UGC NET Questions (Paper – 1)

Statements A, B, C and E correctly describe typical qualitative sampling practices. Purposive sampling is widely used to select cases that yield rich information, and the goal is depth rather than statistical representativeness. Theoretical sampling helps refine emerging categories, and sample size is often guided by saturation, when new data no longer add substantially new insights. Statement D is false because probability sampling is not mandatory in qualitative research and is often not used.

Option A:
Option A is correct because it brings together the four true statements and excludes D, which misrepresents qualitative sampling requirements. It portrays qualitative sampling as flexible, concept-driven and oriented toward information richness rather than probability.

Option B:
Option B is incomplete because it omits E, ignoring how the concept of saturation influences decisions about when to stop collecting data. Without E, the dynamic nature of sample size determination in qualitative work is underplayed.

Option C:
Option C is incorrect because it includes D, the statement that probability sampling is mandatory, which contradicts common qualitative practice. Including D alongside true statements creates a misleading picture of qualitative methodology.

Option D:
Option D is wrong because it retains D and drops B, failing to acknowledge that depth of understanding, rather than representativeness, is the usual goal. This combination therefore distorts both the purpose and the technique of qualitative sampling.

Purposive sampling involves the deliberate selection of participants or cases that are especially knowledgeable or experienced regarding the phenomenon of interest. It is widely used in qualitative research where depth and insight are more important than representativeness. The researcher uses judgment to choose cases that can provide rich and relevant information. Thus, selecting information-rich cases matches the definition of purposive sampling.

Option A:
Simple random sampling gives each member of the population an equal probability of selection, with no emphasis on special knowledge or richness of information. It is aimed at representativeness rather than particular insight.

Option B:
Systematic sampling selects every kth element from an ordered list following a random start and does not focus on individuals’ information richness. Its purpose is efficient probability sampling, not purposive selection.

Option C:
Cluster sampling chooses intact groups such as schools or villages as primary units, often for logistical reasons. It can be random but does not inherently involve selecting units because they are especially informative about the phenomenon.

Option D:
Purposive sampling allows the researcher to focus on extreme cases, typical cases or key informants who can illuminate central issues deeply, matching the emphasis on information-rich cases in the stem and making this option correct.

Purposive sampling involves deliberately selecting participants or cases that are expected to yield the most relevant, detailed or insightful information for the research question. The researcher uses judgement based on theory, experience or expert knowledge to choose these cases. This method is common in qualitative studies where depth of understanding is more important than statistical representativeness. Hence, selecting information-rich cases for in-depth study is accurately termed purposive sampling.

Option A:
In purposive sampling, the researcher might choose key informants, typical cases or extreme cases depending on the study’s objectives. The emphasis is on relevance and richness of data rather than on probability-based selection. These characteristics correspond closely to the description in the stem.

Option B:
Random sampling gives each element of the population a known, usually equal, chance of selection and is used primarily in quantitative research for statistical generalisation. It does not rely on the researcher’s judgement about which cases are information-rich. Therefore, random sampling is not the correct completion.

Option C:
Quota sampling involves filling predetermined numbers of participants in specified categories, such as age or gender, often using convenience procedures. Although it may achieve some diversity, it is not focused primarily on depth or richness of information, so quota sampling does not match the stem.

Option D:
Systematic sampling selects every kth unit from an ordered list after a random start, and is a probability sampling method. It is driven by a mechanical rule rather than by the researcher’s judgement about information richness, making it inappropriate as an answer here.

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.

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.

Purposive sampling, also called judgmental sampling, involves selecting units based on the researcher’s judgment about which cases will be most informative or relevant to the research question. It is commonly used in qualitative studies where depth of understanding is more important than statistical representativeness. The researcher may choose typical cases, extreme cases or key informants. Because the stem describes selecting information-rich cases for in-depth study, it clearly refers to purposive sampling.

Option A:
Simple random sampling gives every element of the population an equal chance of selection using random methods such as tables or generators. It is not based on the researcher’s judgment about which cases are most informative. Therefore, simple random does not fit the description in the stem.

Option B:
Convenience sampling selects units that are easiest to access, such as friends, students in a class or people in a nearby location, regardless of their information richness. Although convenient, it can lead to biased samples and is conceptually different from purposive selection of particularly informative cases. Hence, convenience is not the correct answer.

Option C:
Purposive sampling is guided by theoretical and practical considerations concerning which cases can shed the most light on the phenomenon, and it is often combined with qualitative methods like interviews or observations. This targeted selection aligns exactly with the idea of information-rich cases highlighted in the stem, making purposive the proper completion.

Option D:
Snowball sampling involves asking initial participants to refer others in their networks, which is useful for hard-to-reach populations. While it can also lead to information-rich participants, the defining feature is chain referral, not direct judgmental selection by the researcher. Thus, snowball sampling is not the best label for the situation described.

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.