Mapping the pubmed data under different suptopics using NLP.pptx
Cluster and multistage sampling
1. 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.
2. 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.
3. 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.
4. STEPS IN CLUSTER RANDOM
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify and define a logical cluster.
5. 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.
6. 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.
8. 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.)
9. 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.)
10. Multistage random sampling
Multistage sampling refers to sampling plans where the sampling
is carried out in stages
using smaller and smaller sampling units at each stage.
Not all Secondary Units Sampled normally used to overcome
problems associated with a geographically dispersed population
11. 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