Techniques – Solved PYQs of Sociology – UGC NET

Sampling

1. Which type of sampling method is ideally suited where there is no knowledge about population?

(A) Convenient sampling method
(B) Purposive sampling method
(C) Quota sampling method
(D) Snowball sampling method

Correct Answer: (D) Snowball sampling method

Snowball sampling is a non-probability sampling technique that is particularly useful when researchers have little or no knowledge about the population being studied or when the population is difficult to identify, locate, or access. In this method, the researcher begins with a small number of known participants and then asks them to identify or refer other individuals who meet the criteria for the study. As each participant helps locate additional participants, the sample gradually expands, much like a snowball rolling and increasing in size.

This sampling method is especially valuable for studying hidden populations, hard-to-reach groups, or communities for which no complete sampling frame exists. Examples include migrant workers, homeless individuals, drug users, marginalized communities, underground organizations, and people involved in sensitive social activities. Since researchers may not initially know who belongs to the target population, referrals from participants become an effective means of gaining access.

Option (A) Convenience sampling involves selecting respondents who are easiest to reach and available to the researcher. Although simple and inexpensive, it is not specifically designed for situations where the population is unknown. Option (B) Purposive sampling relies on the researcher’s judgment to select individuals who possess particular characteristics relevant to the study. This method requires some prior knowledge of the population. Option (C) Quota sampling involves selecting respondents according to predetermined categories and proportions, which also requires information about the population structure.

Snowball sampling has become an important technique in qualitative research, community studies, ethnographic investigations, and research involving socially sensitive topics. While it may not produce statistically representative samples, it is highly effective for gaining access to populations that are otherwise difficult to study. Its strength lies in the use of social networks and participant referrals to identify individuals who would be challenging to locate through conventional sampling procedures.

2. Matching Sampling Concepts (DEC 2012)

Match List-I with List-II and select the correct answer from the codes given below:

Codes:

Correct Answer: (B) II, I, IV, III

The correct matching is:

(a) Oral History → (ii) People’s Knowledge
(b) Survey → (i) Quantitative Data
(c) Sampling → (iv) Representative of a whole
(d) Case Study → (iii) In-depth study of a unit

Oral History is a research method that involves collecting and preserving people’s memories, experiences, narratives, and personal accounts of past events. It relies heavily on the knowledge possessed by individuals and communities, making people’s knowledge its central source of information. Oral history is widely used in sociology, anthropology, history, and cultural studies to understand experiences that may not be recorded in written documents.

A Survey is a method of collecting information from a sample of respondents through questionnaires or structured interviews. Survey research is generally associated with the collection of quantitative data, which can be statistically analyzed to identify patterns, trends, and relationships within a population. Although surveys can sometimes include qualitative elements, their primary association is with quantitative measurement.

Sampling refers to the process of selecting a subset of individuals, groups, or units from a larger population. The purpose of sampling is to obtain a group that is representative of the whole population, enabling researchers to make valid generalizations without studying every member of the population. Representative sampling is especially important in quantitative research and survey studies.

A Case Study is a research method involving the intensive and detailed examination of a single individual, group, institution, community, event, or social unit. The emphasis is on obtaining a deep and comprehensive understanding of the selected case. This makes the case study an in-depth study of a unit, often using multiple sources of information and various research techniques.

The matching pattern (a)–(ii), (b)–(i), (c)–(iv), and (d)–(iii) corresponds to Option (B). These concepts represent important research methods and procedures used in social science investigations, each serving a distinct purpose in the collection and analysis of social data.

3. Concept of Sampling (JUNE 2013)

A university has 7530 students and a researcher draws 10 percent sample from it amounting to a total of 753. This is called:

(A) Sample size
(B) Sample ratio
(C) Sample element
(D) Purposive sampling

Correct Answer: (B) Sample ratio

In sampling theory, the proportion of the population that is selected for inclusion in a study is known as the sample ratio or sampling ratio. It is calculated by dividing the number of units selected for the sample by the total number of units in the population. In the given example, the total population consists of 7530 students, and the researcher selects 753 students, which represents 10 percent of the total population. This proportion of 10 percent is the sample ratio.

The sample ratio is important because it indicates the extent to which the sample represents the population. Researchers often express it as a percentage or fraction. A sampling ratio of 10 percent means that one out of every ten members of the population has been included in the sample. The concept is frequently used in survey research and quantitative studies where researchers need to determine how much of the population will be studied.

