UGC NET Questions (Paper – 1)

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.

A counterbalanced design systematically varies the order in which treatments are administered so that each treatment appears in each position equally often across participants. This procedure helps control for order and carryover effects that could confound the results of repeated-measures experiments. By distributing potential order effects evenly, researchers can attribute observed differences more confidently to the treatments themselves. Because the stem describes using every possible order of treatments equally often, counterbalanced design is the correct answer.

Option A:
Matched-groups designs involve pairing participants on certain characteristics and then assigning members of each pair to different groups, reducing variability between groups but not directly addressing order effects in repeated measures. Thus, matched groups is not the appropriate completion.

Option B:
Counterbalancing ensures that practice, fatigue or other sequence-related influences are not confounded with specific treatments. It is especially valuable when the same participants receive multiple conditions. The stem’s emphasis on controlling order effects by using all possible orders aligns exactly with this strategy, confirming counterbalanced as the right term.

Option C:
Factorial designs study the effects of two or more independent variables simultaneously, examining both main effects and interactions. They concern the structure of independent variables rather than the order of administration in repeated measures. Hence, factorial design is not suitable here.

Option D:
Single-subject designs focus on repeated observations of a small number of participants, often using AB or ABA patterns, and may or may not involve counterbalancing. The term single-subject alone does not capture the systematic variation in treatment order described in the stem. Therefore, single-subject is not the correct answer.

Longitudinal studies involve repeated observations of the same individuals, groups or phenomena over an extended period. This design allows researchers to analyse changes, trends and developmental patterns that cannot be seen in single time-point studies. It is especially important in education, psychology and health research for understanding growth and long-term effects. Thus, a design collecting data from the same participants at multiple times is correctly termed a longitudinal study.

Option A:
Cross-sectional studies take a snapshot at one time point and cannot track individual changes over time. They may compare different groups but do not revisit the same participants, so cross-sectional does not match the stem.

Option B:
Option B, longitudinal, highlights that the time dimension is essential and that repeated measurements of the same units are central to the design. This fits the description of studying change over time, making this option correct.

Option C:
Experimental designs focus on manipulation and control; they may be conducted in cross-sectional or longitudinal formats, but experiment is not defined solely by repeated measurement of the same participants. Hence, experimental is not the best completion.

Option D:
Causal-comparative (ex post facto) designs compare existing groups after an outcome has occurred and do not necessarily involve repeated measurements across time. Therefore, they are not the correct answer here.

Here statements A, C, D and F correctly present the role of review of literature, while B and E are wrong. Statement A is true because reviewing existing work prevents unnecessary duplication. Statement C is correct since literature review supports the choice of theoretical framework, and statement D is right that it may suggest tools and designs. Statement F is also true as prior studies help interpret current findings, whereas B is false because review is not restricted to the final stage and E is false because even experts require systematic review to stay updated.

Option A:
Option A labels only B as wrong, but statement E is also incorrect. Although B wrongly postpones literature review to after report writing, E wrongly claims that experts can skip review. Since both B and E misrepresent good practice, this option does not capture the full set of wrong statements.

Option B:
Option B singles out E as wrong but fails to include B. While E is indeed false, B is equally problematic in suggesting an inappropriate timing for the review. Because the option ignores one of the wrong statements, it is incomplete.

Option C:
Option C incorrectly adds F to the list of wrong statements. Statement F is in fact correct because prior studies provide a reference for interpreting current results. Grouping F with B and E misclassifies a true statement as wrong and makes the option invalid.

Option D:
Option D properly identifies B and E as the only wrong statements and leaves A, C, D and F untouched as correct. It reflects that review is needed before and during research and that expertise does not remove the need for systematic review. Therefore this combination is the correct answer.

A longitudinal study follows the same individuals or groups over time, collecting data at multiple intervals to observe changes, trends or developmental patterns. This design is especially useful in educational and psychological research where growth, learning trajectories or long-term effects of interventions are of interest. By keeping the sample constant, researchers can attribute changes more directly to time or experiences rather than to differences between people. Hence, a design with repeated study of the same participants over an extended period is correctly called longitudinal.

Option A:
Cross-sectional studies, by contrast, collect data from different individuals at a single point in time, providing only a snapshot of conditions. They cannot directly trace changes within the same individuals, which is the central feature of longitudinal research described in the stem. Therefore, cross-sectional is not the correct option here.

Option B:
Experimental designs manipulate an independent variable and typically compare groups under different conditions; they may or may not involve repeated measures. While an experiment can be longitudinal if it extends over time, the term experimental alone does not emphasise the repeated observation of the same group across a long period, which is the key element in the question.

Option C:
Longitudinal research can take various forms, such as panel studies or cohort studies, but all share the feature of repeated measurement on the same participants. This allows researchers to analyse trajectories, stability and change at the individual level. Because the stem focuses on studying the same group over time, longitudinal is the accurate completion.

Option D:
Ex post facto research examines existing groups after an effect has occurred, often at a single time point, without manipulating variables. It does not necessarily involve repeated observation of the same participants over an extended period. Consequently, ex post facto study is not the right answer here.

