17. CLUSTER SAMPLING

Question 1

1. What is Cluster Sampling based on?

Answer 1

• it is based on a Hierarchical structure
• this is the structure of natural clusters and groups of
  individuals within the population

Question 2

2. Give some examples of natural clusters?

Answer 2

• hospitals
• schools
• streets
• city districts

Question 3

3. What sample do you take in Cluster Sampling?

Answer 3

• you take a random sample of these natural clusters
• you then select all the individuals in the selected
  clusters

Question 4

4. What is the Sampling frame in this scenario?

Answer 4

• it is a list of all the clusters

Question 5

5. What can you include into your sample if possible?

Answer 5

• you can include every individual from each sampled
  cluster

Question 6

6. What happens if the clusters themselves are large?

Answer 6

• you can sample individuals from within each cluster

Question 7

7. What are the 4 steps of the Cluster Sampling Procedure?

Answer 7

1. Identify the source population
2. Set up the Sampling Frame
   - this is comprised of clusters
3. Decide on the sample size
   - decide on the number of clusters and individuals
4. Randomly select clusters from the sampling frame

Question 8

8. What are 2 advantages of Cluster Sampling?

Answer 8

1. IT IS GOOD
   - for dealing with large and dispersed populations
2. IT IS LESS COSTLY
   - it is less time consuming

Question 9

9. What are 3 disadvantages of Cluster Sampling?

Answer 9

1. THERE ARE SUBSTANTIAL DIFFERENCES
   - between the clusters
   - this can cause errors
2. IT IS DIFFICULT TO GUARANTEE
   - that the sampled clusters really representative of the
   whole population
3. THE REPRESENTATIVENESS MAY BE COMPROMISED
   • if too few clusters are selected
   • if the clusters are too specific
   • if the clusters contain too few individuals