Cluster Sampling and Stratified Sampling are probability sampling techniques with different approaches to create and analyze samples.
Cluster Sampling is a method where the target population is divided into multiple clusters. Some of these clusters are selected randomly for sampling or a second stage or multiple stage sampling is carried out to form the target sample. Depending on the number of steps followed to create the desired sample, cluster sampling is divided using a single-stage, two-stage or multiple stage sampling techniques. This sampling method is extremely cost-effective as it requires minimum efforts in sample creation and also convenient to execute.
LEARN ABOUT: Survey Sampling
Stratified Sampling is a probability sampling method, also called random quota sampling, where a large population is divided into unique, homogeneous strata and further, members from these strata are randomly selected to form a sample. Elements of each of the samples will be distinct which will give the entire population an equal opportunity to be a part of these samples. Segregation on the basis of age, religion, nationality, socioeconomic backgrounds, qualifications etc. can be done using this sampling technique.
LEARN MORE: Population vs Sample
In this blog, we will be discussing Cluster Sampling vs Stratified Sampling.
Factors for Comparison
|Definition||Members of this sample are chosen from naturally divided groups called clusters, by randomly selecting elements to be a part of the sample.||Members of this sample are randomly chosen from non-overlapping, homogeneous strata.|
|Purpose||Cost reduction and increased efficiency.||Enhanced precision and population depiction.|
|Sample selection||Selection of the sample is done by randomly selected clusters and including all the members from these clusters.||Selection of the sample is done by randomly selecting members from various formed strata.|
|Selection of elements that form a Sample||Conjointly||Distinctively|
|Division type||Naturally formed||Depends on the researcher|
|Heterogeneity||Internally, with the clusters||Externally, between various strata|
|Homogeneity||Externally, between various clusters||Internally, with the strata|
Cluster Sampling – Key Points:
- Naturally existing groups are chosen to be a part of the final sample set.
- Mainly used in market research, in this technique, a population is divided into clusters and these clusters are randomly chosen to be a part of the sample.
- Information can also be collected from elements selected from each of the sub-clusters.
- This method is usually applied in groups where there is diversity within the groups and not between clusters.
- The only prerequisite is that all the clusters should be distinctive and non-overlapping.
Stratified Sampling – Key Points:
- A population is divided into strata by random selection.
- The simplest explanation of strata is a group of members of a population.
- Simple random sampling is then performed on these strata to form samples.
- One similarity that stratified sampling has with cluster sampling is that the strat formed should also be distinctive and non-overlapping.
- By making sure each stratum is distinctive, the errors in results are drastically reduced.