Texas Forensic Science Commission Licensing Practice Exam

A researcher might prefer cluster sampling over simple random sampling primarily because it can be more cost-effective and efficient in certain scenarios. Cluster sampling involves dividing the population into groups or clusters and then randomly selecting entire clusters to study. This method can significantly reduce the time, cost, and logistical challenges associated with data collection, especially in geographically dispersed populations.

For instance, if a researcher is studying a large group of people spread across a wide area, it may be impractical to randomly select individuals from the entire population. Instead, the researcher could identify clusters—such as neighborhoods or schools—and then sample all individuals within selected clusters. This not only cuts down on travel and administrative costs but also allows for quicker data collection since the researcher can focus on fewer locations.

While cluster sampling can sometimes lead to less precise estimates compared to simple random sampling, the efficiency and potentially lower costs make it a preferred choice in many research contexts, particularly when dealing with larger populations.