Cluster sampling is described as a sampling strategy in which the researcher forms numerous clusters of individuals from a population, each exhibiting similar characteristics and having an equal likelihood of being included in the sample.
Imagine a situation where a data organization wants to assess the performance of smartphones throughout Germany. They can segment the entire population into cities (clusters), then choose towns with the highest populations and narrow down the selection to those using mobile devices for the survey.
Cluster Sampling
Cluster sampling is a probability-based sampling method where researchers divide a population into clusters for research purposes. Random clusters are then selected using techniques like simple random or systematic random sampling for data collection and analysis.
Cluster sampling involves analyzing a sample that includes multiple parameters like demographics, habits, background, or any other population attribute of interest to the research.
This method is typically used when the statistical population comprises groups that are similar but internally diverse. Instead of studying the entire population, cluster sampling enables researchers to gather data by dividing the population into smaller, more productive groups.
Example: Cluster Sampling
For example, consider a researcher who wants to assess the performance of sophomore business students across the United States. It’s impractical to study every student in every university. With cluster sampling, the researcher can group universities in each city into clusters, representing the sophomore student population.
Using simple or systematic random sampling, clusters are randomly chosen for the study. Finally, sophomores from these selected clusters are sampled for the research study using the same sampling techniques.
Types of Clusters
Cluster sampling can be classified based on the number of stages used to obtain the cluster sample and the representation of groups in the cluster analysis. Typically, cluster sampling involves multiple stages, with each stage representing a step toward obtaining the desired sample. The classification includes single-stage, two-stage, and multiple-stage cluster sampling methods.
1. Single-stage Cluster Sampling:
This method involves sampling only once.
Example: Single-stage Cluster Sampling:
For instance, an NGO aims to create a sample of girls across five neighboring towns for educational support. Using single-stage cluster sampling, the NGO randomly selects towns (clusters) to form a sample and offer assistance to girls in those towns who lack access to education.
2. Two-stage Cluster Sampling:
In this approach, instead of selecting all elements from a cluster, a few members are chosen from each group using systematic or simple random sampling.
Example: Two-stage Cluster Sampling
For example, a business owner wants to evaluate the performance of plants across various U.S. locations. They create clusters of plants and then select random samples from these clusters to conduct research.
Multiple-stage cluster sampling
Multiple-stage cluster sampling extends beyond two-stage sampling, creating more intricate clusters for research purposes. For conducting comprehensive research across diverse geographies, complex clusters are necessary, which can be achieved through multiple-stage sampling.
Example: Multiple-stage cluster sampling
Consider an organization aiming to analyze smartphone performance across Germany. They could divide the entire population into cities (clusters), selecting those with the highest populations.
Furthermore, they might filter these clusters to include only cities with significant mobile device usage for their survey.
How to Perform a Cluster Sampling?
Here are the steps to conduct cluster sampling:
1. Define Sample:
Determine the target audience and the required sample size.
2. Create and Assess Sampling Frames::
Develop a sampling frame either by using an existing framework or creating a new one tailored to the target audience.
Evaluate the frames based on coverage and clustering aspects, making necessary adjustments. Ensure that the groups in the sampling frame are diverse yet representative of the population.
3. Determine Group Sizes:
Decide on the number of groups and ensure that each group has a similar average size, making them distinct from each other.
4. Select Clusters:
Randomly select clusters from the sampling frame, ensuring unbiased representation.
5. Create Sub-types:
Based on the number of steps involved in forming clusters, cluster sampling can be categorized into two-stage and multi-stage sampling.
In two-stage sampling, additional steps are taken within selected clusters to obtain the final sample, while multi-stage sampling involves more complex cluster formations and sampling processes.
Uses for Cluster Sampling:
Cluster sampling finds applications in various fields:
1. Market Research:
In market research, cluster sampling based on geographic regions can be more cost-effective than surveying an entire broad location. Clusters divided by region allow for manageable sampling sizes while still providing representative data.
2. Statistical Applications:
Cluster sampling is extensively used in statistics when collecting data from an entire population is impractical or costly.
For example, in understanding smartphone usage in Germany, cities can form clusters for sampling, making it a practical and economical solution for statisticians.
3. Inferences in Special Situations:
Cluster sampling is also valuable in scenarios like wars or natural disasters, where collecting data from every individual is impossible.
By sampling clusters within affected areas, researchers can draw meaningful inferences about the population’s characteristics or needs.
Advantages of Cluster Sampling
Cluster sampling offers several advantages:
- Cost and Time Efficiency: It consumes less time and resources compared to sampling each unit individually across a region. This makes it highly economical and efficient in terms of work and cost.
- Convenient Access to Large Samples: Researchers can select large samples easily using cluster sampling, which enhances accessibility to various clusters within the population.
