Cluster Sampling: Definition, Examples
Summary
TLDRThis video explains cluster sampling, its requirements, and the distinction from stratified sampling. Cluster sampling involves dividing a population into natural groups or clusters and randomly selecting samples from each. It's particularly useful when direct access to the entire population is impractical. Key requirements include heterogeneity within clusters and mutual exclusivity between them. Unlike stratified sampling, which groups by characteristics, cluster sampling relies on natural groupings like geographic locations. The video clarifies the process and highlights its practicality over other sampling methods.
Takeaways
- 🌐 Cluster sampling is used when natural groups are present in a population.
- 🔄 The population is divided into clusters, and random samples are collected from each.
- 💼 It's often used in market research when complete population information is unavailable.
- 📊 Cluster elements should ideally be as heterogeneous as possible, containing distinct subpopulations.
- 🌟 Each cluster should be a small representation of the entire population.
- 🚫 Clusters should be mutually exclusive, meaning they should not overlap.
- 🔄 Cluster sampling is similar to stratified sampling but focuses on natural groupings rather than characteristics.
- 🗺️ An example of natural grouping is geographic location, like clusters in California, New York, and Florida.
- 🎰 Simple random sampling is used to choose one cluster for interviews from the natural groupings.
- 📝 Once a cluster is chosen, all members of that cluster are interviewed, and those in unchosen clusters are not.
Q & A
What is cluster sampling?
-Cluster sampling is a technique where the entire population is divided into natural groups called clusters, and then random samples are collected from each cluster.
When is cluster sampling used?
-Cluster sampling is used when natural groups are present in a population, and when it is more economical or practical than other sampling methods like stratified sampling or simple random sampling.
What are the requirements for cluster sampling?
-For cluster sampling, elements should be as heterogeneous as possible, each cluster should be a small representation of the entire population, and clusters should be mutually exclusive.
How is cluster sampling used in market research?
-In market research, cluster sampling is often used when a researcher cannot access information about the entire population but can get information about the clusters.
What is the difference between cluster sampling and stratified sampling?
-Cluster sampling relies on natural groupings like geographic location, while stratified sampling involves grouping by characteristics, such as color or age.
Can you provide an example of how cluster sampling might work?
-An example of cluster sampling could be selecting clusters of people from different states like California, New York, and Florida, and then using simple random sampling to choose one cluster for interviews.
What happens if simple random sampling picks a cluster like California?
-If simple random sampling picks California, all individuals within that California cluster would be interviewed, while those in other clusters would not be selected.
Why might cluster sampling be more practical than simple random sampling?
-Cluster sampling might be more practical because it can be easier and less costly to access and gather data from natural clusters rather than from individuals scattered throughout the entire population.
How does the size of each cluster affect the sampling process?
-The size of each cluster should ideally be small enough to represent the entire population but large enough to provide a meaningful sample. This balance is crucial for the accuracy of the sampling.
What does it mean for clusters to be mutually exclusive?
-Mutually exclusive clusters mean that no individual can belong to more than one cluster. This ensures that the sampling process does not overlap and that each member of the population is only counted once.
How does the heterogeneity of clusters affect the representativeness of the sample?
-Heterogeneity in clusters ensures that the sample is diverse and representative of the entire population. If clusters are too homogeneous, the sample may not accurately reflect the population's diversity.
Outlines
📊 Introduction to Cluster Sampling
This paragraph introduces the concept of cluster sampling, a technique used when natural groups are present in a population. It explains that the population is divided into clusters, and random samples are taken from each group. Cluster sampling is often more practical and economical than other sampling methods, especially when researchers can't access the entire population but can access clusters. It also outlines the requirements for cluster sampling: clusters should be as diverse as possible, each cluster should be a small representation of the entire population, and clusters should be mutually exclusive.
Mindmap
Keywords
💡Cluster Sampling
💡Natural Groups
💡Stratified Sampling
💡Heterogeneous
💡Mutually Exclusive
💡Market Research
💡Economical
💡Practical
💡Simple Random Sampling
💡Subpopulation
💡Geographic Location
Highlights
Cluster sampling is used when natural groups are present in a population.
The whole population is subdivided into clusters.
Random samples are collected from each group in cluster sampling.
Cluster sampling is often used in market research.
It's more economical or practical than stratified sampling or simple random sampling.
Cluster elements should be as heterogeneous as possible.
Each cluster should be a small representation of the entire population.
Clusters should be mutually exclusive.
Stratified sampling groups by characteristic, unlike cluster sampling.
In cluster sampling, natural groupings like geographic location are used.
Simple random sampling is used to choose one cluster for interviews.
All individuals in the chosen cluster are interviewed.
Individuals in clusters not chosen are not interviewed.
Cluster and stratified sampling are often confused due to their similarities.
The video aims to clarify the differences between cluster and stratified sampling.
The video provides a practical example of how cluster sampling works.
The presenter encourages viewers to subscribe for more informative videos.
Transcripts
in this video I'll show you cluster
sampling its requirements and the
difference between cluster and
stratified sampling cluster sampling is
used when natural groups are present in
a
population the whole population is
subdivided into clusters and random
samples are then collected from each
group you'll find this used in market
research when a researcher can't get
information about the population as a
whole however they can get information
about the Clusters and it's often more
economical or more practical than
stratified sampling or simple random
sampling there are a few
requirements cluster elements should be
as heterogeneous as possible in other
words the population should contain
distinct subpopulations of different
types each cluster should be a small
representation of the entire population
each cluster should be mutually
exclusive in other words it should be
impossible for each cluster to occur
together cluster sampling and stratified
sampling are very similar in fact
they're so similar they're often
confused but with stratified sampling
you're going to group by characteristic
for example I might decide to subdivide
my small population here by color with
cluster sampling I'm looking for a
natural grouping like geographic
location for example I might have
clusters of people in California New
York and Florida and I'm going to use
simple random sampling to choose one
cluster for interviews with simple
random sampling I'm going to assign a
number to the groups and then choose one
of those random numbers let's say my
simple random sampling picked California
I'm going to interview all three people
in that California cluster I'm not going
to interview anybody in the Clusters
that were not chosen with simple random
sampling I hope you found the video
helpful please take a moment to
subscribe and I'll see you in the next
video
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