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.

A sampling frame is a practical list or representation of all units in the population that can potentially be selected for the sample, such as a roster of students or a registry of schools. It operationalises the population for sampling purposes. The quality of the sampling frame strongly affects the representativeness of the sample. Because the stem describes a complete list of sampling units from which the sample is drawn, sampling frame is the correct term.

Option A:
Population denotes the entire set of individuals or units to which the researcher wishes to generalise findings, but it may exist conceptually without a concrete list. A sampling frame is a specific listing used to select sample elements. Therefore, population does not precisely match the stem’s description.

Option B:
A sample is the subset of units actually selected from the sampling frame for study, not the list from which they are chosen. The stem focuses on the list itself, so sample is not the right completion.

Option C:
Sampling frames are crucial for probability sampling, as they determine whether every population element has a known chance of selection. Incomplete or biased frames can lead to coverage error. This role corresponds exactly to the description in the question, confirming sampling frame as the appropriate answer.

Option D:
Cluster refers to naturally occurring groupings within the population, such as schools or villages, which may be sampled in cluster sampling designs. It is not the same as the comprehensive list of units used in selection. Hence, cluster is not the correct term here.

Quota sampling involves specifying how many respondents are needed from particular categories of a population, such as a fixed number of males and females or urban and rural residents, and then selecting conveniently available individuals until each quota is filled. It ensures some representation of key groups but does not use random selection. Because selection within categories is left to interviewer judgment or convenience, it is considered a non-probability method. Therefore, the technique described is correctly termed quota sampling.

Option A:
Quota sampling can approximate the distribution of important characteristics in the population, but results may still be biased because individuals within quotas are not randomly chosen. Interviewers might select more cooperative or accessible respondents. These features correspond to the stem’s focus on filling predetermined category numbers, making this option correct.

Option B:
Cluster sampling selects intact groups such as schools or villages, usually with random methods, rather than filling category counts with conveniently chosen individuals.

Option C:
Simple random sampling gives every individual an equal chance of selection by random procedures and does not involve non-random filling of predetermined quotas.

Option D:
Purposive sampling selects information-rich cases based on researcher judgment rather than meeting numerical targets for categories, so it is conceptually different from quota methods.

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.

Cluster sampling selects naturally occurring groups, or clusters, such as classrooms, villages or city blocks, rather than individual elements at the first stage. After clusters are randomly chosen, all or a sample of elements within each cluster are studied. This method is particularly useful when a complete list of individuals is unavailable but a list of clusters exists. Thus, the technique described in the stem is correctly called cluster sampling.

Option A:
Systematic sampling chooses every kth element from an ordered list after a random start and does not focus on intact groups such as villages. Its selection pattern is interval-based, not cluster-based. So it is not the right option.

Option B:
Stratified sampling divides the population into homogeneous strata and draws random samples from each stratum. It ensures representation of each subgroup but does not necessarily treat intact groups as primary units. Therefore, it does not match the stem.

Option C:
Quota sampling is a non-probability method in which the researcher fills predetermined numbers in specific categories using convenience selection. It is not a cluster-based probability technique. Hence, it cannot complete the blank.

Option D:
Option D, cluster, explicitly involves random selection of group units followed by study of members within those units. This is exactly the approach described in the question, making this option correct.

A sampling frame operationalizes the target population by listing units that can potentially be included in the sample. Examples include voter lists, school registers or membership databases. The quality of the frame affects representativeness because omissions or duplications can bias selection. Thus, it is the practical source list from which sample units are chosen.

Option A:
Option A defines the population, which is the broader group of interest, not necessarily the actual list used for sampling.

Option B:
Option B is correct as it highlights the frame as the list or device from which sampling occurs. It connects the conceptual population to the operational step of sample selection.

Option C:
Option C refers to the achieved sample or respondents, which may be smaller than the selected sample due to nonresponse. This is not the sampling frame.

Option D:
Option D describes the questionnaire, which is a data collection tool, not the list of units.

Systematic sampling begins by choosing a random starting point on an ordered list of population elements and then selecting every kth element thereafter, where k is the sampling interval. This procedure is simple to implement and can approximate random sampling if the order of the list is not correlated with the variables of interest. It also ensures a spread of the sample across the entire frame. Therefore, the method described in the stem is correctly called systematic sampling.

