UGC NET Questions (Paper – 1)

A quasi-experimental design resembles a true experiment in that the researcher introduces an intervention or treatment and measures its effect on an outcome, but it lacks random assignment of participants to conditions. Groups may be pre-existing intact classes or institutions, which can introduce selection biases. Researchers often use statistical controls or matching to reduce these threats. Because the stem describes manipulation of an independent variable without random assignment, quasi-experimental design is the correct term.

Option A:
Experimental design includes both manipulation of independent variables and the random assignment of participants to treatment and control groups, which allows strong causal inferences. The absence of randomisation in the stem makes experimental design an inaccurate label in this case.

Option B:
Ex post facto design studies differences that already exist without manipulating variables, so it is non-experimental; the researcher only observes and tries to infer possible causes from existing conditions. The stem, however, explicitly mentions manipulation of the independent variable, which does not occur in ex post facto designs.

Option C:
Correlational design examines the relationships between variables without manipulating them, using statistical associations to describe and sometimes predict outcomes. It does not meet the criterion of introducing a treatment or intervention as stated in the stem. Therefore, correlational design is not the best completion here.

Option D:
Quasi-experimental designs are common in educational and social settings where random assignment is impractical or unethical, yet some level of control and manipulation is still feasible. They provide more tentative causal evidence than true experiments but are stronger than purely observational studies, matching the description in the question.

A research design is a structured plan that links the research problem and questions to methods of data collection and analysis. It clarifies decisions about sampling, instruments, procedures and controls. A good design enhances the validity of conclusions by anticipating sources of error and specifying strategies to handle them. Therefore, the overall blueprint referred to in the stem is known as the research design.

Option A:
The design details whether the study will be experimental, correlational, descriptive or qualitative and indicates how variables will be measured and compared. Without a clear design, data collection may become unfocused and analyses inappropriate. This central planning role aligns exactly with the stem, making this option correct.

Option B:
A hypothesis is a tentative prediction or explanation about relationships between variables; it informs the design but is not the plan itself.

Option C:
A schedule usually refers to instruments such as interview schedules, not to the entire plan of the study.

Option D:
A scale is a measurement tool for quantifying attitudes or traits, such as a Likert scale, and is only one component of the larger design.

In this question, statements A, C, E and F correctly describe the scientific method, while B and D are wrong. Statement A is true because systematic observation and logical reasoning are central to scientific inquiry. Statement C correctly identifies hypothesising, testing and verification as typical steps, and statement E is right that objectivity and replicability are crucial. Statement F is also true since scientific principles can be adapted within qualitative frameworks, whereas B is false because empirical evidence is fundamental and D is false as intuition alone cannot constitute scientific research.

Option A:
Option A treats B as the only wrong statement and ignores D. While B is indeed false because the scientific method depends on empirical evidence, D is also incorrect since relying solely on intuition contradicts scientific rigour. By missing one of the wrong statements, this option does not fully answer the question.

Option B:
Option B correctly identifies both B and D as wrong statements and does not misclassify any of the true statements. It recognises that rejecting empirical evidence and relying only on intuition are both incompatible with the scientific method. Because it captures the full set of wrong statements and excludes the correct ones, this option is the right answer.

Option C:
Option C marks only D as wrong and overlooks B. Although D is clearly false, B is equally incorrect because scientific research requires empirical data. Since this combination fails to include all the wrong statements, it cannot be accepted.

Option D:
Option D wrongly includes statement C among the wrong statements along with B and D. Statement C is actually a correct description of typical steps in the scientific method. By misclassifying a correct statement as wrong, this option becomes logically inconsistent.

Statements A, B, C and E are correct because technical reports present detailed methods and analyses, popular reports translate findings for general readers, executive summaries condense key points and the same study may be reported differently for different audiences. Statement D is false; proper referencing is essential across all report types to acknowledge sources and allow verification. Hence, the combination including A, B, C and E and excluding D is correct.

Option A:
Option A is correct since it recognises both the diversity of report formats and the need for adaptation to audience, while also assuming proper referencing even if that is not restated. It excludes D, which negates a core academic norm.

Option B:
Option B is incomplete because it omits E, failing to state explicitly that multiple formats can be produced from a single study for various stakeholders. Without E, the adaptability of reporting is not fully captured.

Option C:
Option C is wrong as it includes D, which wrongly suggests that references are unnecessary, and omits A, which correctly describes the detail level of technical reports. This combination both adds a false statement and drops a true one.

