Cluster sampling (also known as one-stage cluster sampling) is a technique in which clusters of participants representing the population are identified and included in the sample. This is a popular method in conducting marketing researches.
The main aim of cluster sampling can be specified as cost reduction and increasing the levels of efficiency of sampling. This specific technique can also be applied in integration with multi-stage sampling.
A major difference between cluster and stratified sampling relates to the fact that in cluster sampling a cluster is perceived as a sampling unit, whereas in stratified sampling only specific elements of strata are accepted as sampling unit.
Accordingly, in cluster sampling a complete list of clusters represent the sampling frame. Then, a few clusters are chosen randomly as the source of primary data.
Cluster sampling process can be single stage or multistage. In single stage sampling, all members of selected clusters are included in the study, whereas in multistage sampling additional sampling methods are used to choose certain individuals within selected clusters.
Area or geographical sampling can be specified as the most popular version of cluster sampling. Specifically, a specific area can be divided into clusters and primary data can be collected from each cluster to represent the viewpoint of the whole area.
The pattern of cluster analysis depends on comparative size of separate clusters. If there are no major differences between sizes of clusters, then analysis can be facilitated by combining clusters. Alternatively, if there are vast differences in sizes of clusters, probability sampling proportionate to sample size can be applied to conduct the analysis.
Application of Cluster Sampling: an Example
Imagine you want to evaluate consumer spending on various modes of transportation in Greater London. Since Greater London is a large area, we need to sample from only 6 boroughs out of total 32 boroughs it comprises.
There are following five stages for the application of cluster sampling for this research:
1. Choosing target audience and sample size. The target audience for such a study is Greater London and sample size includes all the people living in Greater London.
2. Dividing population into clusters. Population in each cluster should be diverse and potential characteristics of the entire population should be represented in each cluster. Overlap between clusters should not exist, i.e. same people should not belong to more than one clusters. The Greater London consists of 32 boroughs. Each borough meets requirements above to be considered as a cluster. Accordingly, the area can be divided into 32 clusters with each cluster representing a borough.
3. Marking each cluster with a unique number. We can easily number each borough from 1 to 32.
4. Choosing a sample of clusters applying probability sampling. Usingsystematic random sampling (or any other probability sampling), we can choose 6 boroughs from the total 32 boroughs. It can be argued that these 6 boroughs can be considered as mini-representation of the entire Greater London. Households residing in 6 boroughs will represent samples for the study.
5. Choosing individual households to be included in the study. For this research we would be using multistage, rather than single stage cluster sampling. Accordingly, rather than using all households within selected 6 boroughs, we will choose certain households residing in these boroughs using probability sampling method such as systematic or stratified.
Advantages of Cluster Sampling
- It is the most time-efficient and cost-efficient probability design for large geographical areas
- This method is easy to be used from practicality point of view
- Larger sample size can be used due to increased level of accessibility of perspective sample group members
Disadvantages of Cluster Sampling
- Requires group-level information to be known
- Commonly has higher sampling error than othersampling techniques
- Cluster sampling may fail to reflect the diversity in the sampling frame
My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words.
 Jackson, S.L. (2011) “Research Methods and Statistics: A Critical Approach” 4th edition, Cengage Learning