UGC NET Questions (Paper – 1)

A sampling unit is the basic unit that can be selected for inclusion in the sample, such as an individual, household or school. It is the entity about which the researcher collects data. Clearly defining sampling units is essential for precise sampling and analysis. Thus, the element about which information is collected is correctly termed the sampling unit.

Option A:
Option A clearly names the sampling unit as the fundamental unit of selection and observation. It may coincide with the unit of analysis in many studies. Because the stem asks for the basic element from which data are collected, this option is correct.

Option B:
Sampling error refers to the natural discrepancy between sample statistics and population parameters due to observing only a subset. It is an error term and not the basic element itself. So it does not fit the description.

Option C:
Sampling ratio is the proportion of the population that is included in the sample. It is a statistic, not the element on which information is collected. Hence, this option is not appropriate.

Option D:
Sampling interval is the fixed distance between selected units in systematic sampling, such as every 10th name. It is a procedure parameter rather than the unit of data collection. Therefore, it is not the correct answer.

The chi-square test is designed to assess whether there is a significant association between two categorical variables in a contingency table by comparing observed frequencies with expected frequencies under independence. It does not require assumptions about normal distribution of a quantitative variable and is therefore considered non-parametric. When the calculated chi-square value exceeds the critical value, the null hypothesis of independence is rejected. Hence, the test described in the stem is the chi-square test.

Option A:
The chi-square statistic summarises the squared differences between observed and expected frequencies scaled by the expected frequencies. Because it is suited to nominal data in contingency tables, it is the standard method for testing independence among categorical variables, matching the stem precisely and making this option correct.

Option B:
The t test compares means of one or two groups on a quantitative variable and assumes approximate normality; it is not used for testing independence in contingency tables of categorical data.

Option C:
The F test underlies analysis of variance and regression, focusing on ratios of variances for comparing multiple means rather than associations between categorical variables.

Option D:
The z test is used for large-sample inference about means or proportions under normal approximations and is not the typical choice for contingency table analysis of categorical data.

Statements A, B, C and E are correct descriptions of pilot studies and pre-testing, while D and F are false. Statement A is true because a pilot is a small-scale version of the main study. Statement B rightly notes that pre-testing reveals ambiguities, and statement C is correct as pilots assess feasibility of procedures. Statement E is also true since pilot data can inform sampling and variability estimates, whereas D is false because changes may be needed after a pilot and F is false because pilot work is useful even for experienced researchers.

Option A:
Option A combines A, B, C and E, capturing the main purposes of pilot studies and pre-testing while excluding the incorrect statements. It acknowledges trial runs, identification of problems, feasibility assessments and refinement of sampling. As it contains all and only the true statements, this is the correct answer.

Option B:
Option B omits E and therefore ignores the role of pilot data in refining sampling and variability estimates. Although the statements it includes are correct, the combination is incomplete because it leaves out one true statement from the list.

Option C:
Option C includes D along with B, C and E. Since D is false in claiming that no changes should be made after a pilot, any option that contains it becomes flawed. Consequently this combination cannot be considered correct even though it also includes some true statements.

Option D:
Option D contains F, which is false, and omits B. It thereby misrepresents the necessity of pilot studies irrespective of prior experience and also misses the pre-testing function of detecting ambiguous items. Mixing false statements with true ones makes this option invalid.

A ratio scale has all the properties of an interval scale—equal intervals between values—and, in addition, a true zero point that indicates the complete absence of the measured attribute. This allows statements such as one value being twice or half of another to be meaningful. Examples include height, weight and reaction time. Because the stem highlights equal intervals, a true zero and meaningful ratios, it is describing a ratio scale.

Option A:
Interval scales, such as temperature in Celsius, have equal intervals but lack a true zero point, which makes ratio statements (like “twice as hot”) conceptually problematic. Therefore, interval is not sufficient to capture the “true zero and meaningful ratios” criterion given in the question.

Option B:
Ratio scales permit the full range of mathematical operations, including addition, subtraction, multiplication and division. This rich set of properties enables powerful statistical analyses and interpretable comparisons. These features match the description in the stem, so ratio is the correct completion.

Option C:
Nominal scales classify data into categories without order or quantitative meaning, making arithmetic operations and ratio statements inappropriate. They lack both equal intervals and a true zero, so nominal cannot be the answer.

Option D:
Ordinal scales indicate order among categories but do not assume equal differences between ranks, and they lack a true zero point. While they allow ranking, they do not support meaningful ratio comparisons, making ordinal an incorrect option for the stem’s description.