Option (A) Sample size refers to the actual number of units selected for the study. In this example, the sample size is 753, not the 10 percent proportion. Option (C) Sample element refers to an individual unit selected from the population, such as a single student in this case. Option (D) Purposive sampling is a non-probability sampling technique in which respondents are selected deliberately based on specific characteristics or research objectives.

The distinction between sample size and sample ratio is important in research methodology. While sample size refers to the number of respondents included in the study, the sample ratio refers to the relationship between the sample and the total population. Understanding sampling ratios helps researchers design representative studies and evaluate the adequacy of their samples for drawing valid conclusions about the larger population.

4. Sampling from Substrata (DEC 2014)

In which of the following sampling methods, equal number of units are selected from each substratum regardless of their strength in the population and sub-population?

(A) Simple random sampling
(B) Stratified random sampling
(C) Proportionate stratified random sampling
(D) Disproportionate stratified random sampling

Correct Answer: (D) Disproportionate stratified random sampling

Disproportionate stratified random sampling is a form of stratified sampling in which the population is first divided into distinct subgroups or strata, and then samples are drawn from each stratum. Unlike proportionate stratified sampling, the number of units selected from each stratum is not based on the actual size or proportion of that stratum in the population. Instead, researchers may select equal numbers or intentionally different numbers from each stratum regardless of their relative strength in the population.

This method is particularly useful when some strata are very small and might be underrepresented if sampling were conducted strictly according to population proportions. By selecting equal numbers from each stratum, researchers can ensure adequate representation of all groups and make meaningful comparisons among them. For example, if a population consists of several social groups of unequal sizes, a researcher may choose the same number of respondents from each group to facilitate comparative analysis.

Option (A) Simple random sampling does not involve dividing the population into strata. Every unit in the population has an equal chance of selection, and no attention is given to subgroup representation. Option (B) Stratified random sampling is a broad category that includes both proportionate and disproportionate forms but does not specifically indicate equal selection from each stratum. Option (C) Proportionate stratified random sampling allocates sample units according to the actual proportion of each stratum in the population, ensuring that larger strata contribute more respondents and smaller strata contribute fewer respondents.

The distinguishing feature of disproportionate stratified random sampling is that sample allocation does not mirror the population distribution. Researchers often use this method when they need detailed information about smaller groups, wish to compare strata of different sizes, or seek to improve analytical precision for specific subpopulations. This makes it especially valuable in sociological, educational, demographic, and market research where subgroup comparisons are an important objective.

5. Condition for Random Sample (DEC 2015)

In order to qualify as a random sample:

(A) The researcher must pretest the subject
(B) The researcher must conduct pilot study
(C) At least hundred people must be selected
(D) Every member of the population must have an equal chance of being selected

Correct Answer: (D) Every member of the population must have an equal chance of being selected

A random sample is a sample selected through a procedure in which every member of the population has an equal and independent chance of being chosen. This principle is the foundation of probability sampling and is essential for ensuring fairness, minimizing selection bias, and improving the representativeness of the sample. When each member of the population has an equal opportunity to be included, the sample is more likely to reflect the characteristics of the entire population accurately.

The concept of random sampling plays a crucial role in scientific research, particularly in quantitative studies and survey research. Researchers use methods such as random number tables, lottery methods, computer-generated random selections, or other probability-based techniques to ensure that selection is based purely on chance rather than personal judgment or convenience. This increases the reliability and validity of the findings and allows researchers to make generalizations from the sample to the broader population.

Option (A) The researcher must pretest the subject is incorrect because pretesting is related to testing research instruments such as questionnaires or interview schedules before the main study. It is not a condition for obtaining a random sample. Option (B) The researcher must conduct pilot study is also incorrect because a pilot study is a preliminary investigation conducted to test research procedures and instruments. While useful, it is not a requirement for random sampling. Option (C) At least hundred people must be selected is incorrect because there is no fixed number that determines whether a sample is random. A sample may be random regardless of its size, provided that the selection process gives every population member an equal chance of inclusion.

The defining feature of a random sample is equality of selection probability. This principle helps reduce systematic errors, enhances objectivity, and forms the basis for many statistical techniques used in social science research. Random sampling remains one of the most important methods for achieving representative samples and producing findings that can be generalized to the larger population with greater confidence.

