UGC NET Questions (Paper – 1)

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 D correctly describe the main measures used in descriptive statistics. Means, medians and modes summarise central tendency in different ways, while variance and standard deviation capture variability. Statement E is false because descriptive statistics summarise data but do not by themselves conduct hypothesis tests; that is the role of inferential statistics. Therefore, the combination that includes A, B, C and D but excludes E is correct.

Option A:
Option A is correct because it recognises the four key descriptive indicators and their appropriate use, while rejecting the claim that they automatically perform inferential functions. It preserves the conceptual distinction between describing and testing.

Option B:
Option B is incomplete because it omits D, thereby failing to mention how dispersion measures contribute to understanding the spread of data. Without D, the description of descriptive statistics is focused only on central tendency.

Option C:
Option C is incorrect because it leaves out A, not explicitly acknowledging the suitability of the mean for interval and ratio data, which is essential in many analyses. This omission makes the set partial.

Option D:
Option D is wrong because it includes E, the assertion that descriptive statistics automatically test hypotheses, which confuses their function with inferential procedures.

Objectivity means that research conclusions are grounded in observable facts, data and logical reasoning rather than in the researcher’s personal opinions or emotions. It requires the use of systematic methods, clear criteria and transparent procedures. When objectivity is high, different competent researchers examining the same data are likely to reach similar conclusions. Therefore, basing findings on evidence rather than preference reflects objectivity.

Option A:
Feasibility refers to the practicality of conducting a study in terms of time, resources and access, and does not directly address whether findings are evidence-based or biased.

Option B:
Generalisability concerns the extent to which results can be applied to populations or settings beyond the sample studied; it depends partly on sampling and design but is distinct from the issue of personal bias.

Option C:
Objectivity encourages researchers to design studies that minimise bias, such as through blinding or standardisation, and to present results honestly regardless of expectations. This commitment to evidence aligns exactly with the stem, making this option correct.

Option D:
Subjectivity emphasises personal perspectives and interpretations, which may be valued in some qualitative approaches but contradict the specific idea of freeing findings from personal preferences described in the question.

Statement D is the only wrong statement because qualitative research does not “always” use probability sampling and large random samples; it often relies on purposive or theoretical sampling and smaller, information-rich cases. Statements A, B and C correctly describe common features of quantitative and qualitative research, and E is correct about integrating both within mixed-methods designs. Therefore, D alone misrepresents qualitative sampling practice.

Option A:
Option A is correct because it isolates D as the sole incorrect statement and implicitly confirms that A, B, C and E are consistent with standard methodological descriptions. It acknowledges that sampling strategies differ across approaches and that qualitative studies frequently use non-probability samples.

Option B:
Option B is incorrect because it includes A in the set of wrong statements even though A correctly defines quantitative research as relying on numerical data and statistical analysis. Misclassifying a correct definition as wrong makes this option unacceptable.

Option C:
Option C wrongly treats C as wrong along with D, even though C accurately notes that quantitative research may involve experiments, surveys and correlational designs. This option therefore misrepresents accepted quantitative designs.

Option D:
Option D incorrectly includes B with D, despite B being a correct description of qualitative research focusing on meanings and perspectives. Grouping a true statement with a false one leads to an erroneous combination.

Option E suggests that both D and E are wrong, but E is correct because mixed-methods studies do integrate qualitative and quantitative components. By labelling E as wrong, this option directly contradicts established descriptions of mixed-methods research.

Explanatory research goes beyond description to investigate why and how certain phenomena occur by testing specific hypotheses about causal relationships among variables. It often uses more rigorous designs such as experiments or sophisticated statistical models to rule out alternative explanations. The goal is to build and confirm theories that explain observed patterns. Because the stem refers to research designed to test hypotheses about cause–effect relationships, explanatory research is the correct term.

Option A:
Exploratory research is conducted when the problem is not clearly defined and the researcher seeks to gain familiarity, generate ideas or identify variables, without strong prior hypotheses about cause and effect. It is thus less focused on hypothesis testing than explanatory research, so it does not fit the stem.