- Data Accuracy: With large samples in each cluster, the potential loss of data accuracy at the individual level can be mitigated, leading to more reliable information.
- Ease of Implementation: Implementing cluster sampling is straightforward, allowing researchers to gather information from diverse areas and groups efficiently, especially in practical situations.
- Characteristics Determination: Cluster sampling is useful in determining group characteristics, such as population demographics, without needing a sampling frame for the entire population, making it a versatile and practical sampling technique.
Disadvantages of Cluster Sampling
Cluster sampling has certain limitations:
- High Sampling Error: When clusters fail to accurately represent the population or lack characteristics that mirror the overall population, there can be a higher sampling error. This error becomes more pronounced with an increased number of clustering stages, reducing statistical certainty and accuracy.
- Lower Internal Validity: Compared to simple random sampling, cluster sampling may have lower internal validity, especially with more stages of clustering. This can weaken the reliability of results and the ability to generalize findings to the entire population.
- Complexity in Study Design: Planning study designs for cluster sampling can be complex. Researchers must carefully decide how to divide a larger population into clusters efficiently and accurately, which requires attention to detail and proper implementation to ensure representative sampling.
- Potential Bias: If clusters do not accurately represent the overall population, there’s a higher risk of bias in the sample. This can lead to skewed or inaccurate results, impacting the validity of the study.
In what situations is cluster sampling appropriate?
Cluster sampling is appropriate in several situations:
- Geographically Widespread Populations: When the population is spread across a large geographical area, making simple random sampling costly or impractical, cluster sampling can be used with geographically based clusters to minimize travel costs.
- Face-to-Face Data Collection: If data collection involves face-to-face interviews or on-site inspections, cluster sampling is suitable as it allows researchers to focus on specific clusters for efficient data gathering.
- Unavailable Individual Lists: When a comprehensive list of individuals in the population is not available, but it’s possible to identify clusters that represent the population, cluster sampling becomes a viable option.
- Naturally Divided Population: Cluster sampling is ideal for populations naturally divided into groups (clusters) that are internally diverse, reflecting the overall population’s diversity. It strikes a balance between statistical accuracy and cost-effectiveness in such cases.
Other articles
Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.
Citation Styles
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Comparision
- Basic and Applied Research
- Cross-Sectional vs Longitudinal Studies
- Survey vs Questionnaire
- Open Ended vs Closed Ended Questions
- Experimental and Non-Experimental Research
- Inductive vs Deductive Approach
- Null and Alternative Hypothesis
- Reliability vs Validity
- Population vs Sample
- Conceptual Framework and Theoretical Framework
- Bibliography and Reference
- Stratified vs Cluster Sampling
- Sampling Error vs Sampling Bias
- Internal Validity vs External Validity
- Full-Scale, Laboratory-Scale and Pilot-Scale Studies
- Plagiarism and Paraphrasing
- Research Methodology Vs. Research Method
- Mediator and Moderator
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- Parametric and Non-Parametric Test
Comparision
- Independent vs. Dependent Variable – MIM Learnovate
- Research Article and Research Paper
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- Clinical Research vs Lab Research
- Research Lab and Hospital Lab
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- Quantitative Researchers vs. Quantitative Traders
- Premise, Hypothesis and Supposition
- Survey Vs Experiment
- Hypothesis and Theory
- Independent vs. Dependent Variable
- APA vs. MLA
- Ghost Authorship vs. Gift Authorship
Research
- Research Methods
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- Case Study Research
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- Conclusive Research
- Descriptive Research
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- Theoretical Framework
- Conceptual Framework
- Triangulation
- Grounded Theory
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- Mixed Method
- Correlational Research
- Randomized Controlled Trial
- Stratified Sampling
- Ethnography
- Ghost Authorship
- Secondary Data Collection
- Primary Data Collection
- Ex-Post-Facto
Research
- Table of Contents
- Dissertation Topic
- Synopsis
- Thesis Statement
- Research Proposal
- Research Questions
- Research Problem
- Research Gap
- Types of Research Gaps
- Variables
- Operationalization of Variables
- Literature Review
- Research Hypothesis
- Questionnaire
- Abstract
- Validity
- Reliability
- Measurement of Scale
- Sampling Techniques
- Acknowledgements
Statistics
- PLS-SEM model
- Principal Components Analysis
- Multivariate Analysis
- Friedman Test
- Chi-Square Test (Χ²)
- T-test
- SPSS
- Effect Size
- Critical Values in Statistics
- Statistical Analysis
- Calculate the Sample Size for Randomized Controlled Trials
- Covariate in Statistics
- Avoid Common Mistakes in Statistics
- Standard Deviation
- Derivatives & Formulas
- Build a PLS-SEM model using AMOS
- Principal Components Analysis using SPSS
- Statistical Tools
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- One-tailed and Two-tailed Test
- Parametric and Non-Parametric Test