Stratified Sampling vs. Cluster Sampling

Main Difference
The main difference between stratified sampling and cluster sampling techniques is that in the stratified sampling sub-groups known as strata are manually created by the researcher, and the sample is taken randomly as per choice. On the other hand in cluster sampling, the naturally formed groups in the population known as clusters are concerned for picking up the sample.

Difference Between Stratified Sampling and Cluster Sampling

Stratified Sampling vs. Cluster Sampling

The stratified sampling method is more expensive whereas cluster sampling is an efficient and cost-effective method when it comes to targeting a natural less diverse group of the population.

Stratified Sampling vs. Cluster Sampling

Manual sub-groups known as Strata are formed by researchers as per specific requirements in the stratified sampling while naturally occurring sub-groups known as Clusters are preferred for extracting the efferent random sampling on a large scale.

Stratified Sampling vs. Cluster Sampling

In a stratified sampling method, the population elements are selected individually, on the other hand, in the cluster sampling method, the elements of the population are selected collectively.

Comparison Chart

Stratified Sampling	Cluster Sampling
Stratified sampling is a kind of sampling technique in which the population is divided into two subgroups or strata. The samples are then extracted randomly from every group created.	Cluster sampling is the kind of sampling technique in which the population is not divided manually into any groups, whereas the samples are randomly picked from the naturally formed groups termed as clusters.
Divergence
Done by the researcher or group of researchers	The clusters occur naturally forming subgroups.
Kind of Sample
In the stratified sampling method, the sample is taken up randomly from all the manually created subgroups or strata.	In the cluster sampling method, the sample is taken up randomly from all over the naturally formed population clusters.
Focal Goal
The basic goal of stratified sampling is to precise the whole sample so that only concerned sample population is extracted to ensure accurate results.	The main goal of cluster sampling is to increase the efficiency of both the sampling method and the conducted test. Another reason is to make the sampling method cost-effective.
Heterogeneity
On the basis of heterogeneity, the samples are taken from between the manually created strata.	On the basis of heterogeneity, the samples are taken within the naturally developed group or cluster.
Homogeneity
By homogeneity, the samples are taken from within the artificially created subgroups.	By homogeneity, the samples in cluster sampling are taken from different natural clusters.
Population Assortment
The population elements are selected individually in a stratified sampling method.	In the cluster sampling method unlike stratified sampling, the elements of the population are selected collectively.
Uses
Diversification in Population	No diversification in Population
Subtypes
Proportionate Stratified Sampling, Disproportionate Stratified Sampling	Single-stage Cluster Sampling, Double-stage Cluster Sampling, Multistage Cluster Sampling

Stratified Sampling vs. Cluster Sampling

Stratified sampling is the sort of sampling method that is preferred when the individuals in the population are diverse, and they are manually divided into subgroups called strata for precise and accurate results. Whereas in the cluster sampling technique is ideal when the individuals in the naturally occurring groups known as clusters, does not possess much diversity and can be randomly sampled for efficient and cost-effective results.

What is Stratified Sampling?

Stratified sampling is a kind of sampling technique in which the population is divided into two subgroups or strata. The samples are then extracted randomly from every group created. Stratified sampling is best when the individuals in the population differ from each other as a whole; this is because they are manually divided into subgroups. The population elements are selected individually in a stratified sampling method. By heterogeneity, the samples are taken from between the manually created strata. By homogeneity, the samples are taken from within the artificially created subgroups. The divergence of the groups in the stratified sampling method is usually done by the researcher or group of researchers manually on their own set of anticipated techniques. The divergence of the groups in the stratified sampling method is usually done by the researcher or group of researchers manually on their own set of anticipated techniques. Stratified Sampling is subdivided into proportionate stratified sampling and disproportionate stratified sampling.

What is Cluster Sampling?

Cluster sampling is the kind of sampling technique in which the population is not divided manually into any groups, whereas the samples are randomly picked from the naturally formed groups termed as clusters. Cluster sampling is the most efficient sampling technique and is most suitable when the individuals in the population inside the clusters do not have any diversity in them. In the cluster sampling method unlike stratified sampling, the elements of the population are selected collectively. By heterogeneity, the samples are taken within the naturally developed group or cluster whereas when it comes to homogeneity, the samples in cluster sampling are taken randomly from the different clusters as a whole. Cluster Sampling is subdivided into single-stage cluster sampling, double-stage cluster sampling, and multistage cluster sampling.

Conclusion
The Stratified sampling method is suitable for the population with diversity in its individuals and when the concerned targets are individuals. Whereas the Clustering sampling method is suitable when natural collective individuals with minimum diversity are a target. Cluster sampling is the most efficient and cost-effective sampling method.