Cluster sampling is a valuable and practical probability sampling method widely used in research when dealing with large, dispersed populations. It involves dividing the population into distinct groups, called clusters, then randomly selecting some of these clusters and collecting data from either all or a sample of individuals within the chosen clusters. This approach offers efficiency and cost-effectiveness while maintaining representative data collection, especially when accessing every individual in the entire population is difficult or impractical.
More precisely, Cluster sampling is defined as a sampling method where multiple clusters of people are created from a population where they are indicative of homogenous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population. This is a probability sampling procedure.
Types of Cluster sampling
There are three types as following,
Single stage Cluster: In this process sampling is applied in only one time. For example, An NGO wants to create a sample of girls across five neighboring towns to provide education. Using single-stage sampling, the NGO randomly selects towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.
Two-stage Cluster: In this process, first choose a cluster and then draw sample from the cluster using simple random sampling or other procedure. For example, A business owner wants to explore the performance of his/her plants that are spread across various parts of the U.S. The owner creates clusters of the plants. Then they selects random samples from these clusters to conduct research.
Multistage Cluster: Few step added to two-stage then it is called multistage cluster sampling. For example, An organization intends to survey to analyze the performance of smartphones across Germany. They can divide the entire country’s population into cities (clusters) and select cities with the highest population and also filter those using mobile devices.
Cluster Sampling Methodology
Conducting cluster sampling typically follows these steps:
- Divide the Population into Clusters: Researchers divide the total population into mutually exclusive and collectively exhaustive clusters. For example, dividing a national population into districts or neighborhoods.
- Randomly Select Clusters: A random subset of clusters is chosen. The number of clusters selected depends on the intended sample size, practical constraints, and desired accuracy.
- Sample Within Clusters:
- In single-stage cluster sampling, data is collected from every individual within the selected clusters.
- In two-stage sampling, a further sampling is done within the chosen clusters by randomly selecting individuals.
- Multi-stage cluster sampling involves additional levels of sampling within clusters—for example, randomly selecting schools within neighborhoods and then students within schools.
- Collect and Analyze Data: Data collected from the selected clusters or individuals within clusters is analyzed to make inferences about the larger population.
Sampling size and cluster sizes are carefully considered for statistical efficiency. Researchers may evaluate within-cluster similarity (intra-cluster correlation) to optimize how many clusters and individuals per cluster to sample.
Advantages of Cluster Sampling
Cluster sampling provides several benefits:
- Cost and Time Efficiency: Sampling entire clusters or a subset of elements within clusters reduces logistical and administrative costs, especially for large or widely dispersed populations.
- Practicality in Difficult Settings: It is easier to implement when the population is geographically scattered or when a complete list of individuals is unavailable.
- Representative Data: When clusters are chosen randomly and clusters are diverse internally, the sample can be a good representation of the entire population.
- Flexibility: Multi-stage cluster sampling allows adjustment based on available resources and precision requirements.
Disadvantages and Limitations
Cluster sampling also has potential drawbacks:
- Lower Statistical Efficiency: Sampling clusters rather than individuals usually results in higher sampling error compared to simple random sampling of individuals, due to similarities within clusters.
- Intra-Cluster Homogeneity: If individuals within clusters are too similar, the sample may not capture population variability, reducing generalizability.
- Complex Analysis: Data analysis requires specialized techniques to account for the design effect and clustering.
- Dependence on Good Clustering: If clusters do not accurately reflect the diversity of the population, the sample will be biased.
Applications of Cluster Sampling
Cluster sampling is commonly used in:
- Large-scale surveys: National health, economic, or educational surveys frequently use cluster sampling to efficiently cover vast or remote populations.
- Market research: When customers are distributed across regions or stores, cluster sampling can select representative locations.
- Environmental studies: Sampling natural clusters like forests or water bodies.
- Social sciences: Studies involving schools, communities, or households.
Cluster sampling vs stratified sampling
There are some dissimilarities between cluster and stratified sampling-
| Cluster sampling | Stratified sampling |
| Elements of a population are randomly selected to be a part of groups (clusters). | The researcher divides the entire population into even segments (strata). |
| Members from randomly selected clusters are a part of this sample. | Researchers consider individual components of the strata randomly to be a part of sampling units. |
| Researchers maintain homogeneity between clusters. | Researchers maintain homogeneity within the strata. |
| Researchers divide the clusters naturally. | The researchers or statisticians primarily decide the strata division. |
| The key objective is to minimize the cost involved and enhance competence. | The key objective is to conduct accurate sampling, along with a properly represented population. |
Conclusion
Cluster sampling is a powerful method for sampling large, dispersed, or logistically challenging populations. By dividing the population into clusters and randomly selecting some clusters for full or partial sampling, it balances cost, coverage, and representativeness effectively. While it may slightly reduce statistical efficiency compared to simple random sampling, its practicality and flexibility make it indispensable in many research fields. Proper design, including random cluster selection and attention to cluster heterogeneity, is essential for reliable results. Data Science Blog
Questions and Answers
Q1: What is the difference between one-stage and two-stage cluster sampling?
In one-stage cluster sampling, all individuals within the selected clusters are included in the sample, while two-stage cluster sampling involves randomly selecting individuals from within those clusters after the clusters themselves are chosen.
Q2: How are clusters formed in cluster sampling?
Clusters are formed by dividing the population into mutually exclusive and collectively exhaustive groups, usually based on geography, institutions, or natural groupings, aiming for clusters that internally reflect the population’s diversity.
Q3: When is cluster sampling most useful?
It is particularly useful when the population is large and spread out over a wide geographic area, making individual sampling costly or impractical.
Q4: What are the main disadvantages of cluster sampling?
It can suffer from increased sampling error due to similarities within clusters, potential biases if clusters are not representative, and requires complex data analysis methods.
Q5: How does cluster sampling differ from stratified sampling?
In stratified sampling, the population is divided into homogeneous strata, and samples are drawn from each to ensure representation. In cluster sampling, clusters are heterogeneous internally, and whole clusters or portions of clusters are sampled.