UGC NET Questions (Paper – 1)

Nonprobability sampling is often used when it is difficult or impossible to identify every element of the population or when the focus is on information rich cases rather than representativeness. For specialized topics, the researcher may deliberately select experts or key informants with deep knowledge.

Option A:
This option suggests that nonprobability sampling is used when a full frame and easy random selection are available, which is the opposite of recommended practice; in such circumstances, probability sampling is usually preferred.

Option B:
This option is correct because it points to situations with ill-defined populations and the need for expertise. Selecting knowledgeable individuals purposively is then more practical than attempting random sampling.

Option C:
Option C describes a goal of estimating population parameters with known sampling error, which is best served by probability sampling methods, not nonprobability ones.

Option D:
Option D refers to legal requirements for random procedures—again a context where probability sampling, not nonprobability sampling, is appropriate.

Snowball sampling begins with a small number of initial participants who meet the inclusion criteria, and these participants then refer others they know who also fit the criteria. The sample grows like a snowball rolling downhill, making this approach particularly useful for hard-to-reach or hidden populations. Because the selection process depends on networks rather than known probabilities, it is a non-probability technique. Thus, the chain referral procedure described in the stem is accurately called snowball sampling.

Option A:
Quota sampling involves filling predetermined numbers of units in specified categories, such as age or gender, using convenient procedures. It does not rely on participants recruiting other participants through personal networks. Therefore, quota sampling does not match the recruitment process outlined in the question.

Option B:
Stratified sampling is a probability method where the population is divided into homogeneous strata and random samples are drawn from each stratum. It is based on randomisation rather than on referrals from existing participants. Hence, stratified sampling is not the correct answer.

Option C:
Systematic sampling selects every kth element from an ordered list after a random start, which again does not involve social networks or referrals. It is a probability technique quite different from the non-probability chain approach described in the stem. Thus, systematic sampling is not appropriate here.

Option D:
Snowball sampling is especially valuable when no complete sampling frame is available and when trust within networks is needed for access. These characteristics align exactly with the description of participants recruiting acquaintances, confirming it as the right term.