Whether you are planning to complete an academic research project or going through the evaluation phase of research, the investigation must proceed with data collection. Gathering data for a research is a critical step as data determine the authenticity of an evaluation. Once you define the research questions, the next step must be to choose the right sampling as well as data collection techniques. Sampling is essential to reach the target respondents that seem ideal for a project. From a long list of available sampling techniques, cluster and purposive sampling are two important ones that we aim to discuss in the upcoming sections of this article. By knowing the importance of purposive and cluster sampling along with the differences between them, you can better select the sampling technique for your dissertation or academic research. Let’s get started with a brief introduction of both types of sampling. Sampling: Definition and importance

In this type of sampling, researchers divide a large population into smaller units or subunits called clusters. Later on, they randomly select a sample from these clusters to conduct an experiment or arrange interviews, depending on the data collection method selected in a research proposal. Till now, we know that cluster sampling is an act of collecting data followed by cluster formulation from a large population. Thereby, it reflects the type of probability sampling that simply means a random collection of samples from a population. 

After reading the definition, you may wonder ‘how these clusters help you in conducting a generalised study. If you wonder about it, congratulation, you understand what I want you to go through. Clustering is important is allows you to conduct a large sample of studies by spending as less time or effort as possible. Additionally, when it comes to breaking the geographical limitations in a scientific study, cluster analysis also makes its way there as well. These clusters help researchers to invite widely geographically dispersed participants to voluntarily play their role in a study.  

Purposive Sampling: Definition and importance

Unlike cluster sampling, purposive sampling, as the name suggests, is the sampling that is categorised under the non-probability sampling method.   It simply means purposive sampling does not offer an equal chance to every member of a large population to play their part in a study. Rather, it allows researchers to set some criteria (under the guidance of the pre-defined research goals) to subjectively select samples for a study. To put it simply, it is the method of selecting participants or respondents, no matter whether living or non-living, by taking into account certain characteristics such as age, gender, or any other thing.

This method of sampling has its own place as it helps researchers to squeeze as much useful information as possible. Thereby, if a researcher has to complete research in limited time or resources, purposive sampling can serve the aim. This is because; it does not need you to reach a large number of participants; rather, the research’s objectives can easily be gained by involving a few but best-fit participants in a study. 

Selecting an appropriate sampling technique for your dissertation or PhD research is an easy process. You must consider a few things while deciding on the best one, like the subjectivity of your research and time, resources, and facilities available to you. Still, if you feel confused in any point in this decision-making process, you can quickly resolve any problem by seeking PhD dissertation help from experts in your own field. 

Difference between purposive and cluster sampling:

The foremost difference between the two is they both belong to two different methods of sampling. Cluster sampling is a type of probability sampling, while purposive belongs to non-probability. Concurrently, making a cluster or small units is the working principle of cluster sampling, but purposive sampling subjectively considers the characteristics of participants that seem ideal to be involved in a study. Additionally, the chances of manipulation of data by participants are relatively higher in purposive sampling than in cluster sampling. All in all, purposive sampling allows researchers to work on small samples to get more information, while cluster sampling is the method of involving a large number of participants to fulfil the generalisation criteria.

Final Thoughts:

In the big picture, no sampling method can be preferred over others without considering the aims and objectives of the research. Basically, sampling aims to assist researchers in the data collection process. However, sampling methods aim to meet the varying needs of different types of research objectives. In the end, we must say if you are dam clear about your research goals, you can definitely select the best methods of sample for your dissertation with great ease.