UGC NET Questions (Paper – 1)

Cluster sampling involves dividing the population into natural groups, or clusters, such as schools, villages or classes, and then randomly selecting some of these clusters for inclusion in the sample. All or many members within chosen clusters are then studied. This method is often used when a complete list of individuals is difficult to obtain but a list of groups is available. Since the stem describes selecting intact groups like schools as sampling units, cluster sampling is the correct term.

Option A:
Simple random sampling selects individual units from the entire population so that each has an equal chance of inclusion, without grouping them into clusters. It does not specifically use intact groups as the units of selection, so it does not match the situation in the stem.

Option B:
Stratified random sampling first divides the population into homogeneous strata based on characteristics such as gender or region and then draws random samples from each stratum. The primary aim is proportional representation of strata, not sampling intact groups as units. Therefore, stratified random sampling is not the best answer here.

Option C:
Cluster sampling is particularly useful when the population is geographically dispersed, because studying selected clusters reduces travel and administrative costs. However, it may increase sampling error compared to simple random sampling. The defining feature of using intact groups matches the description in the question.

Option D:
Quota sampling is a non-probability technique in which researchers fill predetermined quotas for subgroups using convenience procedures, not random selection of clusters. It does not require intact groups such as schools to be the sampling units, so quota is not appropriate.

Cluster sampling selects naturally occurring groups, or clusters, such as classrooms, villages or city blocks, rather than individual elements at the first stage. After clusters are randomly chosen, all or a sample of elements within each cluster are studied. This method is particularly useful when a complete list of individuals is unavailable but a list of clusters exists. Thus, the technique described in the stem is correctly called cluster sampling.

Option A:
Systematic sampling chooses every kth element from an ordered list after a random start and does not focus on intact groups such as villages. Its selection pattern is interval-based, not cluster-based. So it is not the right option.

Option B:
Stratified sampling divides the population into homogeneous strata and draws random samples from each stratum. It ensures representation of each subgroup but does not necessarily treat intact groups as primary units. Therefore, it does not match the stem.

Option C:
Quota sampling is a non-probability method in which the researcher fills predetermined numbers in specific categories using convenience selection. It is not a cluster-based probability technique. Hence, it cannot complete the blank.

Option D:
Option D, cluster, explicitly involves random selection of group units followed by study of members within those units. This is exactly the approach described in the question, making this option correct.