6. Precondition for Random Sampling (NOV 2017)

The pre-condition to qualify a sample as a random one is:

(A) The researcher must pretest the subject
(B) The researcher must conduct pilot study
(C) At least 100 respondents must be selected from a given population
(D) Every member of the population must have an equal chance of being selected

Correct Answer: (D) Every member of the population must have an equal chance of being selected

Explanation:
Random sampling is a sampling method in which each member of the target population has an equal and independent chance of being selected for inclusion in the sample. This principle is the most important requirement for a sample to be considered truly random. The objective of random sampling is to minimize selection bias and ensure that the sample accurately represents the characteristics of the entire population.

Option (A) is incorrect because pretesting is a procedure used to test research instruments such as questionnaires before the main study. While pretesting improves the quality of data collection, it is not a requirement for random sampling. Option (B) is also incorrect because a pilot study is a small-scale preliminary study conducted to assess the feasibility of a research project. Conducting a pilot study has no direct connection with whether a sample is random or not. Option (C) is incorrect because there is no fixed minimum number of respondents required for random sampling. A sample can be random regardless of its size, provided that every unit in the population has an equal opportunity to be selected.

The concept of equal probability selection is central to probability sampling techniques, including simple random sampling, systematic sampling, stratified sampling, and cluster sampling. In simple random sampling, selection may be carried out using methods such as lottery techniques, random number tables, or computer-generated random numbers. The quality of randomness depends on the selection process rather than the number of participants or the use of preliminary research activities. Equal chance of selection remains the fundamental criterion that distinguishes random sampling from non-probability sampling methods such as convenience sampling, purposive sampling, and quota sampling.

7. Probability Sampling (JULY 2018)

Which one of the following methods is known as probability sampling?

(A) A sample selected by Tippets Numbers from the given totality
(B) A sample selected from those who were available
(C) A sample selected considering the purpose of research
(D) A sample selected by considering the various categories of respondents

Correct Answer: (A) A sample selected by Tippets Numbers from the given totality

Explanation:

Probability sampling refers to a sampling technique in which every unit of the population has a known and measurable chance of being selected. The defining feature of probability sampling is the use of a random selection process, which helps reduce bias and increases the representativeness of the sample. Option (A) is the correct answer because Tippett’s Random Number Table, developed by L. H. C. Tippett, is a recognized method used for selecting samples randomly from a population. By using random numbers, the researcher ensures that each member of the population has an equal opportunity to be included in the sample, which is the basic requirement of probability sampling.

Option (B) represents convenience sampling, where respondents are chosen based on their availability and accessibility. This method does not provide equal chances of selection to all population members and is classified as a non-probability sampling technique. Option (C) refers to purposive sampling or judgment sampling, in which respondents are selected according to the specific objectives and requirements of the study. The researcher’s judgment plays a central role in this method, making it a non-probability approach. Option (D) resembles quota sampling when categories of respondents are considered without random selection. Although respondents may be chosen from different groups, the absence of a random process means it does not qualify as probability sampling.

Probability sampling includes methods such as simple random sampling, systematic sampling, stratified sampling, and cluster sampling. These techniques are widely used in social science, educational, and communication research because they improve the likelihood that findings can be generalized to the larger population. The use of random number tables, computer-generated random numbers, or lottery methods are common procedures for implementing probability sampling. Tippett’s Random Number Table remains an important historical tool in research methodology and is frequently cited in examinations dealing with sampling techniques and research methods.

8. Sampling with Equal Units (DEC 2018)

In which of the following sampling methods, equal number of units are selected from each substratum regardless of their strength in the population and sub-population?

(A) Simple random sampling
(B) Stratified random sampling
(C) Proportionate stratified random sampling
(D) Disproportionate stratified random sampling

Correct Answer: (D) Disproportionate stratified random sampling

Explanation:

Disproportionate stratified random sampling is a method of stratified sampling in which the population is first divided into different strata or subgroups based on specific characteristics such as age, gender, occupation, education, or region. After the strata are formed, an equal number of units may be selected from each stratum regardless of the actual size or proportion of that stratum in the total population. This feature makes option (D) the correct answer.

In a population, some strata may be very large while others may be relatively small. If the researcher selects the same number of respondents from every stratum without considering their actual population strength, the sampling becomes disproportionate. This method is often used when researchers want adequate representation from smaller groups that might otherwise be underrepresented in the sample. For example, if a population consists of 80% urban respondents and 20% rural respondents, a researcher may still choose an equal number of participants from both groups to facilitate comparison and detailed analysis.

