Question

Stratified Sampling and Cluster Sampling are two approaches used within Probability Sampling Techniques.

Explain using examples, instances where Stratified Sampling would be preferred over Cluster Sampling, and vice versa.

Remember to cite your source using current APA format, and post the url for your paper. Your original post should be no more than 250 words.

Answer 1

In stratified sampling technique, the sample is created out of the random selection of elements from all the strata while in the cluster sampling, all the units of the randomly selected clusters form a sample.

The differences between stratified and cluster sampling can be drawn clearly on the following grounds:

1. A probability sampling procedure in which the population is separated into different homogeneous segments called ‘strata’, and then the sample is chosen from the each stratum randomly, is called Stratified Sampling. Cluster Sampling is a sampling technique in which the units of the population are randomly selected from already existing groups called ‘cluster.’
2. In stratified sampling the individuals are randomly selected from all the strata, to constitute the sample. On the other hand cluster sampling, the sample is formed when all the individuals are taken from randomly selected clusters.
3. In cluster sampling, population elements are selected in aggregates, however, in the case of stratified sampling the population elements are selected individually from each stratum.
4. In stratified sampling, there is homogeneity within the group, whereas in the case of cluster sampling the homogeneity is found between groups.
5. Heterogeneity occurs between groups in stratified sampling. On the contrary, the members of the group are heterogeneous in cluster sampling.
6. When the sampling method adopted by the researcher is stratified, then the categories are imposed by him. In contrast, the categories are already existing groups in cluster sampling.
7. Stratified sampling aims at improving precision and representation. Unlike cluster sampling whose objective is to improve cost effectiveness and operational efficiency.