UGC NET Questions (Paper – 1)

Statements A, B, C and E accurately describe the roles of research questions and hypotheses. Questions express what is to be explored, hypotheses offer testable predictions, and both should be consistent with stated objectives. Qualitative studies often keep questions more open and evolving. Statement D is false because some studies, particularly qualitative or exploratory ones, may use research questions without formal hypotheses, so both need not always appear together.

Option A:
Option A is incomplete because it omits statement E, overlooking the flexibility of research questions in qualitative research. Without E, the option does not fully reflect differences in how questions and hypotheses function across methodologies.

Option B:
Option B includes B, C and E but leaves out A, thereby failing to indicate that research questions themselves articulate the core focus of the study. This omission weakens the explanation of how inquiry is framed.

Option C:
Option C contains A, C and E but omits B, so it does not mention that hypotheses provide testable predictions. As a result, the description of how hypotheses connect to questions and objectives is incomplete.

Option D:
Option D is correct because it encompasses all four true statements and excludes D, which wrongly insists that questions and hypotheses must always appear together. It therefore captures the flexible use of these elements in different research traditions.

Statements A, B, C and E correctly outline the nature and importance of coding and classification. Coding converts raw responses into analysable symbols, schemes should cover all possibilities without overlap, open-ended responses often need inductively developed categories and careful coding minimises error. Statement D is false because altering answers to achieve symmetry violates data integrity and leads to misleading results. Therefore, the combination including A, B, C and E but excluding D is correct.

Option A:
Option A includes A, B and C but omits E, thereby failing to state explicitly that precise coding contributes to error reduction and accurate analysis. Without E, the rationale for investing effort in careful coding is not fully conveyed.

Option B:
Option B contains B, C and E but leaves out A, so it does not mention the fundamental step of assigning symbols or numbers to responses. This omission makes the description of coding incomplete, even though the other points are true.

Option C:
Option C is correct because it brings together all four true statements, emphasising both the procedures and the benefits of systematic coding and classification. It rightly excludes D, which suggests unethical alteration of responses.

Option D:
Option D includes A, C and E but omits B, ignoring the requirement that categories be exhaustive and mutually exclusive. Without that requirement, coding categories could overlap or leave gaps, so this option is incomplete.

The median is the middle value of an ordered distribution such that half of the observations fall below it and half above it. It is particularly useful for skewed distributions or data with extreme values, because it is less affected by outliers than the mean. For ordinal data, the median is often the most appropriate measure of central tendency. Therefore, the measure that divides the distribution into two equal halves is correctly called the median.

Option A:
The mean is the arithmetic average obtained by summing all scores and dividing by the number of observations. While widely used, it is sensitive to extreme values and does not necessarily separate the distribution into two equal halves. Hence, mean is not the correct measure described in the stem.

Option B:
The mode is the value that occurs most frequently in a distribution and can be used for nominal, ordinal or interval data. A distribution may have multiple modes or none clearly defined, and the mode does not guarantee that 50% of cases lie above and 50% below. Thus, mode is not the best completion.

Option C:
Standard deviation measures the average deviation of scores from the mean and expresses the spread or variability in the data, not its central location. It does not divide the distribution into equal halves and therefore does not fit the description given in the question.

Option D:
The median’s reliance on the positional middle of ordered scores, rather than on mathematical averaging, ensures that it always splits the distribution into two equal parts, which matches the stem exactly.

Statistical power quantifies the sensitivity of a test to identify real effects when they exist. It is mathematically equal to one minus beta, where beta is the probability of a Type II error. High power means that the test is unlikely to miss true differences or relationships. Therefore, the probability described in the stem is called the power of the test.

Option A:
Precision refers to how close repeated estimates are to one another, such as narrow confidence intervals, but it is not the standard term for the probability of detecting true effects.

Option B:
Power is influenced by factors such as sample size, effect size, significance level and variability in the data. Researchers often conduct power analyses while designing studies to ensure that they have a reasonable chance of finding meaningful effects. This focus on correctly rejecting false null hypotheses matches the stem, making this option correct.

Option C:
Reliability concerns the consistency of measurements over time or across items and is a property of instruments, not directly of hypothesis tests.

Option D:
Validity relates to the appropriateness of inferences drawn from test scores or study designs; it does not specifically denote the probability of detecting true effects in statistical testing.

