Multi-stage sampling is a type of

1. Cluster sampling
2. Random sampling
3. Accidental
4. Innovative sampling

Correct Ans: (A)

Explanation:

Researchers use multi-stage sampling, a type of cluster sampling, to improve efficiency when studying large populations. Instead of selecting individuals directly, they divide the population into clusters and sample from those clusters in multiple stages.

This method makes data collection more practical and manageable, especially in large-scale research. Researchers follow multiple steps to ensure a representative and reliable sample.

How Multi-Stage Sampling Works:

1. First Stage – Cluster Selection: Researchers divide the population into clusters based on location, profession, or other criteria. For example, they may divide a country into regions when studying media consumption.
2. Second Stage – Sub-Cluster Sampling: They randomly select some clusters for further study. For instance, they may choose specific cities from each region.
3. Final Stage – Individual Selection: They randomly pick respondents from the chosen sub-clusters to ensure the sample represents the larger population.

Advantages of Multi-Stage Sampling:

• Reduces Costs: Researchers spend less time and resources compared to simple random sampling.
• Simplifies Data Collection: This method allows them to focus on specific groups without surveying an entire population.
• Ensures Representation: It helps them collect data from diverse sub-groups.

Additionally, multi-stage sampling differs from random sampling, where researchers select individuals directly, and accidental sampling, which lacks structure. This probability-based method improves research accuracy and reliability.

In conclusion, multi-stage helps researchers analyze large populations effectively by selecting samples in multiple steps.