Statement B is the only wrong statement because ex post facto research does not randomly assign participants to treatment and control groups; it studies groups that already differ on the variable of interest. Statements A, C, D and E correctly distinguish experimental designs from ex post facto designs. Experimental work manipulates the independent variable and controls extraneous factors, whereas ex post facto designs rely on existing differences and require cautious causal interpretation. The phrase “after the fact” correctly captures that the critical events have already occurred in ex post facto research.

Option A:
Option A is correct because it identifies B alone as the wrong statement and leaves A, C, D and E as correct descriptions. It acknowledges that random assignment is a feature of true experiments, not ex post facto designs, and correctly treats all other statements as conceptually sound.

Option B:
Option B is incorrect because it treats A as if it were also wrong, even though A correctly characterises experimental research as involving manipulation and control. By misclassifying a true statement as wrong, this option fails to isolate the actual source of error.

Option C:
Option C wrongly groups D with B, even though D correctly notes that causal claims from ex post facto studies must be cautious. While B is indeed false, D is a prudent caution and should not be listed as wrong, so this option presents an inaccurate combination.

Option D:
Option D adds E to B as if both were wrong, but E accurately explains the meaning of “after the fact” in ex post facto research. Because it treats a true explanatory statement as incorrect, this option cannot be accepted.

Option E is also incorrect because it includes A and C, both of which are true, among the wrong statements. This shows confusion between features of experiments and ex post facto designs and leads to an invalid set of “wrong” statements.

Assumptions are implicit or explicit statements that the researcher accepts as true for the purposes of the study without directly verifying them within that study. Examples include assuming that instruments are understood correctly by respondents or that the sampling frame adequately represents the population. These assumptions underpin the design and interpretation of results. Because the stem refers to statements taken as true without direct testing that support the design, it is defining assumptions.

Option A:
Hypotheses are formal, testable statements about relationships between variables and are subjected to empirical verification during the study. They are not accepted as true in advance but are evaluated using data. Therefore, hypotheses do not match the idea of statements accepted without direct testing.

Option B:
Assumptions function as background conditions that need to be plausible for the design and conclusions to be valid. Researchers often state them explicitly so that readers can judge whether they are reasonable. This role aligns precisely with the stem’s description, confirming assumptions as the correct completion.

Option C:
Delimitations are boundaries that define the scope of the study, such as excluding certain populations or contexts. They are conscious choices rather than untested statements about reality. Hence, delimitations is not appropriate here.

Option D:
Recommendations are suggestions for action or further research offered at the end of a study based on its findings. They come after results are obtained and do not serve as pre-existing conditions on which the design rests. Consequently, recommendations cannot complete the stem correctly.

A mediating variable, or mediator, accounts for the process by which an independent variable exerts its effect on a dependent variable. It stands in the causal chain between cause and outcome, translating the initial influence into the final effect. Identifying mediators helps researchers understand how and why a relationship occurs, not just whether it exists. Therefore, a variable that explains the mechanism of influence is correctly called a mediating variable.

Option A:
Control variables are held constant or statistically adjusted to prevent them from confounding the relationship between independent and dependent variables, but they do not represent the pathway of influence.

Option B:
Moderators change the strength or direction of the relationship but do not necessarily explain the mechanism through which the effect travels; they answer “when” or “for whom” rather than “how.”

Option C:
Mediating variables offer insight into intermediate steps, such as improved motivation mediating the effect of a teaching strategy on achievement. This explanatory role matches the stem and makes this option correct.

Option D:
Demographic variables describe characteristics like age or gender and may act as predictors, controls or moderators but do not inherently serve as mechanisms in a causal chain.

A cross-sectional study gathers data from a sample of individuals at one specific point in time, without following them over an extended period. It is often used to describe the prevalence of characteristics, attitudes or behaviours in different groups or to compare subgroups at that moment. Because measurements are taken only once, researchers cannot directly infer changes over time but can examine associations among variables. The stem clearly describes this one-time data collection, so cross-sectional study is the correct term.

Option A:
Longitudinal studies collect data from the same participants repeatedly over extended periods to track changes and developmental trends. They involve multiple time points rather than a single point, making them distinct from cross-sectional designs. Since the stem explicitly mentions a single point in time, longitudinal study cannot be the right answer.

Option B:
Cross-sectional designs are common in survey research where researchers want a snapshot of conditions or attitudes in a population. For example, a cross-sectional survey might compare research attitudes of first-year and final-year students at the same time. This matches the description in the stem, which focuses on describing or comparing groups based on one-time data collection.

Option C:
Experimental studies manipulate one or more independent variables and typically involve control groups and random assignment to test causal hypotheses. While they may be conducted within a limited time frame, their defining feature is manipulation and control, not merely one-time data collection from different individuals. Thus, experimental study is not the best label for the design described.

Option D:
Case-control studies are usually retrospective, comparing individuals with a particular condition (cases) to those without it (controls) to explore possible risk factors. Although case-control designs may gather data at one time, they have a specific focus on past exposures and are often used in epidemiology. The broader description in the stem fits the generic cross-sectional design better than the more specialised case-control approach.