Action research is conducted by practitioners such as teachers or administrators who systematically inquire into their own practice to bring about improvement. It typically involves repeated cycles of planning an intervention, acting by implementing it, observing the outcomes and reflecting on the results before planning the next cycle. This close link between inquiry and action makes the research highly context-specific and directly relevant to practice. Because the stem describes a cyclic process of plan, act, observe and reflect to improve practice, action research is the correct term.

Option A:
Basic research aims to develop or refine theory without a specific focus on immediate practical change in a local setting. It is usually carried out by researchers rather than by front-line practitioners, so it does not match the cyclic, practitioner-centred process described in the stem.

Option B:
Action research empowers practitioners to become researchers of their own contexts, fostering reflective practice and ongoing professional development. Each cycle generates insights that can be quickly applied and tested again, which aligns perfectly with the stem’s emphasis on continuous improvement.

Option C:
Survey research collects data from samples using questionnaires or interviews to describe trends, attitudes or relationships at a broader level. It does not inherently involve cycles of action and reflection aimed at improving the researcher’s own practice, so survey research is not suitable here.

Option D:
Historical research analyses past events and trends using documents and records to understand how current conditions developed. It is not organised as a cyclical intervention process in the present, so historical research cannot complete the stem correctly.

A control group provides a baseline against which the effects of the experimental treatment can be compared. By keeping conditions for this group similar to those of the experimental group except for the treatment, researchers can attribute observed differences in outcomes more confidently to the intervention itself. Control groups are central to establishing internal validity in experimental designs. Thus, the group described in the stem is correctly called a control group.

Option A:
Control groups may receive no treatment, standard treatment or a placebo, depending on the study, but their key role is to mirror the experimental conditions minus the active ingredient being tested. This comparative function aligns exactly with the stem, making this option correct.

Option B:
A placebo group receives an inert or sham treatment and is one specific type of control group, particularly in clinical trials, but the more general term used in basic experimental design is control.

Option C:
Random describes a procedure for assignment or selection, not a type of group, so it does not fit the blank, which asks about the role of a group.

Option D:
Matched groups are formed by pairing participants on key variables before assigning them to conditions, which is a method of forming comparable groups but not the standard name of the comparison group itself.

Triangulation involves combining different methods, sources, investigators or theoretical perspectives to cross-check findings. When similar results emerge from diverse lines of evidence, confidence in the conclusions increases. This strategy is especially valued in qualitative research but can be used in mixed methods as well. Thus, the practice described in the stem is called triangulation.

Option A:
Standardisation refers to applying consistent procedures and instructions across participants, which reduces variability but does not necessarily involve multiple sources of evidence.

Option B:
Randomisation is a technique for assigning participants to groups or selecting samples by chance to control bias; it is distinct from corroborating results with multiple approaches.

Option C:
Triangulation might include, for example, combining interviews, observations and document analysis to study classroom interaction. If all three sources point to similar themes, the findings are considered more credible. This multi-angle confirmation is exactly what the stem highlights, making this option correct.

Option D:
Stratification refers to dividing a population into subgroups for sampling and does not inherently involve using multiple methods to study the same phenomenon.

Statements A, B, C and E correctly define important kinds of variables in research, while statement D is wrong. Statement A is true because the independent variable is manipulated or selected by the researcher to see its effect on the dependent variable. Statement B correctly identifies the dependent variable as the outcome that is expected to change when the independent variable is varied, and statement C accurately describes extraneous variables as influences on the dependent variable that are not of primary interest. Statement E is also correct since moderator variables interact with the independent and dependent variables to modify the strength or direction of their relationship, whereas statement D is false because control of extraneous variables is important irrespective of sample size.

Option A:
Option A treats both B and D as wrong, but this is not accurate. While D is indeed incorrect, B is a correct description of the dependent variable as the outcome that responds to changes in the independent variable. By misclassifying a true statement as wrong, this option fails to identify the wrong statement set properly and therefore cannot be the correct answer.

Option B:
Option B classifies C and D as wrong statements. Although D is wrong, C is correct because extraneous variables are exactly those variables that may unintentionally influence the dependent variable even though they are not the main focus of the study. Since this option marks a correct statement as wrong and omits the correct status of A, B and E, it is invalid.

Option C:
Option C combines A, B and D and implicitly treats all three as wrong. Both A and B are actually accurate definitions of independent and dependent variables, respectively. Including these correct statements with D, which is wrong, leads to an incorrect grouping of wrong statements, so this option cannot be chosen.