The median is the middle value of an ordered data set, with an equal number of observations lying below and above it. It is especially useful when the distribution is skewed or when extreme values might distort the mean. As a positional measure, it depends on the order of scores rather than their magnitude. Because the stem describes a value that divides the distribution into two equal halves, median is the correct term.

Option A:
The median is determined by arranging the scores in ascending or descending order and identifying the central position, or averaging the two central values when the number of observations is even. It remains unaffected by extreme scores because it depends only on relative position. These characteristics fit the description in the question, confirming median as the right completion.

Option B:
Mode is the score that occurs most frequently in a distribution and may not coincide with the point that divides the distribution into two equal halves. A distribution can have more than one mode or even no clear mode. Thus, mode is not what the stem is defining.

Option C:
Mean is the arithmetic average, found by adding all scores and dividing by the number of scores. It takes every value into account and is sensitive to extreme scores, but it is not the score where 50 percent of observations lie below and 50 percent above. Therefore, mean is not the correct answer here.

Option D:
Midrange is calculated as the average of the minimum and maximum values in the distribution and may not reflect the central tendency accurately if extreme scores are present. It is not defined as the point that splits the distribution into two equal halves. Hence, midrange is not the appropriate completion.

An ethics committee, often known as an institutional review board, examines research proposals to ensure that risks to participants are minimised and justified. It checks for adequate informed consent, confidentiality protections and fair selection of subjects. Approval from this committee is usually required before data collection begins. Hence, the committee described in the stem is correctly called the ethics committee.

Option A:
A sampling committee is not a standard term in research governance; sampling decisions are usually made by the research team. It has no recognised role in protecting participant rights at an institutional level. Therefore, this option is not suitable.

Option B:
Option B, ethics committee, focuses specifically on ethical issues such as risk–benefit balance, vulnerable groups and privacy. It may require modifications to the protocol to improve participant protection. Because the stem emphasises rights, safety and well-being, this option is correct.

Option C:
A steering committee may oversee the overall direction of a large project, including budgets and timelines, but is not primarily responsible for detailed ethical review. So it does not match the description in the question.

Option D:
An editorial committee deals with publication decisions for journals or books, evaluating quality and originality rather than ethics of data collection. Hence, it is not the correct label here.

Debriefing is conducted after participants have completed a study, particularly when deception or incomplete disclosure was involved. During debriefing, the researcher reveals the true purpose, clarifies any misleading information and checks for adverse reactions. This process helps restore trust, educates participants about the research and permits them to withdraw their data if they feel distressed. Therefore, the post-study explanation described in the stem is correctly called debriefing.

Option A:
Option A, debriefing, is an important ethical safeguard that demonstrates respect for participants and mitigates potential harm caused by deception. It is distinct from initial information-giving, which is covered by informed consent, and clearly fits the stem’s context.

Option B:
Briefing refers to providing initial instructions or information before the study begins and does not involve correcting deceptive elements afterwards. Thus, briefing is not the right answer for a post-participation explanation.

Option C:
Anonymising is the process of removing or encoding identifying information in data so that individuals cannot be recognised. It is related to privacy protection but not to explaining the true purpose after deception.

Option D:
Sampling is a methodological step concerning the selection of participants and has nothing to do with post-study ethical communication, so it is not appropriate here.

Statements A, B and D describe central functions of hypotheses in research. Hypotheses help focus data collection on relevant variables, indicate suitable statistical tests and often specify the expected direction of relationships. Statement C is false because hypotheses concern propositions to be tested, not established facts, and E is false because explanatory research commonly uses hypotheses to examine causal relations. Thus, the correct combination must include A, B and D while excluding C and E.

Option A:
Option A is incomplete because it excludes D, ignoring the role of hypotheses in indicating whether relationships are expected to be positive, negative or null. Without D, the directional function of hypotheses is missing from the description.

Option B:
Option B is correct because it combines the three statements that accurately characterise how hypotheses guide the design and analysis of research. It recognises that hypotheses are tools for inquiry rather than restatements of facts, and it correctly excludes C and E.

Option C:
Option C is wrong because it retains C, which incorrectly treats hypotheses as established facts, and omits A, which highlights their role in focusing data collection. Including C distorts the provisional nature of hypotheses.

Option D:
Option D is incorrect because it includes E, the statement that hypotheses are meaningless in explanatory research, even though explanatory studies rely heavily on hypothesised relationships. Accepting E contradicts the core logic of hypothesis-testing research.

A normal distribution is a theoretical continuous probability distribution characterised by perfect symmetry about its mean, with the mean, median and mode all equal at the centre. The tails extend infinitely in both directions, and many natural and social phenomena approximate this shape under certain conditions. It plays a central role in inferential statistics because many tests assume normality of errors or variables. Because the stem describes a symmetrical, bell-shaped distribution with coinciding mean, median and mode, normal distribution is the correct term.