Option B:
Descriptive research aims to present an accurate picture of characteristics, frequencies or distributions of variables, answering “what” questions rather than “why.” It does not primarily test causal hypotheses, so descriptive research is not the right completion here.

Option C:
Explanatory research typically builds on earlier exploratory and descriptive work and requires careful control or statistical adjustment for confounding variables. Its emphasis on causal reasoning and hypothesis testing aligns exactly with the description given in the question.

Option D:
Diagnostic research is oriented toward identifying reasons for specific conditions or problems in applied contexts, such as why a particular programme is failing, but the term is less commonly used as a general label for testing causal hypotheses across variables. Therefore, diagnostic research is not the best answer.

Statements A, B, C and D define different forms of triangulation, and E explains its main purpose. A is true because data triangulation uses varied sources or times, while B is correct that investigator triangulation brings in multiple researchers. C accurately describes methodological triangulation, and D is right that theory triangulation applies several theoretical lenses. E is also true since triangulation aims to enhance credibility by cross-checking evidence, whereas F is false because triangulation is widely used in qualitative and mixed-methods research, not only quantitative work.

Option A:
Option A is correct as it includes all the valid statements about types and purposes of triangulation and omits F, which is overly restrictive. Together, these statements show that triangulation can be applied at several levels to strengthen research findings. Hence, this option contains all and only the true statements.

Option B:
Option B excludes statement E, ignoring the central reason for using triangulation, namely enhancing credibility of results. While A, B, C and D are correct, the omission of E leaves the account incomplete. Therefore this option cannot be accepted as correct.

Option C:
Option C incorporates F, which wrongly limits triangulation to quantitative studies, and leaves out A, which defines data triangulation. By mixing a false statement with true ones and omitting a correct statement, this combination becomes conceptually flawed.

Option D:
Option D also includes F and omits B, thereby restricting triangulation to certain methods and ignoring the role of multiple investigators. This inaccurate representation disqualifies the option as a correct answer.

A research proposal is a comprehensive document prepared before the study is conducted, describing the problem background, objectives, literature context, methodology, ethical considerations, budget and timeline. It is usually submitted for review and approval by academic committees, funding bodies or supervisors. The proposal demonstrates the feasibility and scholarly merit of the planned study. Therefore, the detailed plan described in the stem is correctly called a research proposal.

Option A:
Proposals often include sections on sampling, instruments, data analysis strategies and expected outcomes, giving reviewers a clear picture of how the research will be implemented. This level of detail and its role in seeking approval align exactly with the description provided in the question.

Option B:
A synopsis is a brief summary of the proposed or completed study, often much shorter than a full proposal and sometimes prepared after an initial approval. It does not typically contain all the methodological and administrative details required in a formal plan. Hence, synopsis is not the best completion.

Option C:
A monograph is a scholarly book or in-depth written work on a single subject, usually reporting completed research rather than proposing it. It is produced after the study is done, not as a planning document, so monograph is not appropriate here.

Option D:
An abstract is a concise summary of a completed or proposed study, highlighting key elements in a few hundred words, and is not a full plan with detailed methods and schedules. Therefore, abstract cannot substitute for the detailed plan described in the stem.

The null hypothesis (H0) typically asserts that there is no significant effect, difference or relationship in the population. It serves as the baseline assumption in statistical testing. Researchers try to gather evidence to decide whether this assumption can be rejected. Hence, the hypothesis of no difference is correctly termed the null hypothesis.

Option A:
A research hypothesis expresses the researcher’s expectation about relationships or differences, often in directional language. It is not usually the default statement of “no difference.” Therefore, it is not the best completion here.

Option B:
The alternative hypothesis (H1) states that a real effect or relationship does exist in the population. It is accepted when the null hypothesis is rejected, so it does not claim “no difference” as described in the stem.