Option D:
Option D correctly identifies D as the only wrong statement. It recognises that even large samples cannot substitute for proper control of extraneous variables because uncontrolled influences can still bias results. By leaving A, B, C and E untouched as correct statements and singling out D, this option best answers the instruction to select the wrong statements.

Primary data are original information gathered firsthand by the researcher through methods such as surveys, experiments, observations or interviews. They are tailored to address the specific objectives and context of the current study. Because they are collected directly, issues of relevance and accuracy can be handled during the data collection process. Therefore, data collected specifically for the present investigation are correctly called primary data.

Option A:
Secondary data are information that already exist, having been collected by others for different purposes, such as government reports or previous research. While they may be reused, they are not gathered directly by the researcher for the current study, so secondary is not the correct completion.

Option B:
Primary data have the advantage of greater control over measurement procedures, timing and sampling, which can enhance validity and reliability when collected carefully. However, they are also more costly and time-consuming to obtain. These features match the description in the stem of data collected directly by the researcher.

Option C:
Documentary data are usually drawn from written records such as reports, letters or institutional documents and may be either primary or secondary depending on their origin and use. The term does not specifically denote direct collection for a particular study. Hence, documentary is not the best answer.

Option D:
Archival data refer to stored records or datasets, often created in the past, which researchers may access as secondary sources. They are not necessarily collected specifically for the current investigation, so archival is not the appropriate term here.

Inductive reasoning involves collecting specific observations or empirical findings and then inferring broader patterns, regularities or theoretical propositions from them. It is central to exploratory and qualitative research, where theory often emerges from the data rather than being imposed beforehand. While inductive conclusions are not logically certain, they can be strongly supported by the weight of evidence. Therefore, the specific-to-general logic described in the stem is accurately termed inductive reasoning.

Option A:
Hypothetico-deductive reasoning combines elements of deduction and induction by formulating hypotheses from theory and then testing them, but its core movement is from general hypotheses to specific predictions. It does not start primarily from specific observations, so it is not the best completion.

Option B:
Deductive reasoning proceeds from general principles to specific conclusions and is typically used to derive hypotheses from theory for testing. This is the reverse direction from what the stem describes, so deductive reasoning is not correct here.

Option C:
Inductive reasoning often involves iterative comparison of new observations with emerging categories, leading to increasingly refined concepts and theoretical explanations. This approach is widely used in grounded theory and other qualitative methodologies, aligning closely with the description in the question.

Option D:
Intuitive reasoning relies on immediate insight or “gut feeling” rather than on systematic accumulation and analysis of specific observations. Although intuition may play a role in research creativity, it does not describe the systematic logical process outlined in the stem, so intuitive reasoning is not appropriate.

A ratio scale possesses all the properties of an interval scale—equal units and meaningful differences—plus a true zero point that indicates the complete absence of the quantity being measured. Because of the true zero, statements such as one value being twice another are meaningful. Examples include height, weight and Kelvin temperature. Thus, a scale with equal units and a true zero that allows ratio comparisons is correctly called a ratio scale.

Option A:
Nominal scales classify objects into categories without any inherent order, such as gender or blood group. They do not have equal units or a zero point with quantitative meaning, so they do not match the stem.

Option B:
Ordinal scales rank order items but do not guarantee equal intervals between ranks; they allow comparison of “greater than” or “less than” but not meaningful differences or ratios. Hence, ordinal is not the right answer.

Option C:
Interval scales have equal units and allow meaningful differences to be calculated, but they lack a true zero, so ratios can be misleading. Celsius temperature is a classic example. Therefore, interval does not fulfil the true-zero condition in the stem.

Option D:
Option D, ratio scale, uniquely combines equal intervals with an absolute zero, enabling multiplication and division operations that are not valid on other scales. This matches the description in the question precisely.

Statements D and E are wrong because they encourage hiding or rejecting studies with limitations, which is contrary to ethical and scholarly norms. All empirical studies have some limitations, and acknowledging them improves transparency and helps readers interpret results appropriately. In contrast, A, B and C correctly describe what delimitations and limitations are and why they should be reported. Thus, only the combination containing D and E as wrong statements is correct for this question.

Option A:
Option A is incorrect because it identifies only D as wrong and ignores E, which falsely suggests that studies with limitations should not be conducted or reported. Both D and E misrepresent how scholarly work is evaluated.

