UGC NET Questions (Paper – 1)

Research questions express the core curiosities of the study in the form of questions that can be answered through systematic investigation. They help to sharpen the focus of the research and indicate the information that needs to be collected. Well-phrased research questions are clear, concise and aligned with the overall problem. Hence, interrogative statements specifying what the researcher wants to know are rightly termed research questions.

Option A:
Option A refers to questions, which are naturally expressed in interrogative form such as “What is…?” or “How does…?”. They function as the guiding inquiries that the study seeks to address through data collection and analysis. Because the stem emphasises interrogative statements about the problem, this option fits exactly.

Option B:
Objectives are declarative, not interrogative, and describe what the researcher will do rather than the question being asked. Although closely related, they are conceptually distinct from research questions. Therefore, objectives are not the best completion here.

Option C:
Variables are characteristics that can take different values and are measured or manipulated in the study. They do not, by themselves, specify what the researcher wants to know in interrogative form. Hence, variables do not match the stem.

Option D:
Limitations refer to constraints or weaknesses in the study’s design or execution, such as time or access restrictions. They are reported for transparency but do not state the central inquiries of the research, so they are not appropriate here.

A census involves complete enumeration of all units in the target population, leaving no element unobserved. Because every member is included, estimates derived from a census are, in principle, free from sampling error, although other types of error may still occur. Censuses are often costly and time consuming but useful when the population is small or very important decisions depend on accurate counts. Thus, a study that covers every member of the population is correctly called a census.

Option A:
A case study is an in-depth investigation of a single case or a small number of cases and does not imply inclusion of all units in a population. It is qualitative in orientation and not equivalent to full enumeration.

Option B:
A pilot study is a small-scale trial run conducted to test the feasibility of instruments and procedures before the main study. It typically uses only a limited number of units, not the entire population.

Option C:
A sample survey examines only a subset of the population and then generalises findings, so it explicitly contrasts with a census. Because it does not include all members, it cannot complete the stem.

Option D:
Census refers specifically to complete coverage, such as national population censuses. This matches the description of collecting data from every member, making this option correct.

A directional hypothesis predicts the specific direction of an expected relationship or difference, such as stating that Method A will produce higher achievement than Method B. It leads to one-tailed statistical tests because the researcher is interested in deviations in a particular direction. This type of hypothesis is based on prior theory or evidence suggesting how results should differ. Since the stem mentions a hypothesis that specifies both existence and direction of difference, directional hypothesis is the correct term.

Option A:
A null hypothesis asserts that there is no significant difference or relationship between variables in the population. It does not predict a direction and often serves as the default assumption in significance testing. Therefore, null hypothesis does not fit the description in the question.

Option B:
Non-directional hypotheses merely state that a difference or relationship exists without indicating which group will score higher or what the direction of association will be. They correspond to two-tailed tests that consider both positive and negative differences. Because the stem emphasises specifying direction, non-directional hypothesis is not appropriate.

Option C:
A directional hypothesis can be more powerful statistically when correctly specified, but it also carries risk if the true effect is opposite in direction. It is justified when prior research strongly suggests a particular outcome. These features align closely with the stem’s description, so directional hypothesis is the right completion.

Option D:
A working hypothesis is a tentative assumption that guides the early stages of research and may or may not be directional. It is a broader term and does not necessarily indicate the explicit direction requirement highlighted in the stem. Hence, working hypothesis is not the best answer.

Information and Communication Technology (ICT) tools such as bibliographic databases, search engines and reference managers greatly assist researchers in conducting a systematic review of literature. They enable efficient searching of large bodies of published work, retrieval of full texts and organised storage of citations and notes. This support makes it easier to map existing knowledge, identify gaps and prepare coherent literature review sections. Hence, the activities described in the stem clearly represent ICT use for review of literature.

Option A:
Data analysis typically involves processing and interpreting collected data using statistical software, coding schemes or qualitative analysis tools. While ICT can assist here as well, the stem focuses on identifying and organising published sources rather than analysing study data. Therefore, data analysis is not the best completion.

