In most cases survey research involves covering a wide geographic bound and/or a large number of population study. And due to the high cost and considerable time of coverage provision is made for the use of samples in the study. To guide appropriate sample selection, a good understanding of the principles of sampling techniques and sampling procedures becomes crucial. Basically there are two methods of sampling, which are the probability and non-probability methods. The probability method has four sampling techniques - simple random, systematic, stratified and cluster. The non-probability method numerous sampling techniques, among which are purposive, quota, snowballing, convenient etc. The emerging questions are: What are these sampling methods or techniques designed for? To sample study population or study location? A careful study of these sampling techniques and procedures reveals that they are designed MAINLY FOR SAMPLING OF STUDY POPULATION or the set of people to be studied. THE SAMPLING TECHNIQUES ARE NEVER DESIGNED FOR SAMPLING OF STUDY LOCATION, NO MATTER HOW LARGE THE GEOGRAPHIC SPREAD OR BOUND IS, there is NO PROVISION FOR SAMPLING AREAS AS STUDY LOCATION By default, an area or a social system becomes a study location by virtue of the existence of the social phenomenon to be studied in the area. Where it is invariably impossible to cover the entire geographic bound, it is not the locations (such as communities, counties, local government areas, or states) to be sampled but the people that make up the study population across the locations. CLUSTER SAMPLING TECHNIQUE provides the right procedure for this. If you are to conduct a survey study on post traumatic effects of COVID-19 in Nigeria or the US, how will handle the geographic spread of the study? Let's have your take! hashtag#SocialScienceResearchHints hashtag#Survey hashtag#Studylocation hashtag#SamplingTechniques hashtag#SamplingProcedures hashtag#ClusterSamplingTechnique
Hello Ammar.
Good to receive your feedback. In response to your question, the condition under which cluster sampling becomes essential are:
1. Where a survey research involves covering a large geographic area, say, a country, state, regions etc or due to enormity of the geographic bound and/or spread of the study population, which invariably cannot be covered by researchers.
2. There must be a complete list of the study population to be sampled. This so, because the cluster sampling technique is a a method of probability sampling, and the first condition to be fulfilled for the use of any of the probability sampling techniques is availability of comprehensive list or total number of the study population to be surveyed.
3. Existence of clustering features. That is there be a means to group the study population across the geographic bound. e.g. counties or communities could be used as clusters to group a study population across a country or state
You may also go through this link: https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/methods-of-sampling-population
Okanlade Lawal-Adebowale -
I am not completely certain as to what you are asking, but perhaps you might have an interest in small area estimation (SAE), or small area prediction, i.e. design-based or model-based. Design-based cluster sampling can help you sample for a broad area when travel is a problem. It is less efficient (meaning variance is relatively high for a given sample size) but can be more convenient and less expensive. But SAE may help give results for smaller geographic regions possibly by employing a model-assisted or a model-based approach. Borrowing strength from neighboring geographic regions may be helpful. J.N.K. Rao and others have done some sophisticated work in this area (SAE). (You can find some more elementary comments on this in a conference paper I wrote in 2022:
https://www.researchgate.net/publication/362370770_Application_of_Efficient_Sampling_with_Prediction_for_Skewed_Data, which references an article by Guadarrama, Molina, and Tillé.)
A big problem with design-based (probability-of-selection-based) sampling is nonresponse. One often uses modeling to handle nonresponse and at some point you may as well use a model-based (prediction-based) procedure overall. You might be interested in knowing that the new chair of the Survey Research Methods Section of the American Statistical Association (ASA), who I believe is also an Assistant Chief Statistician of Canada, has expressed desire to find how to move forward with survey sampling in the future. I commented on two short articles he wrote for Amstat News, a magazine of the ASA:
January 2025:
Survey Research Methods Section Looks Ahead with Sights on Nonresponse | Amstat News:
https://magazine.amstat.org/blog/2025/01/02/survey-research-methods-section/
February 2025:
Surveys Here to Stay | Amstat News:
https://magazine.amstat.org/blog/2025/02/03/srms-update-feb-25/
Best wishes - Jim Knaub
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