80+ Cluster Sample

What is a Cluster Sample?

A cluster sample is a statistical sampling technique used in research studies to select a subset of participants from a large population. Cluster sampling, in contrast to random sampling, involves choosing groups or clusters of individuals according to their common features or geographic location. The individuals inside each of these clusters are then selected at random, and they are all included in the sample. It is often used when the population of interest is large and geographically dispersed. This method is frequently used in a variety of disciplines including the social sciences, public health, and market research because it enables investigators to more accurately capture population variation and minimize sample bias. 

To examine the prevalence of various attitudes, beliefs, and behaviors among individuals across different groups, sociologists and psychologists utilize cluster sampling. For instance, a psychologist may conduct cluster sampling to investigate the incidence of depression in various communities. This sampling technique is used by educational researchers to assess the efficacy of educational initiatives and programs across several districts or schools. It is used by economists to research the effects of economic interventions or policies in various geographic areas. Also, public health researchers use cluster sampling to study the prevalence of various diseases like COVID-19, health behaviors, and risk factors in different populations.

Types of Cluster Samples

Cluster sampling is a popular sampling technique used in various fields to efficiently and effectively gather data from large populations. There are several types of cluster samples, each with its own advantages and disadvantages. Understanding the different types of cluster samples can help researchers choose the most appropriate method for their study

Adaptive Cluster Sample: Adaptive cluster sampling is a technique that modifies the clusters chosen for sampling based on the outcomes of preceding samples. With this technique, sampling effectiveness in populations with high variability can be improved. Area Cluster Sample: Large geographic regions are studied using area cluster sampling. This type of sampling involves selecting larger geographic areas at random, then randomly choosing smaller geographic areas from inside those areas. Multi-Stage Cluster Sample: In a multi-stage cluster sample or a multistage sampling, the population is divided into larger clusters, and then smaller clusters are selected within each larger cluster. Finally, individuals are selected from the smaller clusters to form the final sample. Network Cluster Sample: To examine networks of people, such as social or professional networks, network cluster sampling is utilized. In this type of sampling, a random sample of people is picked, and the connections or relationships between them are then analyzed to create the final sample. Probability Proportional to Size Cluster Sample: Clusters are selected using probability proportional to size cluster sampling depending on their size in relation to the overall population. By providing larger clusters with greater weight when choosing the sample, this technique can improve the sample’s representativeness. Simple Random Sample: Simple random sample is a sample that involves selecting individuals from a population at random. This type of sampling is useful when the population is relatively small and easily accessible. Social Cluster Sample: To study social groupings like families or houses, social cluster sampling is performed. In this type of sampling, groups of people are chosen at random, and the entire group that was chosen is included in the sample. Systematic Cluster Sample: Systematic cluster sampling involves selecting clusters at regular intervals from a population. Also known as systematic sampling, this type of sampling can be useful when the population is too large to sample completely. Stratified Random Sample: Stratified random sampling involves dividing the population into subgroups, or strata, based on relevant characteristics, and then selecting a random sample from each stratum. This type of sampling can increase the precision of estimates by reducing variability within each stratum. Two-Stage Cluster Sample: In a two-stage cluster sample, a random sample of clusters is selected, and then a random sample of individuals within each selected cluster is included in the final sample.

Advantages and Disadvantages of Cluster Sampling

Cluster sampling is a widely used sampling method that offers several advantages, including cost-effectiveness and efficiency. However, it also has some disadvantages, such as reduced precision and limited generalizability. What are the advantages and disadvantages of cluster sampling?

Advantages: Cluster sampling can be more cost-effective than other sampling methods. It can be more efficient than other sampling methods since it reduces the number of units that need to be sampled. It is relatively easy to implement and requires fewer resources than other sampling methods and is ideal for studying populations that are naturally clustered, such as households or schools. Disadvantages: Cluster sampling can be less precise than other sampling methods since it increases variability within clusters. It may not be representative of the entire population, especially if the clusters are not well-defined or heterogeneous. This method requires a larger sample size than other sampling methods to achieve the same level of precision. Plus, it can introduce bias if the clusters are not selected randomly or if there is significant variation within clusters.

How to Use a Cluster Sample

A cluster sample is a sampling method used to collect data from a population. To use it, divide the population into clusters, randomly select clusters, and sample all members within the selected clusters. Follow the steps below to use a cluster sample for your survey research study effectively.

Step 1: Identify the Population

Define the population of interest and determine if it can be grouped into clusters. Think about the research question and identify the main variables of interest to help you determine the population that is relevant to the study. Consider who should be included and excluded from the study based on specific criteria such as age, gender, health status, location, etc. Review existing research literature to gain a better understanding of the population and its characteristics and help you identify relevant sub-populations. Once the population has been identified, define the sampling frame which is a list of individuals or units that comprise the population like census data, registries, etc.

Step 2: Choose the Sampling Method

Select the appropriate sampling method, such as simple random sampling, stratified sampling, or cluster sampling, based on the characteristics of the population and research goals. If you like to use cluster sampling, decide what type of cluster samples you will use.

Step 3: Determine the Cluster Size

Calculate the number of clusters needed and the size of each cluster using the cluster sampling formula. For two-stage cluster sampling, select a random sample of clusters, and then, select a sample of individuals within each selected cluster.

Step 4: Collect Data

Collect data from the selected clusters using proper sampling techniques and statistical methods.

Step 5: Analyze the Data

Examine the data using proper statistical methods and report findings based on the sampling method used. Be sure to consider the advantages and disadvantages of cluster sampling when interpreting results.


What is the cluster sampling formula?

The cluster sampling formula is used to calculate the required sample size for a cluster sample. It is given by: n = (N * n_c) / (N_c + k – 1), where: n is the required sample size, N is the size of the population, n_c is the average cluster size, N_c is the total number of clusters, and k is the number of clusters to be selected. The formula takes into account the cluster size, the number of clusters, and the desired level of precision. The resulting sample size will depend on the size of the population, the cluster size, and the number of clusters selected. Take note that the cluster sampling formula assumes that the clusters are homogeneous and that there is no intra-cluster correlation. If these assumptions are violated, the formula may not provide accurate results.

What is the difference between a cluster sample and a multistage sample?

A cluster sample is a sample used to select a representative sample from a larger population which involves dividing the population into clusters or groups based on a geographic location or some other grouping variable. A sample of clusters is randomly selected, and data is gathered from all individuals within those selected clusters. On the other hand, a multistage sample is a sample being taken at each stage. In the first stage, clusters are randomly selected, and in subsequent stages, sub-sampling is done within those clusters. This process continues until the desired sample size is reached. The main difference between the two is that in cluster sampling, all individuals within a selected cluster are included in the sample, while in multistage sampling, only a subset of individuals within each selected cluster is included in the final sample.

Researchers can select a representative sample from a wider population using the valuable statistical method of cluster sampling. To ensure accurate and effective research findings, it is essential that you understand the cluster sampling formula along with the several types of cluster samples, including two-stage cluster sampling, simple random sampling, and stratified sampling. Cluster sampling has advantages such as reducing costs and time requirements, but it also has some limitations and disadvantages, such as the risk of clustering effects and possible bias in sample selection. By following a few basic steps, researchers can effectively use cluster sampling as a tool for reliable research in a variety of fields. To help you, Sample.net provides a useful template collection for survey research studies that you can easily use such as survey forms and sample survey templates.