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Difference Between Stratified and Cluster Sampling

stratified vs cluster samplingIn our earlier article, we’ve discussed probability and non-probability sampling, in which we came across types of probability sampling, i.e. Stratified Sampling and Cluster Sampling. In stratified sampling technique, the sample is created out of the random selection of elements from all the strata while in the cluster sampling, all the units of the randomly selected clusters form a sample.

In stratified sampling, a two-step process is followed to divide the population into subgroups or strata. As opposed, in cluster sampling initially a partition of study objects is made into mutually exclusive and collectively exhaustive subgroups, known as a cluster. thereafter a random sample of the cluster is chosen, based on simple random sampling.

In this article excerpt, you can find all the differences between stratified and cluster sampling, so take a read.

Content: Stratified Sampling Vs Cluster Sampling

  1. Comparison Chart
  2. Definition
  3. Key Differences
  4. Video
  5. Conclusion

Comparison Chart

Basis for ComparisonStratified SamplingCluster Sampling
MeaningStratified sampling is one, in which the population is divided into homogeneous segments, and then the sample is randomly taken from the segments.Cluster sampling refers to a sampling method wherein the members of the population are selected at random, from naturally occurring groups called 'cluster'.
SampleRandomly selected individuals are taken from all the strata.All the individuals are taken from randomly selected clusters.
Selection of population elementsIndividuallyCollectively
HomogeneityWithin groupBetween groups
HeterogeneityBetween groupsWithin group
BifurcationImposed by the researcherNaturally occurring groups
ObjectiveTo increase precision and representation.To reduce cost and improve efficiency.

Definition of Stratified Sampling

Stratified sampling is a type of probability sampling, in which first of all the population is bifurcated into various mutually exclusive, homogeneous subgroups (strata), after that, a subject is selected randomly from each group (stratum), which are then combined to form a single sample. A stratum is nothing but a homogeneous subset of the population, and when all the stratum are taken together, it is known as strata.

The common factors in which the population is separated are age, gender, income, race, religion, etc. An important point to remember is that strata should be collectively exhaustive so that no individual is left out and also non-overlapping because overlapping stratum may result in the increase in the selection chances of some population elements. The sub-types of stratified sampling are:

  • Proportionate Stratified Sampling
  • Disproportionate Stratified Sampling

Definition of Cluster Sampling

Cluster sampling is defined as a sampling technique in which the population is divided into already existing groupings (clusters), and then a sample of the cluster is selected randomly from the population. The term cluster refers to a natural, but heterogeneous, intact grouping of the members of the population.

The most common variables used in the clustering population are the geographical area, buildings, school, etc. Heterogeneity of the cluster is an important feature of an ideal cluster sample design. The types of cluster sampling are given below:

  • Single-stage cluster sampling
  • Two-stage cluster sampling
  • Multistage cluster sampling

cluster sampling

Key Differences Between Stratified and Cluster Sampling

The differences between stratified and cluster sampling can be drawn clearly on the following grounds:

  1. A probability sampling procedure in which the population is separated into different homogeneous segments called ‘strata’, and then the sample is chosen from the each stratum randomly, is called Stratified Sampling. Cluster Sampling is a sampling technique in which the units of the population are randomly selected from already existing groups called ‘cluster.’
  2. In stratified sampling the individuals are randomly selected from all the strata, to constitute the sample. On the other hand cluster sampling, the sample is formed when all the individuals are taken from randomly selected clusters.
  3. In cluster sampling, population elements are selected in aggregates, however, in the case of stratified sampling the population elements are selected individually from each stratum.
  4. In stratified sampling, there is homogeneity within the group, whereas in the case of cluster sampling the homogeneity is found between groups.
  5. Heterogeneity occurs between groups in stratified sampling. On the contrary, the members of the group are heterogeneous in cluster sampling.
  6. When the sampling method adopted by the researcher is stratified, then the categories are imposed by him. In contrast, the categories are already existing groups in cluster sampling.
  7. Stratified sampling aims at improving precision and representation. Unlike cluster sampling whose objective is to improve cost effectiveness and operational efficiency.

Video: Stratified Vs Cluster Sampling

Conclusion

To end up the discussion, we can say that a preferable situation for stratified sampling is when the identicalness within an individual stratum and the strata mean to vary from each other. On the other hand, the standard situation for cluster sampling is when the diversity within clusters and the cluster should not vary from each other.

Further, sampling errors can be reduced in stratified sampling if between-group differences among strata are increased, whereas the between-group differences among clusters should be minimised to reduce sampling errors in cluster sampling.

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Comments

  1. Maryam Baig says

    January 20, 2017 at 11:57 am

    how to cite this page ?

    Reply
    • Surbhi S says

      January 20, 2017 at 12:19 pm

      “Difference Between Stratified and Cluster Sampling” Keydifferences.com, 20 Jan 2017. <http://keydifferences.com/difference-between-stratified-and-cluster-sampling.html>

      Reply
  2. sm says

    March 1, 2017 at 9:46 pm

    This is extremely helpful and clear – please publish in text books. Thank you for this resource.

    Reply
    • Surbhi S says

      March 2, 2017 at 10:35 am

      Thanks a lot for sharing your views.. This means a lot 🙂

      Reply
  3. Tina says

    September 30, 2017 at 5:37 am

    This is amazing. You’re amazing. THANK YOU

    Reply
  4. El Jay says

    October 27, 2017 at 9:11 am

    This article helped me a lot. You are amazing

    Reply
  5. Mohamed Elfatih says

    November 7, 2017 at 3:57 pm

    What can I say?
    THANK YOU VERY MUCH

    Reply
  6. Saba says

    January 2, 2018 at 7:24 pm

    Extremely Helpful. I was so much confused between these two, None of the books or webpages cleared the confusion. This is the only text that I needed. Thankyou so much. Please keep it up we need it 🙂

    Reply
  7. Hina says

    February 12, 2018 at 1:56 am

    Thank you so much

    Reply
  8. justichelle says

    March 1, 2018 at 4:19 am

    So helpful

    Reply
  9. Jessica says

    July 3, 2018 at 6:50 am

    Wonderful and concise explanation. Very much appreciated. Cheers.

    Reply
  10. Guojun says

    August 31, 2018 at 6:05 pm

    Thank you very much!

    Reply
  11. Sol the man says

    February 5, 2019 at 3:18 pm

    Explained in simple terms specifically for non-statisticians.

    Reply
  12. Hatem Kotb says

    March 21, 2019 at 7:58 pm

    This was helpful, thanks!

    Reply
  13. Manav says

    May 12, 2019 at 9:37 pm

    Your site is great! All the answers and so easily understandable.

    Reply
  14. ANANIAS SHIRIPINDA says

    May 25, 2019 at 4:55 am

    very good notes, easy to understand and there are precise

    Reply
  15. nyshant says

    August 10, 2019 at 12:31 pm

    Clear and concise. exactly what I needed, especially last-minute of reading in exams. Thanks

    Reply
  16. sha says

    January 23, 2020 at 10:59 am

    Extremely helpful, clean and easy to understand. thanks

    Reply
  17. Upendo Nzaligo says

    December 22, 2020 at 5:01 pm

    Tried a lot to catch mind of every one, simple language and clear explanation. Congrats

    Reply
  18. Ismail says

    June 8, 2023 at 4:42 pm

    Throughout my years as Statistics, I never had such a simply explicit and clear explanation like this.

    Thanks a lot!

    Reply

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