Option D:
Option D is also incorrect because it accepts D along with A and E, thereby endorsing the idea that references are unnecessary. Such a view undermines accountability and scholarly transparency.

The independent variable is the factor that the researcher deliberately manipulates or varies in an experiment in order to examine its impact on a dependent variable. It is considered the presumed “cause” in a cause–effect relationship. By changing its levels while controlling other variables, the researcher can test specific hypotheses about its influence. Therefore, the manipulated variable described in the stem is correctly called the independent variable.

Option A:
Option A, independent variable, captures the idea of a variable that is “independent” of changes in other variables within the experimental framework, because it is set by the researcher. Different groups or conditions receive different levels of this variable. Since the stem emphasises manipulation to observe effects, this option is appropriate.

Option B:
The dependent variable represents the outcome or response that is measured to see whether it changes as a result of the manipulation. It is not itself manipulated, so it does not fit the description.

Option C:
A control variable is kept constant across conditions to prevent it from confounding the relationship between independent and dependent variables. While important, it is not the variable being manipulated.

Option D:
Extraneous variables are uncontrolled factors that may influence the dependent variable unintentionally and threaten internal validity. They are neither intentionally manipulated nor central to the experiment’s main hypothesis, so they are not the right answer.

A conceptual framework organises key variables and constructs of a study into a visual or diagrammatic form, often using boxes to represent concepts and arrows to show presumed directions of influence. It is built from theory and prior research and illustrates the logic of how the researcher expects variables to be related. This framework guides the formulation of hypotheses, selection of measures and interpretation of findings. Therefore, the visual model described in the stem is accurately termed a conceptual framework.

Option A:
A sampling framework or sampling frame is the list or structure from which a sample is drawn and is not typically depicted as a network of variables and arrows. It concerns elements or units rather than theoretical relationships. Hence, sampling framework does not fit the stem.

Option B:
Operational framework would describe how constructs are operationalised or measured in practice, but this term is not commonly used to label the visual theoretical model of variables and relationships. It focuses more on procedures than on conceptual linkages, so operational framework is not the best completion.

Option C:
Statistical framework might refer to a set of assumptions and models used for data analysis, such as regression or multilevel models. While it involves variables, it is not necessarily represented as a box-and-arrow diagram outlining theoretical relationships. Thus, statistical framework is not the correct answer here.

Option D:
Conceptual frameworks help readers quickly grasp the structure of the research problem and the proposed causal or associative pathways, which is exactly the function described in the question.

A Likert scale presents statements and asks respondents to choose among ordered response categories such as strongly agree, agree, neutral, disagree or strongly disagree. The responses are scored to reflect the direction and intensity of attitude toward the issue. It is widely used because it is simple to construct and easy for respondents to understand. Since the stem refers to indicating levels of agreement or disagreement with statements, the correct term is Likert scale.

Option A:
The Likert scale treats the ordered categories as reflecting increasing or decreasing levels of the underlying attitude and often sums scores across multiple items to obtain a composite measure. This approach matches the description in the stem, which emphasises degree of agreement with a series of statements.

Option B:
A semantic differential scale uses bipolar adjective pairs, such as good–bad or strong–weak, at the ends of a continuum and asks respondents to mark their position between them. It measures connotative meanings rather than straightforward agreement or disagreement, so it does not fit the stem.

Option C:
A Guttman scale, or cumulative scale, arranges items so that agreement with a particular statement implies agreement with all less extreme statements. It is more complex to construct than a Likert scale and is not simply about rating agreement for each independent item. Therefore, Guttman scale is not the best answer here.

Option D:
Thurstone scales involve expert judges assigning scale values to statements and then using these values to measure attitudes, which is a laborious procedure. While they also measure attitudes, they do not primarily use the familiar agree–disagree format described in the stem. Hence, Thurstone scale is not the correct completion.

Generalisability concerns the extent to which the findings of a particular study can be validly extended beyond the specific sample, setting and time in which the data were collected. High generalisability implies that similar results would likely be obtained in other contexts with comparable conditions. It is closely linked to external validity, which evaluates how well study results apply in real-world situations. Therefore, a statement about how far results may be applied elsewhere refers to the generalisability of the findings.

Option A:
Reliability addresses the consistency of measurement within the study and does not by itself indicate whether results can be extended to other populations or settings. A measure can be reliable in one context but may not generalise to different groups. Hence, reliability is not the correct completion.

Option B:
Generalisability is influenced by factors such as sampling method, sample size, and ecological validity of the research setting. Researchers often discuss limitations on generalisability to avoid overstating the applicability of their results. These concerns match the description in the stem.

