What is multistage sampling? Multistage sampling is also known as multi stage sampling or multiple stage sampling, is a technique where you select a sample from a population by progressively choosing smaller and smaller groups at each stage. This sampling design is frequently employed for data collection from large, widely dispersed populations, such as in national surveys.
Single-Stage vs. Multistage Sampling
In single-stage sampling, the population is divided into units such as households or individuals, and a sample is directly selected by gathering data from every member within the chosen units.
In multistage sampling, the population is divided into increasingly smaller groups in several steps. This method utilizes hierarchical groupings, like moving from state to city to neighborhood, to form a more cost-effective sample and less time-consuming to collect data from.
Both single-stage and multi stage sampling can employ either probability or non-probability methods. However, probability sampling methods are recommended for better external validity and generalizability, as they allow for more robust statistical inferences.
Single-Stage Sampling
Single-stage sampling is a type of cluster sampling in which the researcher selects a multi-stage sample directly from the clusters or groups within the population without further sub-sampling. In this method, the clusters serve as the primary sampling unit, and all elements within the selected clusters are included in the sample.
A sampling frame is required to conduct single-stage sampling. A sampling frame is a comprehensive list of all the clusters or groups that comprise the target population. It should be complete, accurate, and up-to-date to ensure that every cluster has an equal chance of being selected and to minimize sampling bias.
Sampling Frame Example
You are conducting a large-scale study to survey students in your state. Your target population consists of students aged 13 to 19, and you aim to have a sample size of 7,500 students. To create the sampling frame for your study, you would compile a list of all teenage students registered at schools within the state. You can obtain this list by contacting the state education department or individual schools to request student rosters.
Depending on your research goals, population characteristics, and available resources, you can use simple random, systematic, stratified, or cluster sampling methods to select a probability sample from your sampling frame.
Multistage Sampling in Research
Multi-stage sampling in research is particularly useful for large-scale studies where a complete sampling frame is unavailable or impractical to use. It’s commonly employed in educational research, market research, and national health surveys.
Cluster vs. Stratified Sampling
The population is divided into mutually exclusive and exhaustive groups in cluster and stratified sampling.
Cluster Sampling
The population is divided into clusters in cluster sampling, typically based on geographic areas (such as cities or states) or organizations (like schools or universities). In single-stage cluster sampling, you randomly select some clusters and collect data from every individual within those chosen clusters in a single step.
Single-Stage Cluster Sampling Example
Suppose you divide the sampling frame into 98 geographically based clusters of students. You then randomly select 15 clusters and include all students from these clusters in your sample.
Stratified Sampling
In stratified sampling, the population is divided into strata based on specific characteristics such as race, gender, or socioeconomic status. Each member of the population is placed into one stratum. You then select a sample from each stratum to ensure all groups are represented.
Single-Stage Stratified Sampling Example
Imagine dividing the sampling frame into three strata based on different socioeconomic statuses. You use random selection to choose participants from each stratum separately, ensuring adequate representation of your sample’s socioeconomic level.
Multistage sampling typically combines cluster and stratified sampling techniques to create a comprehensive plan that effectively represents the target population.
Multistage Sampling
Multistage sampling is an advanced form of cluster sampling. In this method, you first divide the population into clusters and select some of these clusters. In subsequent stages, you subdivided the selected clusters into smaller clusters, repeating the process until the final step, where you choose specific members from each cluster for your sample.
Unlike single-stage sampling, multistage sampling doesn’t require a complete list of every population member, making it ideal for large, dispersed populations.
Research Example
You implement a two-stage sampling method. First, you randomly select 15 school districts from across the state. Then, within each chosen district, you randomly select 50 students from the combined enrollment lists of all schools in that district. This approach gives you a sample of 750 students, balancing representativeness with practical constraints. It allows you to gather data from diverse locations while managing travel and coordination efforts.
Multistage Sampling Process
In your multistage sampling approach, you start with broad clusters and narrow down progressively.
- First Stage: Divide the population into clusters and select some as primary sampling units (PSUs). For example, list all school districts in the state and choose 15 districts as your PSUs.
