UGC NET Questions (Paper – 1)

Snowball sampling starts with a small number of initial participants who meet the inclusion criteria and then asks them to refer others they know who also fit the criteria. This process continues, expanding the sample like a rolling snowball. It is especially useful for accessing hard-to-reach or hidden populations where no sampling frame exists. Thus, the referral-based method described in the stem is correctly called snowball sampling.

Option A:
Quota sampling sets numerical targets for categories like age or gender and then uses convenience methods to fill those quotas, without relying on participant referrals. It does not match the chain-referral strategy highlighted in the stem.

Option B:
Snowball sampling leverages social networks to identify additional participants, which can be efficient but may introduce bias because individuals tend to refer others similar to themselves. This characteristic aligns exactly with the description of recruiting subjects from acquaintances, so this option is correct.

Option C:
Stratified sampling is a probability method where the population is divided into strata and random samples are drawn from each, which requires a sampling frame rather than referrals.

Option D:
Systematic sampling selects every kth unit from an ordered list after a random start and does not depend on participants bringing in new members.