Option C:
Standardisation involves using uniform procedures and conditions when administering instruments or conducting experiments, which supports reliability and fairness but is not the same as extending findings to other contexts. Therefore, standardisation is not the best answer.

Option D:
Objectivity refers to freedom from researcher bias in collecting and interpreting data, ensuring that findings are based on evidence rather than personal preferences. While it contributes to the credibility of results, it does not directly describe their scope of application, so objectivity is not appropriate here.

In this question statements A, B, D and E reflect core features of research design, while statement C is incorrect. Statement A is true because a research design indeed acts as a blueprint guiding data collection, measurement and analysis. Statement B is correct since a well-planned design seeks to minimise bias and enhance the reliability of data, and statement D properly characterises descriptive designs as portraying characteristics of populations or phenomena. Statement E is also true as causal-comparative research explores cause–effect relationships without direct manipulation of independent variables, whereas statement C is false because even exploratory studies require at least a tentative design or plan.

Option A:
Option A includes A, B and E but leaves out D. Although A, B and E are correct, D is also a true statement about descriptive research designs. By omitting D, this combination does not include all the correct statements and so cannot be accepted as the right answer.

Option B:
Option B selects B, D and E but omits A. While B, D and E are accurate, excluding A means the crucial general idea of research design as a blueprint for the entire study is missing. A complete set of correct statements must include A as well, so this option is incomplete.

Option C:
Option C correctly groups A, B, D and E, all of which present a coherent understanding of research design and related types of studies. It omits statement C, which wrongly suggests that exploratory studies do not need any design. Since this option contains all and only the true statements, it is the correct answer.

Option D:
Option D adds statement C to the otherwise correct combination of A, D and E. Because C is false, claiming that exploratory studies never require a design, its inclusion makes the entire combination incorrect. A valid option cannot mix misleading statements with true ones, so this choice is not acceptable.

Statements C and E are wrong because they conflict with basic principles of research formulation. Hypotheses should, in fact, be clear and testable rather than inherently vague, so C misrepresents their nature. E is wrong because reviewing related literature is essential for refining the problem, establishing significance and avoiding duplication. In contrast, A, B and D accurately describe how problems and objectives should be focused and logically aligned.

Option A:
Option A is incorrect because it treats only C as wrong and ignores the error in E. While C is indeed a wrong statement, E also misleads by suggesting that literature review is unnecessary after stating the problem. A correct answer must capture both faulty statements, not just one of them.

Option B:
Option B wrongly assumes that only E is wrong, overlooking the fact that C also contradicts the requirement that hypotheses be formulated in testable terms. By failing to recognise C as incorrect, this option provides an incomplete identification of wrong statements.

Option C:
Option C is correct because it groups C and E, the two statements that do not conform to sound research practice. It implicitly affirms that A, B and D correctly describe problem identification, objective formulation and logical consistency. This combination therefore reflects accepted guidelines for research design.

Option D:
Option D is incorrect because it adds B to the list of wrong statements, even though B correctly explains how objectives translate the problem into specific tasks. By misclassifying B as wrong, the option undermines a correct description of research objectives.

Statements A, B, D and E correctly reflect the logical flow of most research processes, while C is incorrect. The research journey typically begins with identifying and defining a problem and then formulating questions or hypotheses to address it. After collecting data, the researcher analyses and interprets them, and finally reports and disseminates the findings through theses, articles or presentations. Statement C is wrong because review of literature is normally undertaken before detailed data collection, not after.

Option A:
Option A includes A, B and D but excludes E. Although it correctly covers problem definition, hypothesis formulation and post-collection analysis, it omits the important final step of reporting and dissemination. Because it does not include all true statements, this combination is incomplete.

Option B:
Option B is correct because it brings together all the true statements A, B, D and E and omits C, which reverses the usual ordering of literature review and data collection. It captures the full standard sequence from problem identification to reporting.

Option C:
Option C wrongly includes C along with true statements. Since C states that data collection must always precede literature review, it conflicts with accepted practice where literature review informs design and tools. Including C therefore makes this option incorrect.

Option D:
Option D lists B, D and E but omits A, ignoring the foundational step of clearly defining the research problem. Without acknowledging this early stage, the combination does not reflect a complete sequence, so it cannot be correct.

Option E includes A, B and E but leaves out D, failing to mention analysis and interpretation as a distinct stage between data collection and reporting. Skipping this key step means the option cannot represent the full set of correct statements.

