What sampling strategy combines multiple sampling methods to achieve a representative sample?

The correct choice is multi-stage sampling, which involves combining several sampling techniques to create a representative sample. This strategy is particularly beneficial in large-scale surveys where it can be complex and impractical to conduct a complete random sampling across the entire population.

Multi-stage sampling typically begins by dividing the population into clusters, then selects some of these clusters through a random sampling method, and finally applies another sampling technique to gather data within those selected clusters. This approach allows for both a manageable sample size and a more comprehensive coverage of the population, improving the overall representativeness of the results. By using a combination of methods, multi-stage sampling effectively captures the diversity within the population while also streamlining the data collection process.

In contrast, cluster sampling involves selecting entire clusters as the primary unit of sampling without the additional layers of stratification found in multi-stage sampling. Convenience sampling relies on selecting individuals who are readily available, which can lead to bias and a lack of representativeness. Quota sampling seeks to ensure representation of specific characteristics but does not manage randomness, which can result in a sample that does not adequately reflect the overall population.