Research

Difference Between Stratified Sampling and Cluster Sampling

Main difference

The main difference between stratified sampling and cluster sampling techniques is that in stratified sampling, subgroups known as strata are manually created by the researcher and the sample is taken at random according to their choice. On the other hand, in cluster sampling, naturally formed groups in the population known as clusters are concerned with collecting the sample.

Stratified sampling versus cluster sampling

Stratified is the type of sampling method that is preferred when individuals in the population are diverse, and are manually divided into subgroups called strata to obtain precise and accurate results. While in the cluster sampling technique it is ideal when the individuals of the natural groups known as clusters, do not have much diversity and can be randomly sampled to obtain efficient and profitable results.

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. Samples are then drawn at random from each pool created. Cluster sampling is the type of sampling technique in which the population is not manually divided into any groups, while the samples are randomly selected from the naturally formed groups called clusters.
Divergence
Carried out by the researcher or group of researchers Clusters naturally occur forming subgroups.
Type of sample
In the stratified sampling method, the sample is drawn randomly from all manually created subgroups or strata. In the cluster sampling method, the sample is taken at random from all naturally formed population clusters.
focal lens
The basic goal of stratified sampling is to refine the entire sample so that only the affected sample population is drawn to ensure accurate results. The main objective of cluster sampling is to increase the efficiency of both the sampling method and the test performed. Another reason is to make the sampling method cost effective.
Heterogeneity
On the basis of heterogeneity, samples are taken from among the manually created strata. On the basis of heterogeneity, samples are taken within the naturally occurring group or cluster.
Homogeneity
For homogeneity, the samples are taken from the artificially created subgroups. For homogeneity, the samples in cluster sampling are taken from different natural clusters.
population assortment
The elements of the population are selected individually in a stratified sampling method. In the cluster sampling method, unlike stratified sampling, the elements of the population are selected collectively.
Applications
Population diversification No population diversification
Subtypes
Proportional Stratified Sampling, Disproportionate Stratified Sampling
One-stage cluster sampling, two-stage cluster sampling, multi-stage cluster sampling

What is stratified sampling?

Stratified sampling is a kind of sampling technique in which the population is divided into two subgroups or strata. Samples are then drawn at random from each pool 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 elements of the population are selected individually in a stratified sampling method. For heterogeneity, the samples are taken from among the manually created strata. For homogeneity, the samples are taken from the artificially created subgroups. The divergence of groups in the stratified sampling method is usually done by the researcher or group of researchers manually in their own set of anticipated techniques. The divergence of groups in the stratified sampling method is usually done by the researcher or group of researchers manually in their own set of anticipated techniques. Stratified sampling is subdivided into proportional stratified sampling and disproportionate stratified sampling.

What is cluster sampling?

Cluster sampling is the type of sampling technique in which the population is not manually divided into any groups, while the samples are randomly selected from the naturally formed groups called clusters. Cluster sampling is the most efficient sampling technique and is most suitable when the individuals in the population within the clusters do not have diversity in them. In the cluster sampling method, unlike stratified sampling, the elements of the population are selected collectively. For heterogeneity, the samples are taken within the naturally developed group or cluster, while for homogeneity, the samples in cluster sampling are taken at random from the different clusters as a whole.

Key differences

  • The stratified sampling method is more expensive, while cluster sampling is an efficient and cost-effective method when trying to target a less diverse natural group of the population.
  • Manual subgroups known as Strata are formed by researchers according to the specific requirements of stratified sampling, while natural subgroups known as Clusters are preferred for extracting large-scale efferent random sampling.
  • In a stratified sampling method, the population elements are selected individually, on the other hand, in the cluster sampling method, the population elements are selected collectively.

Final Thought

The stratified sampling method is suitable for the population with diversity in its individuals and when the targets in question are individuals. Considering that the cluster sampling method is suitable when the objective is natural collective individuals with minimal diversity. Cluster sampling is the most efficient and cost-effective sampling method.

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