A research proposal is a detailed plan that outlines the background, significance, objectives, methodology, ethical considerations and budget of a study. When submitted to a funding agency or sponsor, it serves as the basis for deciding whether to allocate financial support. The proposal must convince reviewers of the feasibility, importance and methodological soundness of the project. Thus, the funding-oriented document described in the stem is correctly termed a research proposal.

Option A:
A report summarises the results of a completed study and is written after data collection and analysis, not before funding is granted. It does not function primarily as a request for support, so report is not the correct answer.

Option B:
Option B, proposal, emphasises forward-looking planning and justification, including how funds will be used and what outcomes are expected. This aligns closely with the stem’s focus on submission to an agency for financial backing.

Option C:
A synopsis is often a brief summary of a proposed or completed study and usually lacks the full details about budget and implementation required in a funding application. Hence, synopsis does not fully match the description.

Option D:
A monograph is a substantial written work that reports in-depth research on a single topic, typically produced after the research is complete. It is not the document used to seek funding beforehand, so monograph is not appropriate here.

A hypothesis is a tentative, logically derived statement predicting a relationship or difference between variables that can be examined with data. It translates theoretical ideas into forms that can be subjected to empirical testing. By stating expected patterns, a hypothesis directs the design of the study and the choice of statistical procedures. Therefore, a conjectured relationship that can be tested empirically is correctly termed a hypothesis.

Option A:
A research question is an interrogative statement that identifies the focus of inquiry but does not always specify a testable prediction about the direction or nature of relationships. It often precedes the formulation of hypotheses. Thus, research question does not fully capture the idea of a testable conjecture described in the stem.

Option B:
An assumption is accepted as true without proof and forms part of the logical foundation of the study rather than something to be tested. It is not usually framed as a specific predicted relationship between variables. Hence, assumption is not the best completion here.

Option C:
A proposition is a declarative statement that expresses a relationship between concepts and may form part of a theory, but not all propositions are directly testable within a given study. Hypotheses are typically operationalised and linked to measurable variables, making hypothesis the more precise term for the stem.

Option D:
The hypothesis sets up a claim about the population that can be supported or rejected based on sample evidence. Its testability distinguishes it from more general or purely conceptual statements, which matches the definition required in the question.

Statements A, B and D correctly describe typical concerns and designs in ODL research. Studies often examine learner support, instructional design and technology, use mixed-methods approaches to combine breadth and depth, and pay attention to persistence and dropout. Statements C and E are false: ODL research can use online trace data, and access and equity are central issues rather than irrelevant. Therefore, the set containing A, B and D only is correct.

Option A:
Option A is incomplete because it omits D, thereby ignoring the significant focus on learner persistence and dropout, which are major outcomes of interest in ODL contexts. Without D, the picture of ODL research concerns is less complete.

Option B:
Option B is incorrect as it includes C, the statement that denies the use of trace data, even though ODL environments routinely generate and analyse such records. Including C introduces a clear inaccuracy.

Option C:
Option C is wrong because it includes E and omits A. E wrongly dismisses access and equity, while A correctly notes important ODL research topics. This mixture distorts both substantive concerns and methods.

Option D:
Option D is correct since it groups the three true statements and excludes the two false ones. It reflects the technological, pedagogical and retention-related emphases of ODL research.

Non-response bias occurs when the characteristics of non-respondents differ in important ways from those of respondents, leading to distorted estimates of population parameters. For example, if less satisfied individuals are less likely to answer a satisfaction survey, the results will be overly positive. This bias persists even with large sample sizes if non-response is systematic. Therefore, the bias described in the stem is correctly termed non-response bias.

Option A:
Sampling bias refers more broadly to systematic errors in the way the sample is selected, such as excluding certain groups entirely, but it does not specifically focus on the difference between respondents and non-respondents. Non-response is one particular source of sampling error, so sampling bias is too general here.

Option B:
Measurement bias arises when instruments or questions systematically overestimate or underestimate the true values, for instance due to poorly worded items or faulty devices. It is unrelated to whether people choose to respond or not. Hence, measurement bias is not the appropriate completion.

Option C:
Non-response bias undermines the representativeness of survey findings because missing data are not random but correlated with variables of interest. Strategies such as follow-up contacts or weighting adjustments are often used to reduce its impact. These aspects fit exactly with the description in the question.

Option D:
Response-set bias involves patterns such as acquiescence or social desirability in the way respondents answer questions, affecting the quality of obtained responses rather than whether people respond at all. Therefore, response-set bias does not match the situation described in the stem.