Option B:
Option B is wrong because it treats only E as incorrect, overlooking the harmful recommendation in D to avoid mentioning limitations. In reality, concealing limitations weakens rather than strengthens research.

Option C:
Option C is incorrect because it also labels C as wrong, even though reporting delimitations and limitations is central to helping readers judge the scope of findings. Misclassifying C undermines good reporting practice.

Option D:
Option D is correct because it groups D and E, the only two statements that contradict responsible research practice, and implicitly affirms that A, B and C are accurate descriptions.

A research problem expresses, in a focused statement, the issue, difficulty or gap in existing knowledge that the study aims to investigate. It provides the starting point for all other decisions in the research process, such as objectives, hypotheses and design. A well-formulated research problem is clear, specific and researchable. Because the stem refers to a clearly defined statement indicating the specific issue or gap to be addressed, it is describing the research problem.

Option A:
Objectives of the study specify what the researcher intends to achieve, often derived from the research problem. They translate the problem into concrete aims or tasks. However, they are not the statement of the problem itself, so objective of the study is not the correct completion here.

Option B:
The research problem succinctly captures what is not known, what contradiction exists or what practical difficulty needs systematic inquiry. It directs the review of literature and formulation of hypotheses. Since the question emphasises a statement about the specific issue or gap, research problem is the most accurate term.

Option C:
A hypothesis is a tentative, testable proposition about relationships between variables formulated after the problem has been identified and literature has been reviewed. It is one possible answer to the problem, not the problem statement itself. Therefore, hypothesis cannot complete the stem correctly.

Option D:
Delimitations refer to the boundaries that the researcher consciously sets for the study, such as restricting it to a particular group or area. They explain what the study will not cover, but they do not define the core issue or gap to be investigated. Hence, delimitations is not the right answer.

Statements A, B, C and E are correct because rating scales can measure attitudes or performance, halo effect reflects spill-over of overall impressions, leniency/severity errors indicate consistent over- or under-rating and training with clear criteria can improve reliability. Statement D is false as avoiding rater training is likely to increase bias rather than reduce it. Thus, the combination that includes A, B, C and E and excludes D is correct.

Option A:
Option A is incomplete because it leaves out E, ignoring the important role of clear criteria and training in improving rating quality. Without E, strategies for reducing errors remain underemphasised.

Option B:
Option B is wrong because it omits A and includes only B, C and E. While those three are true, leaving out A fails to mention the broad applicability of rating scales to attitudes, behaviours or performance.

Option C:
Option C is correct as it gathers all four accurate statements about what rating scales can assess, the nature of halo and leniency/severity errors and the value of training and criteria. It appropriately rejects D, which wrongly discourages rater training.

Option D:
Option D is incorrect because it treats D as acceptable by including it in the combination and omits B and C, thus downplaying well-established rater errors. Including D means endorsing a practice that would likely increase bias.

The Likert scale is a widely used attitude measurement technique in which respondents indicate their degree of agreement or disagreement with statements, typically on a five- or seven-point ordered scale. Responses such as “strongly agree,” “agree,” “neutral,” “disagree” and “strongly disagree” are assigned numerical values that can be summed to yield an overall attitude score. Although the data are ordinal, researchers often treat them as approximately interval for many statistical analyses. Because the stem clearly describes an ordered agreement continuum used to measure attitudes, this is known as a Likert scale.

Option A:
Semantic differential scales present bipolar adjective pairs (such as good–bad, strong–weak) and ask respondents to indicate their position between the two on a series of semantic dimensions. While they also use ordered response categories, they focus on underlying connotative meanings rather than levels of agreement with statements. Thus, semantic differential is not the best match for the scale described in the stem.

Option B:
Guttman scales, also called cumulative scales, arrange items so that agreement with a stronger statement implies agreement with all weaker statements in the set. They require a specific hierarchical structure among items, which is not implied by the simple agreement continuum in the question. Therefore, Guttman is not the correct answer.

Option C:
Thurstone scales rely on expert judges to assign scale values to attitude statements, which are then presented to respondents who indicate which statements they agree with. The scoring procedure differs significantly from the straightforward ordered agreement options mentioned in the stem. As such, Thurstone scaling is not the appropriate label here.

Option D:
Likert scales are favoured for their simplicity in construction and administration, requiring only statements and ordered response alternatives. Because the stem focuses on measuring attitudes via agreement levels on a response continuum, the term Likert scale accurately completes the statement.