Which of the following is a common issue in cluster sampling?

Cluster sampling is a technique that involves dividing a population into distinct groups, or clusters, and then randomly selecting entire clusters to represent the population. The correct answer highlights a crucial aspect of this sampling method: the potential for bias if the clusters are not chosen randomly.

If the selection of clusters is not random, the sample may not accurately reflect the characteristics of the entire population, leading to skewed results. For example, if only specific types of clusters (like those that are geographically closer or contain similar demographics) are chosen, this could significantly affect the validity and reliability of the research findings. Ensuring random selection of clusters helps mitigate the risk of bias and increases the likelihood that the sample will be representative of the broader population.

In contrast, the other options present statements that do not accurately capture the essence of cluster sampling. While cluster sampling does not guarantee representation like simple random sampling does, it can still be representative under the right conditions. It may not necessarily require extensive resources compared to other methods, especially considering that fewer individuals might need to be surveyed if entire clusters are selected. Finally, cluster sampling does allow for subgroup analysis, as researchers can analyze data from different clusters to draw conclusions about various subpopulations.