Option B:
Hypothesis testing refers to using statistical or logical procedures to determine whether data support or refute proposed hypotheses. Citation management software does not directly test hypotheses; it helps manage references. Since the stem is about finding and organising literature, hypothesis testing is not the correct option.

Option C:
Review of literature requires systematically locating, screening and synthesising previous studies and theoretical works relevant to a research problem. ICT tools greatly streamline this process by allowing targeted searches and structured citation storage. For this reason, review of literature is the right term to complete the sentence in the question.

Option D:
Sampling concerns the selection of participants or units from a population for data collection, often supported by statistical reasoning. Bibliographic databases and citation managers are not primarily used for choosing samples of individuals for a study. Thus, sampling does not fit the activities mentioned in the stem.

Statements A, B, C and D correctly describe the four classical levels of measurement. Nominal scales simply classify, ordinal scales rank, interval scales provide equal units without a true zero and ratio scales include both equal intervals and an absolute zero. Statement E is false because nominal and ordinal data can still be analysed using appropriate statistics such as frequencies, medians and non-parametric tests. Therefore, the set containing A, B, C and D only is the correct combination.

Option A:
Option A includes A, B and C but omits D, thereby leaving out the crucial description of ratio scales, which are widely used for variables like height, weight or test scores with an absolute zero. Because it fails to list all the true statements, this option is incomplete.

Option B:
Option B is correct because it includes all four accurate descriptions of measurement scales and excludes E, which incorrectly claims that nominal and ordinal data are unusable in statistics. It gives a comprehensive and standard account of measurement levels.

Option C:
Option C includes E and omits A, which is a correct description of nominal scales. Since E is false and A is true, this combination misclassifies statements and cannot be accepted.

Option D:
Option D wrongly adds E, the false claim about unsuitability for analysis, and omits B, which correctly explains ordinal scales. Mixing a false statement with true ones and omitting a correct one makes the option invalid.

Option E includes E and leaves out C, neglecting the proper description of interval scales and accepting an incorrect restriction on nominal and ordinal data. Hence this option cannot be considered correct.

The dependent variable is the observed outcome whose values are expected to change in response to manipulation of the independent variable. It is “dependent” because its variation is presumed to be influenced by changes in the treatment conditions. Researchers measure this variable to test whether the intervention has made a difference. Hence, the variable measured to assess the treatment effect is correctly named the dependent variable.

Option A:
The independent variable is manipulated rather than measured as an outcome, so it does not align with the stem’s emphasis on measurement to assess effects. It functions as the presumed cause rather than the observed result.

Option B:
Option B, dependent variable, emphasises that this variable depends on the levels of the independent variable in the experimental design. Outcome indicators such as test scores, attitudes or performance measures are typical examples. This matches the description in the question, making this option correct.

Option C:
A moderator variable affects the strength or direction of the relation between independent and dependent variables but is not itself the main outcome being measured. It plays a more complex role and thus does not fit the stem.

Option D:
A control variable is held constant to prevent it from interfering with the interpretation of results. It is not the primary outcome of interest, so it is not the correct choice here.

A ranking scale requires respondents to place items in a preferred order, such as first, second, third and so on. This produces ordinal data because the order is meaningful but the exact differences between ranks are not known. Ranking scales are useful when researchers want relative preferences among several alternatives. Because the stem emphasises arranging items in order of preference, a ranking scale is the correct answer.

Option A:
Nominal scales only classify items into categories without any inherent order, such as types of institution or subject specialisation. They do not capture preference ranking or ordering among categories. Therefore, a nominal scale does not correspond to the procedure described in the question.

Option B:
Rating scales ask respondents to indicate the degree of agreement, satisfaction or intensity on a continuum, such as from “strongly agree” to “strongly disagree.” Ratings express intensity rather than an ordered list of multiple items relative to each other. Hence, rating is not the best completion for the stem.

Option C:
A ranking scale records positions of items like most preferred to least preferred, allowing the researcher to see relative standing among options. This is different from measuring equal intervals or absolute amounts. Since the question describes ordering items by preference, ranking scale correctly captures the intended idea.

Option D:
An interval scale assumes equal distances between successive points and enables meaningful addition and subtraction, such as in temperature measurement. It is not defined by asking respondents to order a list of options. Therefore, interval is not the appropriate term here.

