Multistage Sampling, PDF

What is Multistage Sampling?

Multistage sampling is one of the complex yet cost-efficient sampling methods used by statisticians and survey researchers to obtain a sample from a population by dividing them into smaller and smaller clusters and collecting samples of individuals from the smallest resulting groups. Also referred to as two-stage cluster sampling, multistage cluster sampling, and mixed-methods sampling, this sampling method is a subtype of cluster sampling as it involves multiple cluster sampling at different stages or phases. Based on some findings about the calculations of statistics for multistage sampling, approximately between 30 and 50 strata work effectively for multistage samples using regression analysis. 

For example, suppose you need to estimate the average household income in the United States. Assume that there are 100 million households which represents the whole population you want to survey. It would be costly and time-consuming to gather income data on each household, so you collect a simple random sample of 15 states. After that, collect a simple random sample of 10 counties within each state and then, gather a simple random sample of 100 households within each county. Thus, the resulting sample would include 15,000 total households. There were three different stages used in this example, that’s why this is called multistage sampling. 

Types of Multistage Sampling 

Being an extended version of cluster sampling, multistage sampling is a sampling method used to collect data from smaller groups of your target population through different stages. If you want to gather data from a large, dispersed population, experts in survey research highly recommend that you use multistage sampling. 

Multistage Cluster Sampling: This type of multistage sampling technique involves dividing the population into clusters at various stages for accurate data collection, interpretation, and management. In this method, you draw a sample from a target population using smaller and smaller clusters at each stage. Then, you select the population of interest and narrow down your research by choosing specific cities and towns that represent the region. Multistage Random Sampling: The researcher selects the samples using randomization at each stage and does not generate clusters. This type of multistage sampling allows the researchers to apply random sampling to narrow down the sample. So, if you want to use this type of multistage sampling, you must have a sample size of respondents and randomly select from them and then, further pick out another sample from that randomly selected sample in whatever you like.

Advantages of Multistage Sampling 

Multistage sampling is one of the effective probability sampling techniques used in wide-ranging fields such as in collecting household data for the census, assessing quality control, and gathering data for survey polls such as agriculture and population surveys. What are the major advantages of using multistage sampling?

Cost and Time Efficiency: Multistage sampling is generally a cost-effective and time-efficient sampling strategy. Researchers who are conducting population surveys don’t need to require an actual in-field enumeration of addresses on the sample streets because the public list of household addresses is not available. With the use of the multistage sampling method, they are able to reduce the task of identifying all possible sample elements to just a fraction of the possible sample elements. So, this strategy helps them to save money and time, making them more productive in their survey research work. Controlled Geographic Spread: The travel of survey personnel to each target population or sampling element is one of the biggest cost components of face-to-face surveys. When survey researchers conduct their research using multistage sampling, they can easily control the geographic spread and considerably reduce the geographic area that needs to be covered by each survey interviewer. Use this sampling technique when your individuals of interest are geographically dispersed because there is no need for you to construct a sampling frame of individuals, which in many instances would be expensive.Accurate and Precise Estimation for Heterogenous Population Clusters: If the population clusters you are studying for social research are relatively heterogenous or for example, the individual clusters provide a representative picture of the entire population, multistage sampling will produce accurate estimates. This means there is an increased estimate precision in increased heterogeneity within cluster results. Also, this aspect of multistage sampling makes it unique from other sampling methods like stratified sampling because the increased heterogeneity within strata results in stratified sampling results in a loss of precision as the sampling units within strata should be ideally as similar as possible.Simplification: To avoid the problems of randomly sampling from a population that is bigger than your resources can manage, multistage sampling can give you with limited funds and time to sample from such populations by reducing the population into smaller groups or simplifying the population as you can use the smaller groups as the subject for your random sampling. Versatility: This multistage form of sampling is versatile in eclectic ways. It enables survey researchers to adopt random sampling or cluster sampling after the identification of groups. Other researchers can utilize multistage sampling to break down groups and subgroups into smaller groups until they reach their preferred type or size of groups. Plus, there are no restrictions on how researchers split the population into groups, allowing an enormous number of possibilities for the maximization or minimization of variance.