Option A:
Skewed distributions are asymmetrical, with one tail longer or fatter than the other, causing the mean, median and mode to separate rather than coincide at the centre. They do not fit the perfectly symmetrical, bell-shaped description given in the stem. Therefore, skewed is not the right answer.

Option B:
Normal distributions are fully described by their mean and standard deviation, and many sampling distributions approach normality under the central limit theorem. This makes them fundamental to constructing confidence intervals and conducting many parametric tests, which aligns with the importance implied in the question.

Option C:
A bimodal distribution has two distinct peaks, representing two modes, and is not necessarily symmetric or bell-shaped in the classic Gaussian sense. As the stem indicates one central peak where the three measures of central tendency coincide, bimodal distribution cannot be the correct completion.

Option D:
A uniform distribution has equal probability across its range, often represented by a rectangular shape rather than a bell. It does not exhibit the central peak and tapering tails characteristic of the normal curve, so uniform is not appropriate here.

Statements A, B, C and D give an accurate account of the main scales of measurement, whereas statement E is incorrect. Statement A is true because nominal scales simply classify cases into categories with no inherent order. Statement B is correct in noting that ordinal scales permit ranking but do not guarantee equal distances between ranks, and statement C accurately describes interval scales as having equal units but no absolute zero. Statement D properly defines ratio scales as having a true zero and allowing meaningful ratio interpretations, while statement E is false since addition and subtraction are not meaningful on purely nominal categories.

Option A:
Option A lists A, B, C and D only and excludes E, which is the only false statement. By including all the true statements and leaving out the incorrect claim about mathematical operations on nominal data, this combination correctly identifies the full set of correct statements. Therefore it is the right answer.

Option B:
Option B includes A, B and C but omits D. While A, B and C are correct, leaving out D ignores the description of ratio scales, which is also a true and important part of the measurement framework. Because it fails to include all true statements, this combination is incomplete and cannot be accepted.

Option C:
Option C groups A, C and D but leaves out B. Although A, C and D are correct, B is also a correct statement describing the ordinal scale. Excluding B makes the picture of measurement scales incomplete, so this option does not fully answer the question.

Option D:
Option D combines B, C and D with E. While B, C and D are correct, inclusion of E makes the whole combination incorrect, as it wrongly suggests that nominal scale data support meaningful addition and subtraction. A valid combination must not contain any false statements, so this option is not acceptable.

External validity refers to how far the conclusions drawn from a specific study can be applied beyond the particular participants, conditions and time frame of that study. It addresses questions such as whether results would hold in different institutions, regions or age groups. High external validity increases the practical usefulness of research findings for policy and practice. Since the stem focuses on generalisation to other populations, settings or times, external validity is the correct term.

Option A:
Internal validity deals with ruling out alternative explanations within the study so that causal inferences are sound, but it does not by itself guarantee that findings generalise. A study may be internally valid yet externally limited if the sample or context is narrow. Therefore, internal validity does not match the emphasis in the stem.

Option B:
Construct validity concerns whether the research appropriately operationalises and measures the theoretical constructs involved. While it contributes to meaningful interpretation, it is not primarily about generalisability across contexts. Hence, construct validity is not the right completion here.

Option C:
Content validity focuses on whether an instrument adequately represents the relevant content domain or syllabus, which is a different issue from applying results to new settings or populations. Thus, content validity is not the term being defined in the question.

Option D:
External validity can be enhanced by using diverse samples, realistic settings and replication across contexts. These strategies help ensure that the observed relationships are not peculiar to one specific group or environment, aligning with the stem’s focus on generalisation.

Plagiarism is an unethical practice in which someone uses another person’s ideas, data or wording without giving due credit through proper citation or quotation. It violates academic integrity and misleads readers about the true source of the work. Institutions often have strict policies and penalties for plagiarism because it undermines trust in scholarship. Since the stem describes presenting others’ work as one’s own without acknowledgment, plagiarism is the correct term.

Option A:
Ethical writing requires acknowledging all sources that have influenced the work, using quotation marks for exact wording and providing references for paraphrased ideas. It is the opposite of plagiarism because it is based on honesty and transparency. Therefore, ethical writing cannot complete the stem, which focuses on unethical copying.

Option B:
Paraphrasing involves restating someone else’s ideas in one’s own words, but it still requires citation to credit the original author. Properly paraphrased and referenced material is acceptable academic practice. However, the stem refers to using others’ work without acknowledgment, which goes beyond paraphrasing and constitutes plagiarism, so paraphrasing is not the correct answer.

