Slide 12- 1 Cluster and Multistage Sampling Sometimes stratifying isn’t practical and simple random sampling is difficult. Splitting the population into similar parts or clusters can make sampling more practical. Then we could select one or a few clusters at random and perform a census within each of them. This sampling design is called cluster sampling. If each cluster fairly represents the full population, cluster sampling will give us an unbiased sample.
Transcript
Slide 12- 1
Cluster and Multistage Sampling
Sometimes stratifying isn’t practical and simple random sampling is difficult.
Splitting the population into similar parts or clusters can make sampling more practical. Then we could select one or a few clusters at random and
perform a census within each of them. This sampling design is called cluster sampling. If each cluster fairly represents the full population, cluster
sampling will give us an unbiased sample.
Slide 12- 2
Cluster and Multistage Sampling (cont.)
Cluster sampling is not the same as stratified sampling. We stratify to ensure that our sample represents different
groups in the population, and sample randomly within each stratum.
Strata are homogeneous, but differ from one another. Clusters are more or less alike, each heterogeneous and
resembling the overall population. We select clusters to make sampling more practical or
affordable.
Slide 12- 3
Cluster and Multistage Sampling (cont.)
Sometimes we use a variety of sampling methods together.
Sampling schemes that combine several methods are called multistage samples.
Most surveys conducted by professional polling organizations use some combination of stratified and cluster sampling as well as simple random sampling.
STEPS IN CLUSTER RANDOM SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify and define a logical cluster.
STEPS IN CLUSTER RANDOM SAMPLING:
4. List all clusters (or obtain a list) that make up the population of clusters.
5. Estimate the average number of population members per cluster.
6. Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster.
STEPS IN CLUSTER RANDOM SAMPLING:
7. Randomly select the needed number of clusters by using a table of random numbers.
8. Include in your study all population members in each selected cluster.
Cluster sampling
Cluster 4
Cluster 5
Cluster 3
Cluster 2Cluster 1
Types: One stage – when all units in the selected cluster are selected. Two stage – only some units from a selected cluster are taken using simple
random or systematic random sampling.
Advantages Simple as complete list of sampling units within population not required Low cost Can estimate characteristics of both cluster and population Less travel/resources required
Disadvantages Potential problem is that cluster members are more likely to be alike, than
those in another cluster (homogenous). Each stage in cluster sampling introduces sampling error—the more
stages there are, the more error there tends to be Usually less expensive than SRS but not as accurate
Cluster sampling (contd.)
A special form of cluster sampling called the “30 X 7 cluster sampling”, has been recommended by the WHO for field studies in assessing vaccination coverage.
In this a list of all villages (clusters) for a given geographical area is made.
30 clusters are selected using Probability Proportional to Size (PPS).
From each of the selected clusters, 7 subjects are randomly chosen.
Thus a total sample of 30 x 7 = 210 subjects is chosen. The advantage of cluster sampling is that sampling frame is
not required
Cluster sampling (contd.)
Multistage random sampling
Multistage sampling refers to sampling plans where the sampling is carried out in stagesusing smaller and smaller sampling units at each stage.
Not all Secondary Units Sampled normally used to overcome problems associated with a geographically dispersed population
Multistage random sampling
In this method, the whole population is divided in first stage sampling units from which a random sample is selected.
The selected first stage is then subdivided into second stage units from which another sample is selected.
Third and fourth stage sampling is done in the same manner if necessary.
Example: NFHS data is collected by multistage sampling.
Rural areas – 2 stage sampling – Villages from list by PPS, Households from village
Urban areas – Wards (PPS) – CEB (PPS) – 30 households from each CEB