Option C:
Option C, null hypothesis, specifically denotes the statement that any observed differences are due to chance and that there is no real effect in the population. It is the focus of significance testing and is rejected only when evidence is strong. This fits the definition in the question, making this option correct.

Option D:
A directional hypothesis predicts not only that a difference exists but also its direction, such as “greater than” or “less than.” It is a kind of research or alternative hypothesis, not the no-difference statement. So it does not answer the stem.

Statement D is the only wrong statement because altering data to fit expectations is a clear form of falsification and is strictly unethical. Statements A, B and C correctly define plagiarism, fabrication and falsification as forms of research misconduct. Statement E correctly notes that standard ethical codes explicitly prohibit all three practices. Therefore, D alone represents a view that contradicts accepted ethical standards.

Option A:
Option A is incorrect because it treats A together with D as wrong, even though A correctly defines plagiarism as misappropriation of others’ work. Since only D endorses unethical behaviour, grouping A with it misrepresents ethical principles.

Option B:
Option B wrongly suggests that both C and D are wrong. In reality, C accurately defines falsification as manipulative distortion of research procedures or data. This option therefore mislabels a correct statement as wrong.

Option C:
Option C is correct because it isolates D as the sole statement endorsing an unethical practice, namely altering data to match hypotheses. It implicitly affirms that A, B, C and E are in line with standard research ethics, which is consistent with accepted codes of conduct.

Option D:
Option D incorrectly includes B and E among the wrong statements. B correctly describes fabrication as making up data, and E correctly notes that guidelines forbid such behaviour. By treating these as wrong, the option conflicts with basic ethical definitions.

Option E misclassifies C and E as wrong along with D, even though both C and E accurately describe elements of research ethics and misconduct. This broad mislabelling shows conceptual confusion and renders the option unacceptable.

Statements A, B, C and E accurately summarise different research purposes. Descriptive research portrays characteristics, exploratory research clarifies poorly defined problems, and explanatory research tests hypotheses about relationships and causes. Descriptive findings can indeed guide later explanatory work. Statement D is false because historical research does interpret past events; it does not merely list them.

Option A:
Option A is incomplete as it omits E, thereby ignoring how descriptive work often serves as a foundation for more complex causal investigations. Without E, the progression from description to explanation is not fully represented.

Option B:
Option B is correct because it gathers all the true statements about descriptive, exploratory and explanatory research and their interrelationships. It rightly excludes D, which mischaracterises historical research as uninterpreted description. This combination aligns with standard classifications of research purposes.

Option C:
Option C is incorrect because it retains D, the false statement about historical research, and omits A, which properly defines descriptive research. Including D and excluding A distorts the classification of research types.

Option D:
Option D is wrong because it includes D along with other statements, accepting an incorrect view of historical research. By treating D as correct, the option underestimates the interpretive nature of historical inquiry.

Confidentiality means that researchers safeguard the privacy of participants by ensuring that identifiable information is not revealed to unauthorised persons or reported in a way that could expose individual identities. Data may be stored securely, and results are often presented in aggregate form or with pseudonyms to protect participants. Upholding confidentiality builds trust and encourages honest responses, which are crucial for the integrity of research. Because the stem focuses on preventing disclosure of information that reveals identity, it is referring to the principle of confidentiality.

Option A:
Beneficence is the ethical principle that researchers should maximise possible benefits and minimise potential harms to participants. While maintaining confidentiality can contribute to beneficence, the term itself is broader and does not specifically denote privacy protection. Since the question emphasises non-disclosure of identifiable information, beneficence is not the most accurate completion.

Option B:
Confidentiality requires researchers to design data handling procedures that control who has access to raw data, how long data are kept and how they are destroyed. Participants are often informed about these protections during the consent process. This focus on privacy and non-disclosure of identifying details matches exactly the situation described in the stem, making confidentiality the correct answer.

Option C:
Deception involves intentionally withholding important information from participants or giving them false information about some aspect of the study, typically to prevent bias in behaviour. While some studies may use deception under strict ethical safeguards, it is the opposite of transparency and does not protect identities. Therefore, deception cannot be the appropriate term for the principle described.

