##### Asked by: Ermina Oppo

asked in category: General Last Updated: 4th June, 2020# What is the difference between cluster sampling and multistage sampling?

**Multistage sampling**can be a complex form of

**cluster sampling**because it is a type of

**sampling**which involves dividing the population into groups (or

**clusters**). Instead of using all the elements contained

**in the**selected

**clusters**, the researcher randomly selects elements from each

**cluster**.

Consequently, what is the difference between stratified sampling and cluster sampling?

The main **difference between stratified sampling and cluster sampling** is that with **cluster sampling**, you have natural groups separating your population. With **stratified** random **sampling**, these breaks may not exist*, so you divide your target population into groups (more formally called "strata").

Also Know, what is cluster sampling in research? **Cluster sampling** refers to a type of **sampling method** . With **cluster sampling**, the researcher divides the population into separate groups, called **clusters**. Then, a simple random **sample** of **clusters** is selected from the population. The researcher conducts his analysis on data from the sampled **clusters**.

In respect to this, what is an example of cluster sampling?

An **example of cluster sampling** is area **sampling** or geographical **cluster sampling**. Each **cluster** is a geographical area. Because a geographically dispersed population can be expensive to survey, greater economy than simple random **sampling** can be achieved by grouping several respondents within a local area into a **cluster**.

Why do we use multistage sampling?

It allows researchers to **apply** cluster or random **sampling** after determining the groups. Researchers can **apply multistage sampling** to make clusters and subclusters until the researcher reaches the desired size or type of group. Researchers can divide the population into groups without restrictions.