Cluster sampling is a method where researchers divide a population into clusters and then select a simple random sample of these clusters for analysis. This technique simplifies the research process, especially in cases like studying magazine readership across large geographic areas. However, it may introduce errors related to cluster definition and selection, which researchers can mitigate by using smaller clusters.

1. 
 It refers to a type of sampling method . With cluster
sampling, the researcher divides the population into
separate groups, called clusters. Then, a simple
random sample of clusters is selected from the
population. The researcher conducts his analysis on
data from the sampled clusters.
10/14/2022
Cluster Sampling
Muhammad Awais
(facebook.com/awwaiis)

2. 
10/14/2022
Shahabpura
Nawan
Pind,
Akbarabad
Cantt
Muhammad Awais
(facebook.com/awwaiis)

3. 
 For example, analyzing magazine readership habits
of people in Wisconsin would be time-consuming
and complicated if individual subjects were
randomly selected. With cluster sampling, the state
can be divided into districts, counties, or zip code
areas, and groups of people can be selected from
each area.
10/14/2022
Muhammad Awais
(facebook.com/awwaiis)

4. 
 Cluster sampling creates two types of errors: errors
in defining the initial clusters and errors in selecting
from the clusters.
 To help control such error, most researchers suggest
using small areas or clusters, both to decrease the
number of elements in each cluster and to maximize
the number of clusters selected.
10/14/2022
Muhammad Awais
(facebook.com/awwaiis)

5. 
10/14/2022
Muhammad Awais
(facebook.com/awwaiis)