Option D:
Randomisation is a methodological technique for assigning participants to groups in an unbiased manner, commonly used in experimental designs to control confounding variables. It is not an ethical principle about protecting the privacy of participant information. As such, randomisation does not fit the context of the stem.

Quantitative research emphasises numerical measurement of variables and uses statistical methods to summarise and analyse data. It often relies on structured instruments, large samples and objective procedures to test hypotheses or estimate population parameters. This approach is typically associated with the positivist paradigm and seeks to produce generalisable findings. Therefore, research that focuses on numerical data and statistical analysis is correctly called quantitative research.

Option A:
Qualitative research explores meanings, experiences and processes through non-numerical data such as interviews and observations. It does not primarily rely on numerical measurement and statistical analysis, so it does not match the stem.

Option B:
Option B, quantitative research, is designed to quantify relationships, differences or trends, often using descriptive and inferential statistics. Variables are operationalised in measurable units, and results are expressed in numbers like percentages or means. This aligns closely with the description, making this option correct.

Option C:
Narrative research focuses on collecting and analysing stories or life histories and is usually qualitative in nature. Its main concern is understanding narrative structure and meaning, not numerical data.

Option D:
Ethnographic research is a qualitative strategy involving immersion in a cultural setting to understand shared practices and meanings. Although it may generate some counts, it is not primarily numerical and statistical in orientation, so it is not the right answer.

Sampling error refers to the natural discrepancy between sample statistics and population parameters that arises because only part of the population is observed. Even with perfectly random sampling and accurate measurement, different samples will yield slightly different estimates. This variability is quantified in terms such as standard error and confidence intervals. Hence, the difference described in the stem is correctly called sampling error.

Option A:
Measurement error occurs when the instruments or procedures used do not yield perfectly accurate values, leading to inaccuracies that are distinct from the randomness of sampling. It is related to how variables are measured rather than to the fact that only a subset is observed. Therefore, measurement error is not the best completion.

Option B:
Systematic error is a consistent bias in measurement or procedure that pushes results in one direction, such as miscalibration or interviewer bias. It does not average out across samples and is not due simply to using a subset. Consequently, systematic error is not what the stem is defining.

Option C:
Sampling error arises even in well designed probability samples and can be estimated and controlled by appropriate sample sizes. It reflects sample-to-sample fluctuation, making it the correct term for the discrepancy between sample and population values mentioned in the question.

Option D:
Non-response error occurs when individuals who fail to respond differ systematically from those who do, introducing bias, but this is a specific non-sampling error, not the inherent fluctuation due to partial observation. Thus, non-response error is not the accurate completion.

Closed-ended questions present respondents with a limited set of predefined response options, such as yes/no, multiple-choice categories or rating scales. This format simplifies coding, tabulation and statistical analysis because responses can be easily quantified. It also reduces ambiguity and helps ensure that all respondents interpret options in a similar way. Hence, items with fixed responses like ‘yes’ or ‘no’ are accurately described as closed-ended questions.

Option A:
Open-ended questions allow respondents to answer in their own words without being constrained to predefined categories. They yield rich qualitative data but are harder to code and analyse quantitatively. Since the stem explicitly mentions a fixed set of responses, open-ended questions are not the correct type.

Option B:
Closed-ended questions restrict answers to specified alternatives, which enhances comparability across respondents and increases reliability of measurement. This structure fits the description of yes/no items given in the question.

Option C:
Projective questions use ambiguous stimuli or tasks, such as sentence completion or story-telling, to uncover underlying attitudes or motives. They do not typically employ simple fixed categories like yes/no, so projective questions are not appropriate here.

Option D:
Leading questions are worded in a way that suggests a preferred answer or biases respondents, which is a problem of wording rather than of response format. They may be open or closed, but the stem is focused on fixed response sets, making leading questions an incorrect 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.