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.

Statements A, B, C, D and E accurately describe sampling techniques, whereas F is false. Statement A is true because probability sampling ensures each element has a known non-zero chance of selection. Statement B correctly defines stratified random sampling, and statement C is right in classifying purposive sampling as non-probability. Statement D is correct since snowball sampling is used to access hidden populations, and E is true as probability methods reduce researcher selection bias, while F is false because convenience sampling does not guarantee representativeness.

Option A:
Option A includes all the correct statements and excludes the incorrect one. By presenting A, B, C, D and E together, it reflects standard textbook definitions of sampling techniques and their implications. Since it leaves out F, which wrongly promises guaranteed representativeness, this option is the correct answer.

Option B:
Option B omits statement E, thereby ignoring the important point that probability sampling minimises researcher bias. Although A, B, C and D are true, excluding E makes the combination incomplete. A correct option must list all relevant true statements for the concept.

Option C:
Option C brings F into the combination along with the correct statements. Because F falsely claims that convenience sampling ensures full representativeness, including it contaminates the set of statements. Any option that mixes false and true statements cannot be accepted as correct.

Option D:
Option D also contains F and even omits A, which is central to the definition of probability sampling. Leaving out a key true statement and adding a false one makes this combination doubly problematic. Consequently this option is invalid.

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.

Statements A, B, C and E correctly describe common sampling methods, whereas D is false. Simple random sampling gives all units equal selection probability, cluster sampling selects intact groups, systematic sampling chooses every k-th unit after a random start and snowball sampling is indeed useful for reaching hidden populations through referrals. Purposive sampling, however, is a non-probability method because units are intentionally chosen based on specific characteristics rather than random procedures.

Option A:
Option A omits statement E, thereby ignoring snowball sampling as a valid non-probability technique for hard-to-reach groups. While A, B and C are correct, the absence of E makes the combination incomplete.

Option B:
Option B is correct because it includes all four accurate descriptions and excludes D, which misclassifies purposive sampling as probability-based. It reflects the standard classification of sampling methods in research methodology.

Option C:
Option C wrongly includes D, which incorrectly labels purposive sampling as a probability technique, and omits B, which is a correct description of cluster sampling. This mixture of omission and error makes the option flawed.

Option D:
Option D includes B, C and E but leaves out A, neglecting the fundamental simple random sampling method. Because it fails to mention all true statements from the list, it cannot be the correct answer.

Option E contains D, the incorrect statement about purposive sampling, and omits A, the definition of simple random sampling. As a result, it provides an inaccurate view of sampling methods.

Snowball sampling is a non-probability method used especially when the population is hard to identify or reach, such as hidden or stigmatised groups. In this technique, the researcher first accesses a few participants and then asks them to refer others they know who fit the criteria. The sample thus grows like a snowball rolling and accumulating more snow. Because the stem describes existing participants recruiting future participants from their acquaintances, it refers to snowball sampling.

Option A:
Quota sampling involves setting quotas for specific subgroups in the population and then selecting participants, often conveniently, until each quota is filled. It does not rely on participants recruiting one another. Therefore, quota sampling does not match the chain-referral process described in the stem.

Option B:
Convenience sampling selects participants who are easily available to the researcher, such as students in a particular class or people in a nearby location. It does not depend on referrals from current participants and lacks the network-based growth characteristic of snowball sampling. Hence, convenience is not the correct answer.

Option C:
Purposive sampling is based on the researcher’s judgment about which cases are most informative, not primarily on referrals from existing participants. Although snowball sampling may start with purposively chosen initial participants, its defining feature is the subsequent chain referral. Thus, purposive sampling is not what the stem is describing.

Option D:
Snowball sampling is particularly useful for accessing populations that lack complete sampling frames, as participants’ social networks become pathways into otherwise inaccessible communities. This network-based, participant-recruited expansion is exactly what the question outlines, confirming snowball as the appropriate completion.

Statements A, B, C and E describe recognised types and uses of non-probability sampling. Purposive sampling focuses on information-rich cases, convenience sampling uses easily available participants, quota sampling fills predetermined subgroup quotas without random selection and snowball sampling helps access hidden populations. Statement D is false because non-probability samples do not support precise calculation of sampling error. Hence the correct combination must include A, B, C and E and exclude D.

Option A:
Option A is incomplete because it omits E, overlooking the important role of snowball sampling for hard-to-reach groups such as marginalised communities or rare populations. Without E, the list of non-probability types is not fully representative.

Option B:
Option B is correct since it includes all true statements and excludes D, which wrongly promises precise sampling error estimates from non-probability designs. It reflects both the strengths and the limitations of such sampling methods.

Option C:
Option C contains B, C and E but omits A, so it ignores purposive sampling, which is a central non-probability technique in qualitative and exploratory research. Its absence makes the option incomplete.

Option D:
Option D includes A, C and E but excludes B, thereby disregarding convenience sampling, which is one of the most common non-probability methods in practice. Therefore this option cannot be the correct one.