UGC NET Questions (Paper – 1)

A sampling plan outlines in detail how a sample will be drawn from the population, including allocations to strata or clusters and procedures for random selection. It ensures that the sampling process is systematic and follows the research design. By deciding in advance how many units will be taken from each subgroup, the researcher can achieve representativeness and precision. Thus, the schedule of allocations described in the stem is best termed a sampling plan.

Option A:
A sampling frame is the actual list or database of population elements from which the sample is selected, not the decision document specifying allocations and procedures. While essential, it is different from the plan that governs how many are chosen from each subgroup. Therefore, frame is not the correct answer.

Option B:
A sampling strategy indicates the overall approach, such as stratified, cluster or systematic sampling, but may not specify exact numbers for each stratum or cluster. The stem focuses on a concrete schedule of how many participants will be selected from each subgroup, which is more specific than a general strategy. Hence, strategy is not the best completion.

Option C:
A sampling plan details selection steps, allocation rules and sometimes replacement policies, translating the chosen strategy into actionable decisions. This aligns precisely with the stem’s description of specifying how many participants will be taken from each stratum or cluster.

Option D:
Sampling error refers to the difference between sample statistics and population parameters due to using a subset rather than the whole population. It is a type of error, not the pre-specified schedule for drawing the sample, so error is not appropriate here.

Stratified random sampling involves dividing the population into relatively homogeneous strata based on characteristics such as gender, region or discipline and then drawing random samples from each stratum. This ensures that important subgroups are adequately represented in the final sample. It can increase the precision of estimates, especially when subgroup differences are substantial. Because the stem mentions dividing the population into homogeneous subgroups and sampling from each, it describes stratified random sampling.

Option A:
Stratified random sampling uses random selection within each stratum, preserving probabilistic principles while controlling subgroup representation. This approach leads to a sample that mirrors the composition of the population with respect to chosen stratification variables. These features align exactly with the process outlined in the stem, making stratified random the correct answer.

Option B:
Simple random sampling selects units from the population without any prior subdivision, giving each element an equal chance of being chosen, but it does not guarantee representation of specific subgroups. Since the question specifies dividing the population into subgroups first, simple random is not the best match.

Option C:
Systematic sampling selects every kth element from an ordered list after a random start, without necessarily dividing the population into strata. Although it is a probability method, it does not follow the subgroup-based approach described in the stem. Therefore, systematic is not appropriate here.

Option D:
Cluster sampling divides the population into clusters, often based on natural groupings such as schools or villages, and then selects some clusters, usually sampling all or many elements within chosen clusters. Clusters are not necessarily homogeneous, and the method focuses on groups rather than ensuring representation from all subgroups. Thus, cluster sampling does not fit the description given.

Stratified sampling involves dividing the population into strata based on relevant characteristics such as age, gender or region, and then selecting samples from each stratum, usually by random methods. This approach ensures that key subgroups are adequately represented in the sample. It can increase precision and allow for subgroup comparisons. Hence, the technique described in the stem is correctly termed stratified sampling.

Option A:
Cluster sampling selects intact groups such as schools or villages as the initial units and then studies all or some members within them; it does not primarily aim at forming homogeneous strata for representation. Therefore, cluster is not the correct completion.

Option B:
Option B, stratified sampling, focuses on homogeneity within strata and heterogeneity between them in relation to the variable of interest. By drawing from every stratum, it guards against underrepresentation of smaller but important groups, which matches the description in the question.

Option C:
Systematic sampling chooses every kth unit from a list after a random start and does not inherently ensure representation of specific homogeneous subgroups. Thus, it is not the best answer here.

Option D:
Quota sampling is a non-probability method where quotas for categories are filled using convenience selection, so it lacks the random component that defines stratified probability sampling. It therefore does not fit the stem.

Stratified random sampling involves partitioning the population into strata that are internally homogeneous on some relevant characteristic, such as gender or region, and then drawing random samples from each stratum. This ensures that each subgroup is adequately represented in the overall sample. It can improve precision of estimates, especially when there are substantial differences between strata. Therefore, the method described in the stem is accurately called stratified random sampling.

Option A:
Cluster sampling selects intact groups or clusters, such as schools or villages, and then includes all or many members of selected clusters. It does not require that clusters be homogeneous internally on a characteristic used for stratification, so it differs from the technique in the stem.

Option B:
Systematic sampling chooses every kth element from an ordered list after a random start and does not involve dividing the population into homogeneous subgroups first. Thus, systematic sampling is not the correct completion.

Option C:
Convenience sampling selects cases that are easiest to access without randomisation or prior formation of strata, typically producing a non-probability sample. It does not guarantee representation of all subgroups, so convenience sampling is not suitable here.

Option D:
Stratified random sampling can use proportional or disproportionate allocation depending on research needs, but in all cases it explicitly draws random samples within each stratum. This defining feature corresponds perfectly to the description in the question.

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.

Stratified sampling divides the population into homogeneous subgroups or strata based on relevant characteristics such as gender, region or school type. Random samples are then drawn from each stratum. This approach increases precision and ensures that even small but important subgroups are properly represented, improving the quality of estimates.

Option A:
Option A wrongly states that stratification reduces coverage of subgroups, whereas its purpose is exactly to enhance subgroup coverage and representation.

Option B:
Option B is correct because it captures the main rationale: representing key strata in the sample in proportion or with specified emphasis. This helps in making subgroup comparisons and improves overall sample accuracy.

Option C:
Option C describes convenience sampling, which is nonprobability in nature and not related to stratification principles.

Option D:
Option D suggests avoiding random procedures, which contradicts the probabilistic nature of stratified sampling, where random selection operates within each stratum.

Statements A, B, C and E correctly distinguish cluster and stratified sampling. Cluster sampling first selects natural groups, which is economical when populations are spread out, while stratified sampling divides the population into homogeneous strata and can preserve the population structure through proportionate allocation. Statement D is false because stratified sampling usually selects units randomly within each stratum, not by convenience. Hence, the combination including A, B, C and E and excluding D is correct.

Option A:
Option A is correct as it recognises the economic advantage of cluster sampling and the representativeness aim of stratified sampling, while rejecting the idea that selection within strata is merely convenient.

Option B:
Option B is incomplete because it omits E, failing to mention how proportionate stratified sampling mirrors the population composition. Without E, the representational strength of the method is underplayed.

Option C:
Option C is wrong because it includes D, the incorrect claim that stratified sampling relies on convenience within strata, and omits A, the key point about sampling natural clusters. This mix of omission and error mischaracterises both techniques.

Option D:
Option D is incorrect as it again accepts D and excludes B and C, thereby ignoring correct statements about homogeneous strata and the economy of cluster sampling. This leads to a distorted understanding of these probability sampling methods.

Statements A and B correctly describe simple random and stratified sampling. Statement C is also true as cluster sampling initially chooses whole clusters for inclusion. Statement E is correct because systematic sampling indeed selects every nth unit after a random start. Statement D is false since convenience sampling is non-probability based and can easily introduce bias. Thus, A, B, C and E only are correct.

Option A:
Option A is incomplete as it omits E (systematic sampling).

Option B:
Option B is wrong because it includes D (false).

Option C:
Option C is correct because it unites all of the true descriptions and excludes D.

Option D:
Option D is incorrect since it retains D (false) and omits A.