Option (A), simple random sampling, does not involve dividing the population into strata. Every member of the population is treated as part of a single group and is selected through a random process. Option (B), stratified random sampling, is a broader category that includes both proportionate and disproportionate approaches, making it too general to answer the specific question. Option (C), proportionate stratified random sampling, selects respondents from each stratum according to their actual proportion in the population. Larger strata contribute more respondents, while smaller strata contribute fewer respondents, maintaining the same distribution as found in the population.

The primary advantage of disproportionate stratified random sampling is that it allows researchers to study smaller strata in greater detail and make meaningful comparisons among different groups. This method is particularly useful in social science, communication, educational, and market research where certain subgroups are numerically small but analytically important. The defining characteristic remains the selection of equal or intentionally unequal numbers from different strata without following their actual proportion in the population, making disproportionate stratified random sampling a distinct and widely used probability sampling technique.

9. Characteristics of an Ideal Sample (JUNE 2020)

Sample which fulfills the requirements of efficiency, representativeness, reliability and flexibility is called:

(A) Stratified sample
(B) Optimum sample
(C) Accidental sample
(D) Cluster sample

Correct Answer: (B) Optimum sample

Explanation:

An optimum sample is a sample that possesses the essential qualities required for effective and accurate research. It fulfills the requirements of efficiency, representativeness, reliability, and flexibility, making it the most suitable sample for achieving the objectives of a study. The concept of an optimum sample is closely related to obtaining maximum accuracy and usefulness of research findings while using available resources such as time, money, and manpower in a balanced manner.

The characteristic of representativeness means that the sample accurately reflects the important characteristics of the population from which it is drawn. A representative sample allows researchers to generalize findings with greater confidence. Reliability refers to the consistency and stability of the results obtained from the sample. If the study is repeated under similar conditions, a reliable sample is likely to produce comparable findings. Efficiency indicates that the sample provides accurate and meaningful results without unnecessary expenditure of resources. Flexibility refers to the sample’s ability to accommodate the practical needs and changing requirements of the research process while still maintaining scientific validity.

Option (A), stratified sample, is a specific sampling technique in which the population is divided into homogeneous groups called strata before sampling. Although stratified sampling can contribute to representativeness, it does not automatically satisfy all the characteristics mentioned in the question. Option (C), accidental sample, is a non-probability sampling method based on convenience and availability of respondents. Such samples often suffer from bias and may not adequately represent the population. Option (D), cluster sample, is another probability sampling method in which naturally occurring groups or clusters are selected, but the method itself is not defined by the combined qualities of efficiency, reliability, representativeness, and flexibility.

In research methodology, an optimum sample is often regarded as the ideal sample because it strikes a balance between statistical accuracy and practical feasibility. Researchers aim to determine an optimum sample size and composition so that the collected data can provide dependable results while minimizing sampling error and research costs. The idea of an optimum sample is particularly important in social science, communication, educational, and survey research, where the quality of the sample directly influences the validity and credibility of the research findings.

10. Concept of Universe in Sampling (MAR 2023)

The universe of units from which a sample is to be selected is called:

(A) Population
(B) Survey
(C) Reliability
(D) Validity

Correct Answer: (A) Population

Explanation:

In research methodology and statistics, the term population refers to the complete set of individuals, objects, events, groups, or units that possess certain characteristics relevant to a study. It is often described as the universe from which a researcher selects a sample for investigation. Every member of the population shares some common feature that makes it relevant to the research problem. Since it is often impractical or impossible to study every unit in a large population, researchers select a sample that represents the population and use the findings to make inferences about the entire group.

The concept of population is fundamental to the process of sampling. Before selecting a sample, the researcher must clearly define the population to ensure that the sample accurately reflects the characteristics of the larger group. Populations may be finite, such as all students enrolled in a particular university, or infinite, such as all possible outcomes of repeated observations. They may also be classified as target populations and accessible populations depending on the scope and practical limitations of the study.

Option (B), survey, is a research method used for collecting data from respondents through questionnaires, interviews, or schedules. A survey is a technique of data collection and not the universe from which a sample is drawn. Option (C), reliability, refers to the consistency and stability of a research instrument or measurement process. A reliable instrument produces similar results when applied repeatedly under comparable conditions. Option (D), validity, refers to the accuracy and appropriateness of a measurement tool in measuring what it is intended to measure. Both reliability and validity are important concepts in research design, but neither represents the group from which a sample is selected.