How to Conduct Multistage Sampling Research

Conducting multistage sampling research provides a useful approach when survey researchers don’t have a sufficient list of the individuals in a population and no method to collect directly from the population. Understand the basic steps on how to conduct this type of sampling strategy for your survey research project.

Step 1: Think Mindfully

It is a good practice that you brainstorm and think mindfully about a way to carry out multistage sampling research. Make sure that the process design you will implement is both cost-efficient and time-efficient. You must retain its randomness and its sample size. Ask a professional expert in this field when you adopt this sampling method for the first time.

Step 2: Divide the Population into Primary Sampling Units

Divide up the population into clusters and choose some of them that are mutually exclusive and exhaustive as your primary sampling units (PSUs) using a probability sampling method. If you want to make sure that the units are actual representatives of the larger population, use a stratified multistage sample like large-scale surveys, a combination of cluster and stratified random sampling. Start when you stratify your clusters at the first stage and select clusters. In this way, you can sample from within each cluster or unit to make new units.

Step 3: Select your Secondary Sampling Units

Split your primary secondary sampling units to get to smaller sampling units. Choose only some of these smaller units from within each selected PSU to obtain your secondary sampling units (SSUs). If you want to end in this stage, this sampling strategy is called two-stage or double-stage sampling. It is optional to continue the process as you keep adding more stages. But continuing this process can further simplify your research process.

Step 4: Repeat the Process

Check your survey research work if you need to repeat the process of dividing up each sampling unit furthermore and choosing a few of them for the next stage. In the last stage, you finally obtain your ultimate sampling units as these units create the final sample that you will gather data from.

FAQs

What are the differences between multistage sampling and multiphase sampling?

In multistage sampling, the respondents or sampling units are of different sizes in different stages as the size of sampling units at any stage will be larger than the units at any succeeding stage. On the other hand, multiphase sampling is a sampling method used by researchers when the sampling unit is similar in all phases. 

How to choose a sampling technique for research?

Choose a sampling technique for your research study or experiment based on the major goals and objectives of your work. For example, if you prefer to investigate for sampling bias, use a random sampling technique. Some examples of random sampling techniques are cluster, stratified, and systematic sampling.

Why is multistage sampling cost-effective?

Multistage sampling is a cost-effective, convenient, and economical sampling method because researchers are able to collect certain items of information from a sample constituting only a portion of the original sample. 

Why do researchers use multistage sampling?

Researchers adopt multistage sampling in their survey research so that they can easily collect, manage, and interpret the data after splitting the population into clusters or groups at different stages.

What are the different types of multistage sampling?

The different types of multistage sampling are multistage cluster sampling and multistage random sampling.

How to minimize the sampling error?

Reduce the sampling error in your survey research by using a sample error formula. Increase the sample size to create a more precise result and divide the population into smaller clusters. Implement random sampling and analyze your target market for your surveys.

What are the advantages and disadvantages of multistage sampling?

The advantages of multistage sampling are researchers don’t have to being with a sampling frame of their population of interest, this sampling technique is relatively inexpensive and effective when researchers have a large or geographically dispersed population compared to a simple random sample, and it is versatile as researchers can adopt different sampling methods between stages according to their feasibility. On the contrary, the disadvantages of multistage sampling are researchers need a larger sample size for a multistage sample to reach the same statistical inference properties, they need logical reasoning for their decision to avoid biased decision-making, and this sampling method can lead to unrepresentative samples due to sampling bias.

Conduct multistage sampling research when you need to make clusters and subclusters until you reach your preferred size or type of group for your survey. It is a cost-efficient, economical, and fantastic sampling technique that can help researchers in various settings such as applying cluster or random sampling after identifying the groups, dividing the population into groups without restrictions while being careful and flexible in selecting their sample, collecting primary data from a geographically dispersed population, and mitigating the problems encountered during random sampling. To fully assist you in your statistic project and survey research analysis, Sample.net provides a variety of well-structured PDF document templates for survey reports and other sampling methods such as convenience sampling and quota sampling.