Statements A, B, C, D and F correctly describe threats to internal validity, while E is false. Statement A is true because events between measurements can influence outcomes. Statement B is correct as maturation refers to participant changes over time, and statement C rightly defines the testing effect. Statement D accurately explains instrumentation changes, and statement F is true for attrition or mortality, whereas E is false because random assignment is a control technique that strengthens, not threatens, internal validity.

Option A:
Option A includes all the genuine threats named (A, B, C, D and F) and excludes E, which misinterprets the role of random assignment. It thus presents a complete and accurate set of internal validity threats mentioned in the list. Therefore this option is the correct answer.

Option B:
Option B leaves out F, ignoring the issue of differential dropout across groups. Since attrition can seriously bias results, omitting it gives an incomplete picture of internal validity threats. As a result, this combination is not acceptable.

Option C:
Option C contains E in addition to B, C, D and F, thereby treating random assignment as a threat. This is incorrect because random assignment is used to control extraneous variables and enhance internal validity. Including a false statement disqualifies this option.

Option D:
Option D omits B, which is an important threat related to participant changes, and therefore fails to recognise the full range of threats in the list. By leaving out a true statement, it offers only a partial account and cannot be considered correct.

Statements D and E are wrong because observation can certainly be combined with interviews and documents in mixed-methods or triangulated designs, and ethical principles apply strongly to observation, especially regarding privacy. Statements A, B and C correctly distinguish participant, non-participant and structured observation. Thus, the only correct answer is the option that lists D and E as the wrong statements.

Option A:
Option A is incorrect because it isolates D alone as wrong and overlooks E, which mistakenly claims that ethics are irrelevant in observation. Both D and E contradict accepted research practice.

Option B:
Option B is correct because it identifies exactly those statements that conflict with methodological and ethical guidance, while affirming A, B and C as accurate descriptions of observation types.

Option C:
Option C is wrong because it also treats C as incorrect, even though structured observation does rely on predefined categories and checklists. Misclassifying C undermines understanding of structured approaches.

Option D:
Option D is incorrect because it labels B, which correctly defines non-participant observation, as wrong along with D and E. This misrepresents a basic distinction in observational roles.

Multiple correlation refers to the degree of association between a single criterion variable and several predictor variables considered simultaneously. It is summarised by the multiple correlation coefficient R, which indicates how well the combined predictors explain variance in the criterion. This is different from simple correlation, which involves only two variables. Because the stem mentions correlation between one variable and a combination of two or more predictors, multiple correlation is the correct term.

Option A:
Partial correlation measures the relationship between two variables while statistically controlling for the effect of one or more additional variables. It does not directly express the combined predictive power of several variables on one criterion as multiple correlation does. Thus, partial is not the appropriate completion.

Option B:
Simple correlation, often denoted by r, captures the linear relationship between two variables at a time. It does not account for the simultaneous effects of multiple predictors on a single outcome. Since the stem explicitly refers to a combination of predictors, simple correlation is not correct.

Option C:
Multiple correlation summarises how well a set of predictors, such as intelligence, study hours and motivation, explain a criterion like academic achievement. A high R value indicates strong combined prediction. This conception aligns closely with the stem’s description, confirming multiple as the right answer.

Option D:
Serial correlation is often used to describe correlations between observations ordered in time, such as successive scores in a time series, and is not directly about combining predictors for a single criterion. Hence, serial is not suitable here.

Statements A, B, C and F correctly describe primary and secondary data, while D and E are wrong. Statement A is true because primary data are gathered first-hand for the current study. Statement B correctly defines secondary data, and statement C rightly lists typical secondary sources. Statement F is also true since many studies combine both data types, whereas D is false because secondary data can be current and useful and E is false because primary collection is often more costly and time-consuming.

Option A:
Option A marks only D as wrong, but E is also incorrect. Although D wrongly dismisses secondary data as always outdated, E wrongly claims that primary data are always cheaper and quicker. By failing to include E, this option does not fully represent the set of wrong statements.

Option B:
Option B correctly identifies D and E as the wrong statements while leaving A, B, C and F unchallenged as correct. It recognises that secondary data can be valuable and that primary collection may be resource-intensive. Because it includes all and only the wrong statements, this option is the correct answer.

