages: This method ignores the irrelevant ones and focuses on the crucial sub populations. You can opt for different techniques. This also helps in improving the efficiency and accuracy of the estimation.
Disadvantage: It requires a choice of relevant stratification variables, which can be tough at times. When there are homogeneous subgroups, it is not very useful, and its implementation is expensive. If not provided with accurate information about the population, then an error may be introduced.
In this mode of sampling, the naturally occurring groups are selected for being included in the sample.
Its main use is in market research. In this method, the total population is divided into samples or groups after which, a sample of the groups is selected.
After this process, relevant and required data from all the elements of all the groups is collected.
At times, instead of collecting information from each group, information can be collected from a sub-sample of the elements.
If the variation is between the members of the groups and not between the actual groups, then this technique will work the best.
Before you start using this methods on clusters, make sure that the clusters are collectively exhaustive and mutually exclusive.
Stratified Sampling
In this technique, a sample is divided into stratum and on random basis.
Different stratum are created, which will allow the usage of different sampling percentage in each stratum.
These stratum are nothing but simple groups, which consists of a number of elements.
On these stratum, simple random selection is performed.
Make sure that every element is assigned only one stratum. This method is known to produce weighted mean whose variability is less than that of arithmetic mean of a simple random sample of the population.
Even in stratified sampling, the strata should be collectively exhaustive and mutually exclusive.
This will help in applying random or systematic sampling in each of the stratum. This will also help in the reduction of errors.
Cluster Vs. Stratified
Cluster Sampling
Application: It is used when natural groupings are evident in a statistical population.
Choice: It can be chosen if the group consists of homogeneous members.
Advantage: The method is cheaper as compared to the other methods.
Disadvantage: The main disadvantage is that it introduces higher errors.
Stratified Sampling
Application: In this method, the members are grouped into relatively homogeneous groups. This allows greater balancing of statistical power of tests.
Choice: It is a good option for heterogeneous members.
Advantages: This method ignores the irrelevant ones and focuses on the crucial sub populations. You can opt for different techniques. This also helps in improving the efficiency and accuracy of the estimation.
Disadvantage: It requires a choice of relevant stratification variables, which can be tough at times. When there are homogeneous subgroups, it is not very useful, and its implementation is expensive. If not provided with accurate information about the population, then an error may be introduced.
