Multistage Sampling: A Tedious Form of Cluster Sampling

Multistage Sampling | An Introductory Guide with Examples

In multistage sampling, or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups (units) at each stage. It’s often used to collect data from a large, geographically spread group of people in national surveys. Multistage sampling is defined as a sampling method that divides the population into groups (or clusters) for conducting research. It is a complex form of cluster sampling, sometimes, also known as multistage cluster sampling. During this sampling method, significant clusters of the selected people are split into sub-groups at various stages to make it simpler for primary data collection.

Efficient multistage sampling:

• Think cautiously – It’s good practice to brainstorm about a way to implement the multistage approach.
• Keep in mind that as there’s no exact definition of multiphase sampling, there’s no conventional method on a route to mix the sampling methods (such as cluster, stratified, and simple random).
  

  What are the Different Types of Multistage Sampling?

  There are two types of multistage sampling – multistage cluster sampling and multistage random sampling. In market research, multistage sampling is the choosing of samples at stages and choosing smaller sampling units at every step. Let’s take a look at these two approaches in detail.

  What is multistage sampling with Example?

  Multistage sampling is defined as a sampling method that divides the population into groups (or clusters) for conducting research. It is a complex form of cluster sampling, sometimes, also known as multistage cluster sampling. During this sampling method, significant clusters of the selected people are split into sub-groups at various stages to make it simpler for primary data collection. It is to distinguished from multistage cluster sampling.

  There are four multistage steps to conduct multistage sampling:

  • Step one: Choose a sampling frame, considering the population of interest. The researcher allocates a number to every group and selects a small sample of relevant separate groups.
  • Step two: Select a sampling frame of relevant separate sub-groups. Do this from related, different discrete groups selected in the previous stage.
  • Step three: Repeat the second step if necessary.
    • Step four: Using some variation of probability sampling, choose the members of the sample group from the sub-groups.
      

      Example 1:

      Your population is all students aged 13–19 registered at schools in your state. If you’re unable to access a complete sampling frame, you can’t use single-stage probability sampling from the whole population. In addition, collecting data from a sample of individuals across the state would be very difficult, costly, and time-intensive. Instead, you decide to use a multistage sampling method to collect a representative sample of participants.

      In multistage sampling, you always go from higher-level to lower-level clusters at each stage. The clusters are often referred to as sampling units.

      At the first stage, you divide up the population into clusters and select some of them: these are your primary sampling units (PSUs). At the second stage, you divide up your PSUs into further clusters, and select some of them as your secondary sampling units (SSUs). You can end at the second stage, or continue this process with as many stages as you need. In the last stage, you’ll get to your final sample of ultimate sampling units (USUs).

      • In the first stage, you make a list of school districts within the state. You select 15 school districts as your PSUs.
      • In the second stage, you list all schools within those school districts. You select 10 schools from each district as your SSUs.
      • In the third stage, you obtain a list of all students within those schools. You select 50 students from every school as your USUs, and collect data from those students.

      Example 2

      Let’s consider the sample location as the USA. The research goal is to assess the online spending trends of people in the US through an online questionnaire. Researchers can form their sample group comprising 200 households in the following manner:

    • Firstly, choose the number of states using simple random sampling (or any other probability sampling). For example, select ten states.
    • Secondly, choose five districts within each state using the systematic sampling method (or any other probability sampling).
    • Thirdly, choose four households from each district using the systematic sampling or simple random sampling method. You will end up with 200 houses that you can include in the sample group for research.
      
      

      What is the difference between multistage and cluster sampling?

      The process of sampling complete groups or units is called cluster sampling, situations where there is any sub-sampling within the clusters chosen at the first stage are covered by the term multistage sampling. While, the population is regarded as being composed of a number of first stage or primary sampling units (PSU's) each of them being made up of a number of second stage units in each selected PSU and so the procedure continues down to the final sampling unit, with the sampling ideally being random at each stage.

      What are the advantages of multistage sampling?

      The benefits obtained from multistage sampling:

      • It allows researchers to apply cluster or random sampling after determining the groups.
      • Researchers can apply multistage sampling to make clusters and sub-clusters until the researcher reaches the desired size or type of group.
      • Researchers can divide the population into groups without restrictions. It allows flexibility to the researchers to choose the sample carefully.
      • It is useful while collecting primary data from a geographically dispersed population.
      • Cost-effective and time-effective because this method helps cut down the population into smaller groups.
      • Finding the right survey sample becomes very convenient for researchers.
      • The researcher mindfully chooses the audience. It decreases the issues faced during random sampling.
      • It does not need a complete list of all the members of the target population, dramatically reducing sample preparation cost.
      • The researcher does not need to start with a sampling frame of your target population.
      • Compared to a simple random sample, it is relatively inexpensive and effective when you have a large or geographically dispersed population.
      • It is flexible—you can vary sampling methods between stages based on what’s appropriate or feasible.