# Stratified Sampling vs Cluster Sampling

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Stratified Sampling and Cluster Sampling are both a part of Probability Sampling in Statistical Analysis. Probability Sampling is a procedure of selecting samples from a broad population.

The theory behind probability sampling is to randomly select a sample for the purpose of research.

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## Stratified Sampling

Stratified Sampling is a category under Probability sampling which is based on dividing a population into strata, and members for the sample are selected randomly from these strata.

In stratified sampling, the strata must be homogenous and also collectively exhaustive, and mutually exclusive as well. The strata must define a part of the population. Moreover, the members of the sample must be distinct, that is, every element must be a part of one and only one strata in the population. This implies that the entire population requires to be a part of the samples.

To ensure precision and reduce sampling error, simple random sampling is employed in each stratum.

## Characteristics of Stratified Sampling

1. Members of a sample in Stratified Sampling are chosen randomly from homogenous and non-overlapping strata.
2. Each member of the sample must belong to one and only one strata so that the sample can represent the entire population.
3. Stratified samples in the case of population density which varies majorly within a target region, can estimate with accuracy in different parts of the region.
4. When strata during measurement have a lower standard deviation, stratification ensures a reduction in error in estimation.

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## Cluster Sampling

Often used in market research, Cluster Sampling, is a technique used when homogeneity is external but heterogeneity is internal within clusters/groupings. It is a process of dividing a population into multiple groups/clusters.

Cluster sampling is generally used to reduce the number of interviews and the cost, to reach the desired accuracy. When the majority of heterogeneity is internal within the group in fixed sample size, the chance of random error is reduced.

There are single-stage, two-stage, or multiple-stage sampling methods in cluster sampling. These methods depend upon the number of steps required to create the desired sample.

In Cluster Sampling, the total population is divided into clusters. A simple random sample of the cluster is chosen and the elements in each of these clusters are then sampled.

One-stage/Single-stage Cluster Sampling: In case when all elements in each cluster sample are sampled.

Two-stage Cluster Sampling: When from each of these clusters a simple random subsample of elements is chosen.

### Characteristics of Cluster Sampling

1. Cluster sampling is cost-effective in contrast to other sampling techniques.
2. Surveys on large populations are handled with help of cluster sampling.
3. In a cluster sampling, the population should be heterogeneous but there must be homogeneity between clusters/groups
4. The clusters must be representative of the total population.

### Cluster Sampling Usage

As mentioned cluster sampling is advantageous when a large population is in need of a survey because it is less costly. Hence, area sampling is one of the examples of Cluster Sampling. Also, it is used when high mortality cases like wars, famines and natural diseases are required for estimation.

In the process of cluster sampling, respondents are grouped within a local area into several clusters. However, it is also essential to achieve precision in the estimate for which sample size must be increased.

## Key Point Difference between Stratified Sampling and Cluster Sampling

All the above information highlights the difference between the two categories of the Sampling. Underneath are some key difference to clear any lingering doubts

• In Cluster Sampling, the sampling is done on a population of clusters therefore, cluster/group is considered a sampling unit.
• In Stratified Sampling, elements within each stratum are sampled.
• In Cluster Sampling, only selected clusters are sampled.
• In Stratified Sampling, from each stratum, a random sample is selected.
• In Cluster Sampling, the aim is to reduce cost and increase the efficiency of sampling.
• In Stratified Sampling, the motive is to increase precision to reduce error.

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