In research, the sample is considered to be one of the crucial factors that determine the relevancy of your research design and accuracy of your results. Therefore, it is a must to choose a suitable sample for measurements. Generally, sampling is the most common technique used to choose relevant sample or a representative of a section or the whole population.
Sampling technique is nothing but a process of collecting about particular demographic without analysing the study. Although sampling technique yields significant results, the differences between the population and the sample may result in the sampling error. Therefore, it is important to choose a suitable sampling method.
Sampling techniques are of various types such as stratified, snowball, random sampling, etc. Each method has its own purpose. For example, stratified sampling can be used to reduce the sampling size to a given precision, cluster sampling that is widely used when the populations are unclear.
Cluster sampling is an approach where various groups within a population are utilised as a sample. In this technique, the whole population is segregated into well-defined clusters.
For instance, assume a researcher wants to perform research pertaining to Arul’s music event. The researcher desires to collect information from various interest groups of a college. The researcher found that 32% of the students are involved in academic clubs, 28% of students are involved in sports, 10% of students are involved in a music club and the rest in theatre club. Here, all the students are involved only in one club. The researcher concluded that Arul is a part of a music club and students from music club support Arul’s event.
Cluster sampling can be classified into single-stage, two-stage and multiple-stage sampling methods. The classification is done on the basis of the number of steps followed to obtain the cluster sample or representation of cluster in a whole group.
Single-stage cluster sampling - This type of technique is the most commonly used sampling method. Here, the process is conducted only once, and all the elements within the chosen sample are incorporated in the sample.
Example: An NGO wants a group of girls deprived of education across the neighbouring cities. Using single-stage cluster sampling, the NGO selected 5 cities (sample) and extended help to the girls in need.
Two-stage cluster sampling - As the name suggests, the sample is created by selecting a few members from each cluster via simple random or systematic sampling approach. The sample thus created in the two-stage approach provides improved results as they include only the filtered elements.
Example: A business wants to determine the performance of its branches spread across UK. Here, the business considers the number of offices, number of employees per office and the work done in each office. Employees belonging to different branches are formed as a cluster and then is divided into the size of the office. A two-level clustering is formed, and then a simple random technique is applied for further calculations.
Multiple-stage cluster sampling - For researches involving complicated clusters, it is suitable to use this form of sampling technique. In multi-stage sampling, the cluster of the population is divided into smaller clusters.
Example: If Nokia wants to perform a survey to analyse the performance of their smartphones across India, they can divide the total population into cities (cluster), and a city with the highest population can be selected for the survey.
Unlike other techniques, clustering sampling approach demands several requirements such as the cluster elements must be heterogeneous, the cluster must represent the entire population in small groups, and the cluster must be mutually exclusive. On fulfilling the requirements, this technique can be achieved through the following steps.
Determine the sample size - In the initial step, identify the target audience and choose the size of the sample.
Analyse sampling frames - Use the existing or develop a new sampling frame for the target audience. Assess the frames based on the clustering, coverage and modifications accordingly. The groups can be varied as per the population.
Identify groups - Include the average members in every group and determine the number of groups. Ensure that the groups are not similar to each other.
Choose clusters - In the final stage, choose the suitable clusters for random sampling.
Although it is ideal to randomly select the research participants to ensure all respondents are taken into account, and precise results are obtained, using clustering sampling technique has its own advantages.
Clustering sample is easy to implement, reduces variability, is feasible, and includes the least loss in data accuracy.