It is also referred to as multistage cluster sampling. Multistage sampling is a sampling method that selects a sample from a population by splitting up the population into a progressively smaller group or units at each stage of its application. Such a sampling technique can be seen to be frequently applied in the case of national surveys as it can help collect data from a big and geographically dispersed population.

The difference between multistage sampling and single-stage sampling

Single-stage sampling divides a population into units (for example, households or people) and selects a sample by gathering data from everyone in those units.

Multistage sampling involves breaking down the populace into progressively smaller groups to produce a sample in many steps. You may design a less costly and complex sample to gather data by using hierarchical classifications (for example, from province to town to neighbourhood).

In single-stage and multistage sampling, you may apply either probability or non-probability sampling methods. However, probability sampling approaches are preferable for external validity or generalization since they allow more robust statistical judgments.

Single-stage sampling

A sampling frame, a list of every individual in the complete population, is used in single-stage probability sampling. Therefore, it should be as comprehensive as possible to depict your population appropriately.

Sampling frame: Example

In large-scale research, you’re polling students in your region. Your target demographic is students aged 13 to 19, with a sample size of 7500 individuals being optimum.

Your study’s sample frame is a list of all teen students enrolled in schools across the state. You can get this information by contacting the state education department or requesting a separate list of pupils from each school.

To choose a probability sample from your sampling frame, you can use basic systematic, random, cluster, or stratified sampling approaches.

The difference between cluster sampling and stratified sampling

You split your population into mutually exclusive and exhaustive categories in cluster sampling and stratified sampling.

Cluster sampling divides the population into groups based on geography (for example, towns or regions) or organization (e.g., schools or colleges). In single-stage cluster sampling, you pick certain clusters at random for your sample and gather data from everyone in those clusters all at once.

Single-stage cluster sampling: Example

You partition the sample frame by geography, and you eventually wind up with 98 student groupings depending on geography. Then, you randomly choose 15 clusters and include all individuals from those clusters in your sample.

The population is split into strata in stratified sampling, which are frequently based on demographic variables like ethnicity, sex, or socioeconomic level. Every unit or person in the population is assigned to one of the strata. So that all divisions are represented in your sample, you choose some individuals from each stratum.

Single-stage stratified sampling: Example

The sample frame is divided into three strata, each with a distinct socioeconomic level. To guarantee enough participants from each socioeconomic level in your sample, you utilize random selection to choose people from each stratum independently.

Cluster and stratified sampling are frequently used in multistage sampling.

Multistage sampling

Multistage sampling is frequently thought of as a more advanced variation of cluster sampling.

When applying multistage sampling in surveys, the researcher splits the population into clusters, after which they choose specific clusters in the first stage. The chosen clusters are further split into smaller clusters at each successive level. This process is repeated until the final phase is reached. Only a few individuals from each selected cluster would be selected to form your sample in the final stage.

Just like single-stage sampling, you start by specifying the target population. However, in the case of multistage sampling, listing out every individual in a population is not required for making a sample frame. It makes the multistage sampling approach preferable when studying big and scattered populations.

Example

Your population is made up of all pupils aged 13 to 19 who are enrolled in public schools in your state.

You can’t employ single-stage probability sampling from the entire population if you don’t have access to a complete sample frame. Furthermore, gathering data from a random sample of people across the region would be difficult, expensive, and time-consuming.

Instead, you acquire a representative sample of participants using a multistage sampling procedure.

At each step of multistage sampling, you shift from higher-level to lower-level clusters. The clusters are also known as sample units.

In the first stage, you split the population into clusters and pick some of them as your primary sample units or PSUs.

In the second stage, you’ll split your PSUs into more clusters and choose some of them to serve as secondary sampling units or SSUs.

You can stop at the second stage or go on to the next step as many times as you need. Finally, in the last stage, you’ll get to your final sample of ultimate sampling units or USUs.

Multistage sampling: Example

During the first stage, you compile a list of all state school districts. Then, as your PSUs, you choose 15 school districts.

You include all schools inside those school districts in the second stage. As your SSUs, you choose ten schools from each district.

In the third stage, you acquire a list of all pupils inside those schools. You choose 50 kids from each school to be your USUs and gather data from them.

A probability sampling approach must be applied at each stage to pick clusters for a probability sample. However, you may spice it up by selecting units at each stage depending on what’s important and pertinent to your study utilizing basic random, systematic, or stratified techniques.

• PSUs or Primary Sampling Units

In the first step, you’ll partition your population into mutually exclusive and exhaustive clusters, much like you would in cluster sampling.

Next, preferably, using a probability sampling approach, you’ll pick some of your clusters to be your PSUs. To choose your PSUs, you can utilize any single-stage sampling approach.

To guarantee that the units represent the greater population, large-scale surveys frequently utilize a combination of cluster and stratified sampling in the first step. A stratified multistage sample is what you get when you do this.

At the outset, you must stratify your clusters. Then, you can use a probability sampling approach to choose clusters after stratification.

First stage: Example

In the first step, you opt to utilize a mix of cluster and stratified sampling.

You compile a list of all the state’s school districts. Each school district is a grouping of units. Unlike a list of your sample population of children in the state, you may easily construct a list of school districts because the information is publicly available.

You divide the educational districts into urban, suburban, and rural categories. You want to make sure that your sample includes all three areas.

You make a list of the school districts that belong under each stratum. Then you choose five school districts from each stratum using basic random selection. Your key sample units are these 15 school districts.

Because you would obtain data from everyone inside your specified clusters, single-stage cluster sampling comes to an end (the PSUs). Multistage sampling goes even farther by sampling from inside each cluster or unit to build additional units, which is typically impractical in real life.

• SSUs or Secondary Sampling Units

You split your PSUs in the second stage to acquire smaller sampling units. Only a few of these smaller units will be picked from each PSU: they are your SSUs.

Second stage: Example

You include all schools within your chosen school districts in the second stage. Because gathering data from all schools is time-consuming, you sample from this list to reduce the number of schools you will visit.

You choose 10 schools from each district using a basic random sample procedure. These are the units you’ll use for secondary sampling.

If the sampling process is halted during this step, it is called double or two-stage sampling. It would include gathering information from everyone in your secondary sample units, including all kids in the chosen schools.

Adding extra phases to the process is optional, although it can make the research process easier in some cases.

• USUs or Ultimate sampling units

The sampling units can be split even further by repeating the steps. You’ll get your ultimate sample units at the end of the process.

Final stage: Example

Finally, you call the schools you’ve chosen to receive a list of enrolled pupils. You choose 50 kids from each school using a systematic selection from each list.

These pupils are your USUs, forming the final sample from which you will gather data.

Multistage sampling: Pros and cons

With large samples, multistage sampling is practical and adaptable. However, ensuring that the sample represents the general population could be difficult.

Advantages

ü  You don’t have to start with a target population sample frame.

ü  When you have a big or geographically distributed population, it is reasonably economical and effective compared to a basic random sample.

ü  It’s adaptable—you may change the sample methods between phases depending on what’s acceptable or practical.

Disadvantages

×         Compared to simple random samples, multistage sampling will require a higher sample size to attain the same statistical inference features.

×         Because the optimum sample strategy for each step is very subjective, you’ll need to be able to justify your choices.

×         Large portions of populations may not be picked for sampling, resulting in unrepresentative samples.

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