Option C:
Option C chooses only E as wrong and ignores D, thereby overlooking a major misconception about secondary data. Since the question asks for all wrong statements, omitting D makes this combination incomplete.

Option D:
Option D wrongly labels C as part of the wrong set along with D and E. Statement C is accurate, as official documents and published research often serve as secondary sources. Misclassifying a correct statement as wrong renders this option invalid.

Random assignment is the process of allocating participants to experimental and control groups purely by chance, such as by using random numbers or drawing lots. It ensures that, on average, the groups are equivalent with respect to both known and unknown variables before the treatment is applied. This greatly strengthens internal validity by reducing selection bias. Because the stem highlights equal and independent chances of being placed in any group, random assignment is the correct term.

Option A:
Random assignment ensures that individual differences are distributed approximately evenly across groups, so observed post-treatment differences are more likely due to the intervention. It is a defining feature of true experimental designs. This role matches the stem’s description, confirming random assignment as the appropriate completion.

Option B:
Matching involves pairing participants who are similar on certain variables and then assigning them to different groups, which partially controls for those variables but does not guarantee equal independent chance of group placement for all participants. Thus, matching is not the correct answer.

Option C:
Blocking is used to control for the effects of one or more nuisance variables by forming homogeneous blocks and then randomly assigning treatments within each block, especially in agricultural or industrial experiments. While it involves randomisation, the stem’s focus on general equal and independent chances for all participants is more directly captured by random assignment.

Option D:
Clustering usually refers to grouping participants based on natural units such as classes or schools in sampling or design and does not necessarily imply random allocation of individuals to treatment conditions. As such, clustering is not the proper term for the procedure described.

Statements A, B and C accurately define control, moderator and mediator variables, and F adds an important implication. A is true because researchers hold control variables constant to prevent them from confounding the relationship between independent and dependent variables. B correctly states that moderator variables change the strength or direction of this relationship, and C is right that mediators explain how or why an effect occurs. F is also true since identifying such variables helps develop more refined theoretical models, whereas D and E are false because not all extraneous variables are mediators and moderators are often crucial in educational research.

Option A:
Option A omits statement F and therefore fails to acknowledge how identifying mediators and moderators enriches theoretical understanding. Although A, B and C are correct, leaving out F means the combination does not include all the true statements identified in the item. Consequently, it is incomplete.

Option B:
Option B wrongly includes statement D, which asserts that all extraneous variables are mediators. Many extraneous variables do not function as mediators in a causal chain; they may simply add noise or bias. By grouping D with otherwise correct statements, this option becomes conceptually inaccurate.

Option C:
Option C is correct because it selects statements A, B, C and F, all of which align with standard definitions and implications in research design. It excludes D and E, which misrepresent extraneous variables and the relevance of moderators. Therefore this combination contains all and only the true statements.

Option D:
Option D omits statement A and includes D, making it doubly problematic. It loses the essential notion of a control variable and adopts the false idea that all extraneous variables are mediators. Such a combination cannot be considered correct.

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

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

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

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

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

Statements A, B, C and E correctly describe the role of indexing and abstracting services in improving discoverability. Indexing connects researchers to relevant articles, abstracts summarise content, prestigious databases raise journal visibility and carefully chosen keywords make retrieval easier. Statement D is false because such services generally expand and organise access to literature rather than restricting it. Therefore, the combination that includes A, B, C and E and excludes D is correct.

Option A:
Option A is incomplete because it omits E, failing to mention the influence of author-selected keywords on search results. Without E, the active role of researchers in enhancing discoverability is underemphasised.

Option B:
Option B is correct as it unites the four true statements and rejects D, which wrongly advises avoiding indexing and abstracting services. It reflects how these tools support literature search and citation impact.

Option C:
Option C is wrong because it includes D and omits A, thereby suggesting that indexing restricts access and ignoring the basic function of indexing services in helping researchers locate literature.

Option D:
Option D is also incorrect since it accepts D and omits B, neglecting the summarising role of abstracts and misrepresenting how indexing and abstracting operate.