Option C:
Citation is the practice of giving credit to original authors by indicating sources in the text and in the reference list. Correct use of citations prevents plagiarism by making sources explicit. Because the stem describes absence of acknowledgment, citation is actually the safeguard against the problem, not the name of the unethical act itself.

Option D:
Plagiarism covers both direct copying and close paraphrasing without citation, and can involve text, data, images or ideas. It is considered a serious academic offence and may lead to retraction of publications or disciplinary action. These features fit perfectly with the behaviour described in the stem, confirming plagiarism as the appropriate completion.

Qualitative research focuses on exploring how people interpret and make sense of their experiences, social roles and interactions. It typically uses data in the form of words, images or observations rather than numbers. Methods such as interviews, focus groups and participant observation are common in this approach. Because the stem emphasises understanding meanings, experiences and social processes through non-numerical data, it clearly refers to qualitative research.

Option A:
Qualitative research aims at depth of understanding rather than breadth of coverage, often studying fewer cases in greater detail. It allows for rich descriptions and the development of concepts grounded in participants’ perspectives. These features match the emphasis in the stem on meanings and experiences, so qualitative is the correct completion.

Option B:
Experimental research involves manipulating an independent variable under controlled conditions to examine its effect on a dependent variable, relying heavily on numerical data and statistics. While it can include qualitative components, it is not defined by non-numerical data and meaning-focused analysis. Thus, experimental is not the best description here.

Option C:
Correlational research examines the statistical relationships between variables without manipulating them, often using numerical data and correlation coefficients. Its primary concern is the strength and direction of associations rather than deep exploration of meanings. Therefore, correlational research does not fit the definition in the stem.

Option D:
Ex post facto research looks for possible causes after an effect has already occurred, usually using existing groups and quantitative measures. Although it may complement qualitative work, it is not inherently about understanding meanings through non-numerical data. Hence, ex post facto is not the appropriate answer.

Statements A, B, C and D distinguish sampling error from non-sampling error, and F highlights a way to reduce some non-sampling problems. A is true because sampling error results from studying only a subset, while B correctly notes that poor instruments create non-sampling error. C is accurate in classifying non-response bias as a non-sampling error, and D correctly observes that larger samples usually reduce sampling error. F is also true since properly trained field staff can lower interviewer and recording errors, whereas E is false because random sampling addresses sampling error, not all forms of non-sampling error.

Option A:
Option A ignores statement F, which draws attention to practical steps for reducing non-sampling error through training. While A, B, C and D are correct, the omission of F makes the description less complete in terms of remedies. Thus, this option is not fully correct.

Option B:
Option B adds statement E, falsely claiming that non-sampling errors can be entirely removed by random sampling. Random sampling influences only sampling error; measurement, non-response and recording errors can still occur. Because it includes a false statement, this option is invalid.

Option C:
Option C is correct because it lists A, B, C, D and F, capturing both definitions and some methods for error reduction. It properly excludes E, which overstates the power of random sampling. Therefore, this combination contains all and only the true statements.

Option D:
Option D omits statement A and includes E, thereby losing the basic definition of sampling error and adding an incorrect assertion. This combination confuses the distinction between error types and cannot be accepted.

Referencing is the practice of formally acknowledging the ideas, words or data taken from other authors by providing complete details of the sources used. It typically follows specific styles such as APA, MLA or Chicago, which standardise how author names, publication years, titles and other information are presented. Proper referencing allows readers to locate the original sources and shows respect for intellectual property. Because the stem describes writing author, year and source details in a standard manner to acknowledge sources, it is clearly referring to referencing.

Option A:
Plagiarism occurs when someone uses another person’s work without proper acknowledgement, effectively presenting it as their own. It is the opposite of correct referencing, which aims to avoid plagiarism by giving credit to original authors. Since the stem is about acknowledging sources rather than misusing them, plagiarism is not the correct answer.

Option B:
Referencing demonstrates academic honesty and enables critical readers to verify claims, follow up on original studies and assess the reliability of the evidence cited. It is an integral part of scholarly communication and is governed by detailed formatting rules. The description in the stem, involving standardised author and year details, matches this practice exactly, so referencing is the appropriate choice.

Option C:
Sampling deals with selecting a subset of individuals or elements from a population for the purpose of data collection. It concerns who is studied, not how sources are cited in a written report. Therefore, sampling does not fit the context of acknowledging sources.

Option D:
Coding refers to assigning numerical or symbolic codes to responses or data categories for ease of analysis. While coding is crucial in data processing, it is distinct from the documentation practice of listing authors and sources. Hence, coding is not an accurate completion for the stem.