A Comprehensive Analysis of Cluster Sampling versus Multi-Stage Sampling Techniques: Methodologies, Applications, and Comparative Insights

Abstract

Sampling methods play an important role in research efforts, enabling the selection of representative samples from a population for better research. In this comprehensive review, we examine the methods, advantages, disadvantages, applications, and comparative methods of cluster sampling and multistage sampling. Researchers are provided valuable insights to make appropriate decisions tailored to their research objectives. Cluster sampling consists of dividing a population into dissimilar yet externally comparable clusters, whereas multistage sampling further divides these groups into smaller ones in several ways, allowing for the examination of population structures. We explore the advantages, limitations, and usefulness of these approaches in a variety of fields such as market research, public health, social sciences, environmental studies, and agriculture. From measuring consumer preferences to analyzing disease prevalence, both cluster sampling and multi-stage sampling provide researchers with valuable tools for efficiently collecting data and drawing meaningful conclusions. Drawing from a healthcare facilities dataset in Canada, we propose the application of both techniques and advocate for the utilization of multi-stage sampling because of its ability to examine hierarchical structures that are well embedded in the dataset. Using the Open Database of Health Facilities (ODHF), we show how provinces, cities, and healthcare facilities can be represented hierarchically in multi-stage sampling, providing insight into healthcare facility characteristics , while taking a closer look at hierarchical structures. By thoroughly examining these sampling methods,  and applying them to a real-world dataset, we aim to contribute to the advancement of sampling techniques in research practices, ultimately enhancing the reliability and validity of research findings.