- Second Stage: Subdivide each PSU into further clusters, selecting some as secondary sampling units (SSUs). List all schools within the chosen districts and select 10 schools from each district as your SSUs.
- Third Stage: Obtain a list of all students within the selected schools and choose 50 students from each school for your final sample.
First stage: Primary sampling units
The first stage of multistage sampling involves selecting primary sampling units (PSUs) from the total population. PSUs are the largest sampling units and often represent geographical areas such as states, counties, or cities. Researchers randomly select a subset of PSUs from the total population of PSUs. This selection can be done through simple random sampling or stratified sampling based on relevant criteria such as region, population size, or socioeconomic factors.
multistage sampling example
In a study on household energy consumption patterns across the United States, the PSUs could be the 50 states. Researchers randomly select a subset of states, such as California, Texas, New York, Florida, and Illinois, to represent different regions and population sizes.
Second stage: Secondary sampling units
In the second stage, researchers select secondary sampling units (SSUs) within each of the selected PSUs. SSUs are smaller sampling units that are contained within the PSUs. The selection of SSUs can be done through simple random sampling or stratified sampling based on relevant criteria such as public vs. private schools, urban vs. rural schools, or school size. This stage allows researchers to focus on a more manageable number of schools while ensuring a representative sample.
Example
Within each selected state, researchers choose a subset of counties as SSUs. They may use stratified sampling to ensure the representation of urban, suburban, and rural counties. For instance, in California, they may select Los Angeles County (urban), Orange County (suburban), and Humboldt County (rural).
Third stage: Tertiary sampling units
The third stage involves selecting tertiary sampling units (TSUs) within each of the selected SSUs. TSUs are even smaller sampling units that are contained within the SSUs. The selection of TSUs can be done through simple random sampling or stratified sampling based on relevant criteria such as grade level or subject. This stage further narrows down the sample size while maintaining representativeness.
Example
Researchers select a subset of cities or towns within each selected county as TSUs. They may use simple random sampling or stratified sampling based on population size or other relevant criteria. In Los Angeles County, they may select the cities of Los Angeles, Long Beach, and Santa Clarita.
Final stage: Ultimate sampling units
In the fourth and final stage, researchers select individual sampling units within each of the selected TSUs. This selection can be done through simple random sampling, ensuring that each student is equally likely to be included in the sample. At this point, the sample size has been reduced to a manageable level while still maintaining the representativeness of the target population.
Example
Researchers select individual households as the ultimate sampling units within each selected city or town. They may use systematic sampling, selecting every nth household from a list of all households in the area. In Los Angeles, they may choose every 500th household from a directory, ensuring a geographically dispersed sample within the city.
After selecting the households, researchers can collect data on energy consumption patterns, such as electricity and gas usage, through surveys, interviews, or utility records. The data collected from the sample can then be used to make inferences about household energy consumption patterns across the entire United States, considering the various sampling stages and associated clustering effects.
multistage sampling advantages and disadvantages
Multistage sampling is a sampling technique that involves selecting a sample in multiple stages, with each stage involving a different sampling unit. Here’s a table presenting the advantages and disadvantages of multistage sampling:
| Advantages | Disadvantages |
| Cost-effective, as it reduces travel expenses and time. | Complex design and analysis compared to simple random sampling. |
| Efficient for large and geographically dispersed populations. | Higher sampling error due to clustering effects. |
| Allows for the stratification of populations at different stages. | Requires accurate and up-to-date information about the population at each stage. |
| Flexibility in selecting sampling units at different stages. | Potential for bias if the selection process is well designed and executed. |
| It can be used when a complete target population list is unavailable. | It may not be suitable for small or homogeneous populations. |
Multistage sampling is a valuable tool in research, offering a balance between representativeness and feasibility. Whether you’re conducting a multistage random sampling example or exploring cluster multistage sampling, understanding the principles and applications of this method can greatly enhance your research design and
What is multistage cluster sampling with stratification?
This is a combination of techniques where clusters are selected in stages, and stratification is applied at one or more stages to ensure representation of specific subgroups.
How does multistage sampling compare to stratified sampling?
This is a combination of techniques where clusters are selected in stages, and stratification is applied at one or more stages to ensure representation of specific subgroups.