Cluster Sampling

Cluster Sampling
Cluster sampling is a probability sampling technique where the population is divided into separate groups, called clusters, and a random selection of entire clusters is made. Instead of selecting individuals directly, the researcher selects whole clusters and then collects data from all individuals within the chosen clusters.
This method is useful when the population is large and geographically spread out, making it more cost-effective and practical than simple random sampling.

Types of Cluster Sampling
- Single-Stage Cluster Sampling: The researcher randomly selects entire clusters and collects data from all individuals within those clusters.
- Two-Stage Cluster Sampling: The researcher first randomly selects clusters, and then within those clusters, randomly selects individuals instead of surveying everyone.
- Multistage Cluster Sampling: This involves multiple stages of sampling, where clusters are selected at different levels.
- Stratified Cluster Sampling: The population is first divided into strata (subgroups), and then clusters are selected within each stratum to ensure better representation.
Examples
- Educational Research: A researcher studying students’ academic performance selects 10 schools randomly from a city and surveys all students from those schools instead of selecting students individually from different schools.
- Healthcare Studies: A health organization wants to study the eating habits of people in a country. Instead of selecting random individuals, they randomly choose certain cities (clusters) and survey all residents in those cities.
- Market Research: A company testing a new product selects 5 shopping malls in different regions and surveys every customer who visits those malls.
- Election Polling: To predict election results, a polling agency selects certain districts (clusters) randomly and interviews all voters in those districts instead of selecting individuals across the entire country.
- Employee Satisfaction Survey: A company with multiple branches wants to conduct an employee satisfaction survey. Instead of selecting employees randomly from all branches, they randomly pick a few branches and survey all employees in those selected branches.
Methods
Cluster sampling is used when a population is divided into naturally occurring groups (clusters). There are different methods of sample clustering based on how clusters are selected and how data is collected.
1. Single-Stage Cluster Sampling
- The researcher randomly selects entire clusters from the population.
- All individuals within the selected clusters are included in the sample.
- Simple and cost-effective
- Higher risk of bias if clusters are not representative
- Example: A researcher selects 5 schools randomly and surveys all students in those schools.
2. Two-Stage Cluster Sampling
- The researcher first randomly selects clusters from the population.
- Then, randomly selects individuals within each selected cluster instead of surveying everyone.
- Reduces sample size while maintaining randomness.
- More complex than single-stage sampling
- Example: A researcher selects 5 schools and then randomly picks 50 students from each school instead of surveying all students.
3. Multistage Cluster Sampling
- Involves multiple levels of sampling where clusters are selected at different stages.
- Each stage uses random sampling to improve accuracy.
- More precise and flexible.
- Requires more resources and time
- Example: Randomly select states.
4. Systematic Cluster Sampling
- Instead of selecting clusters randomly, clusters are selected using a systematic rule (e.g., every 5th cluster).
- Easy to implement.
- Can introduce bias if clusters have a pattern
- Example: A researcher wants to study university students, so they list all universities in a region and select every 3rd university from the list.
5. Stratified Cluster Sampling
- First, the population is divided into strata (subgroups) based on characteristics like age, gender, or location.
- Then, clusters are selected within each stratum to ensure better representation.
- Ensures better representation.
- More complex and requires prior knowledge of strata
- Example: If studying workplace satisfaction, companies are first divided into small, medium, and large businesses, and then clusters from each category are selected.
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