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Cluster_Sampling_Presentation_Professional.pptx
- 1. Cluster Sampling A DetailedPresentation Professional Edition
- 2. Introduction to ClusterSampling • Cluster Sampling is a probability sampling method where the population is divided into groups (clusters), and a random sample of these clusters is selected for study. – Each cluster should ideally represent the entire population, making it easier and more cost- effective to collect data.
- 3. Definition • Cluster samplinginvolves dividing a population into clusters, then randomly selecting some clusters, and collecting data from all units within the chosen clusters.
- 4. Steps in ClusterSampling – 1. Define the population clearly. – 2. Divide the population into clusters. – 3. Select clusters randomly using probability sampling. – 4. Collect data from all or a sample of units within selected clusters. – 5. Analyze and interpret the results.
- 5. Types of ClusterSampling – Single-Stage Cluster Sampling – All elements from selected clusters are studied. – Two-Stage Cluster Sampling – Random samples are drawn from within selected clusters. – Multistage Cluster Sampling – Combines multiple stages of sampling for large-scale studies.
- 6. Example of ClusterSampling • Suppose a researcher wants to survey students in a country. Instead of selecting individual students, schools (clusters) are randomly chosen, and all students in those schools are surveyed.
- 7. Advantages of ClusterSampling – Cost-effective for large and dispersed populations. – Simplifies data collection and management. – Suitable for geographically spread populations. – Requires fewer resources compared to simple random sampling.
- 8. Disadvantages of ClusterSampling – Less precise than simple random or stratified sampling. – High sampling error if clusters are not homogeneous. – Bias may occur if clusters differ significantly.
- 9. Cluster Sampling vsStratified Sampling – In cluster sampling, clusters are randomly selected; in stratified sampling, elements are chosen from every stratum. – Clusters are mini-populations; strata are homogeneous subgroups. – Cluster sampling reduces cost; stratified sampling increases accuracy.
- 10. Applications of ClusterSampling – Used in large-scale government surveys like census studies. – Applied in education and health surveys. – Common in marketing research to study consumer behavior in different regions.
- 11. Summary • Cluster Samplingis a cost-efficient and practical sampling method for large populations, especially when elements are naturally grouped. However, care must be taken to ensure clusters are representative to minimize bias.