In sampling theory, the terms population, universe, and sometimes aggregate are often used interchangeably to denote the total collection of units under study. Defining the population correctly is essential because the quality of the sample and the accuracy of the research findings depend upon how clearly and precisely the population has been identified. A well-defined population serves as the foundation for selecting representative samples and conducting scientifically sound research.

11. Sampling Type and Inferential Statistics (JUNE 2023)

Identify the type of sampling among the following in which the inferential statistics cannot be applied:

(A) Simple Random Sampling
(B) Stratified Sampling
(C) Purposive Sampling
(D) Multi Stage Random Sampling

Correct Answer: (C) Purposive Sampling

Explanation:

Purposive sampling is a non-probability sampling method in which respondents are selected deliberately by the researcher based on specific characteristics, expertise, experience, or relevance to the research objectives. The selection depends on the researcher’s judgment rather than on a random process. Since every member of the population does not have a known and equal chance of being selected, the sample may not accurately represent the entire population. For this reason, the use of inferential statistics is generally not appropriate in purposive sampling.

Inferential statistics are statistical techniques used to draw conclusions about a larger population based on data collected from a sample. These techniques, such as hypothesis testing, confidence intervals, regression analysis, and significance testing, assume that the sample has been selected through a probability sampling method. Probability sampling allows researchers to estimate sampling error and calculate the likelihood that sample results reflect population characteristics. Without random selection, these assumptions are weakened, making statistical generalization unreliable.

Option (A), simple random sampling, is a probability sampling technique in which every member of the population has an equal chance of selection. Because of its random nature, inferential statistical methods can be validly applied. Option (B), stratified sampling, is also a probability sampling method where the population is divided into homogeneous strata and samples are selected randomly from each stratum. This approach often improves representativeness and supports the application of inferential statistics. Option (D), multi-stage random sampling, involves selecting samples through several stages using random methods at each stage. It is widely used in large-scale surveys and also permits the use of inferential statistical analysis.

Purposive sampling is commonly employed in qualitative research, case studies, exploratory investigations, media studies, and specialized population studies where the objective is to obtain in-depth information rather than statistical generalization. Researchers may intentionally choose key informants, experts, or individuals possessing unique knowledge related to the research topic. While purposive sampling can provide rich and meaningful insights, its findings are generally limited to the selected participants and cannot be confidently generalized to the broader population through the use of inferential statistical procedures.

12. Statements on Sample Size (DEC 2023)

Given below are two statements:

Statement I: Sample size determination indicates the number of people in the sample and the procedures used to compute this number.
Statement II: A large sample will provide less accuracy in the inferences made.

In the light of the above statements, choose the correct answer from the options given below:

(A) Both Statement I and Statement II are true
(B) Both Statement I and Statement II are false
(C) Statement I is true, but Statement II is false
(D) Statement I is false, but Statement II is true

Correct Answer: (C) Statement I is true, but Statement II is false

Explanation:

Statement I is true because sample size determination refers to the process of deciding how many individuals, cases, or observations should be included in a sample for a research study. It not only identifies the required number of respondents but also involves the statistical procedures, formulas, assumptions, and criteria used to calculate that number. Researchers determine sample size by considering factors such as population size, confidence level, margin of error, variability within the population, statistical power, and research objectives. Proper sample size determination is an essential step in research design because it directly influences the quality and credibility of the findings.

Statement II is false because, in general, a larger sample size tends to increase the accuracy of statistical estimates and improve the reliability of inferences made about the population. As sample size increases, sampling error usually decreases, allowing the sample to more closely represent the characteristics of the population. Larger samples provide more stable estimates of population parameters and increase the precision of statistical analyses. For this reason, many quantitative research studies seek an adequately large sample to strengthen the validity of their conclusions.

The relationship between sample size and accuracy is a fundamental principle in sampling theory and inferential statistics. When a sample is too small, random fluctuations may have a greater influence on the results, making estimates less dependable. Increasing the sample size generally improves the confidence researchers can have in their findings, provided that the sample is selected appropriately and remains representative of the population. A larger sample alone does not guarantee perfect results, as issues such as sampling bias, measurement errors, and poor research design can still affect accuracy. Even so, under proper sampling conditions, larger samples contribute to greater precision and more dependable statistical inference, making Statement I correct and Statement II incorrect.