Option A:
Option A, systematic sampling, relies on a fixed numeric step to move through the sampling frame, making selection predictable once the start and interval are set but still probabilistic. Its efficiency and simplicity match the description of selecting every kth element.

Option B:
Convenience sampling is a non-probability method where respondents are chosen because they are easy to reach, without using a fixed interval or random starting point. Thus, it is not appropriate here.

Option C:
Cluster sampling selects intact groups or clusters, not individual elements at regular intervals from a list. It is based on groupings rather than on a numeric step, so it does not fit the stem.

Option D:
Purposive sampling is a judgment-based, non-probability method targeting specific information-rich cases and does not involve fixed intervals or an ordered frame. Hence, it is not the correct completion.

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.

Non-probability sampling refers to techniques in which elements of the population are selected using non-random procedures such as convenience, purposive or quota sampling. In these methods, the exact probability of any given element being included in the sample is unknown. This limits the ability to generalise findings using statistical inference. Because the stem explicitly states that selection probabilities are unknown, non-probability sampling is the correct answer.

Option A:
Probability sampling, by contrast, ensures that each element of the population has a known, non-zero chance of selection, often through random processes. This property enables estimation of sampling error and supports strong generalisation to the population. Since the question mentions unknown probabilities, probability sampling cannot complete the stem correctly.

Option B:
Non-probability sampling is often used in exploratory or qualitative studies, or when constructing sampling frames is difficult or impossible. Although it may introduce bias, it can be practical and informative in certain contexts. These characteristics fit the description provided in the stem, confirming non-probability as the appropriate term.

Option C:
Systematic sampling, a form of probability sampling, selects every kth element from an ordered list after a random start, so individual selection probabilities are known. Thus, systematic sampling does not align with the condition of unknown probabilities.

Option D:
Stratified sampling, another probability method, divides the population into homogeneous groups and draws random samples from each stratum. Here again the chance of selection is known and can be calculated, which contradicts the stem. Therefore, stratified sampling is not the right completion.

Random sampling gives each unit in the population a known, often equal, chance of being selected, which makes the selection process impartial. This greatly reduces systematic bias in the composition of the sample because personal judgment and convenience are minimised. While it does not completely eliminate all error, it helps ensure that differences between the sample and population are mainly due to chance. Therefore, the correct effect on selection bias is that random sampling tends to reduce it.

Option A:
If random sampling were to increase bias, it would defeat its main purpose in scientific research. The entire rationale for using random methods is to minimise systematic distortions in the sample. Thus, the option suggesting an increase in bias is conceptually incorrect.

Option B:
Random sampling reduces the likelihood that any particular type of individual is overrepresented or underrepresented for systematic reasons. It supports the use of probability theory to estimate sampling error and build confidence intervals. These features correspond directly to the idea of reducing selection bias as stated in the question.

Option C:
Creating bias would mean deliberately skewing the sample in favour of certain subgroups, which is characteristic of convenience or judgement sampling, not random sampling. Therefore, the suggestion that random procedures create bias is misleading.

Option D:
Ignoring bias does nothing to protect the quality of inferences and would be a sign of poor methodological practice. Random sampling is chosen precisely because it addresses concerns about bias rather than ignoring them, so this option does not fit the role described in the stem.

A sampling frame is the operational list of all elements in the population that are accessible for selection. It could be a register, roster, database or list of households, depending on the study. A good frame should be as complete and accurate as possible to avoid coverage errors. Thus, the list from which the sample is drawn is properly called the sampling frame.

Option A:
A sampling unit is the basic element or group considered for selection, such as a person, household or school. It may be listed in the frame but is not itself the complete list. Therefore, it does not fully match the stem.

Option B:
Population refers to the entire set of units about which the researcher wishes to generalise. It is a conceptual aggregate and may not be directly available as an actual list. Hence, population is different from the specific list used for sampling.

Option C:
Sample size denotes the number of units selected for the study and does not refer to the list from which they are chosen. It is a numerical characteristic, not a register or roster. So this option is not correct here.

Option D:
Sampling frame specifically denotes the complete operational list of elements or clusters from which probability sampling procedures can be implemented. This is exactly what the question describes, making this option correct.