UGC NET Questions (Paper – 1)

Statement C is the only wrong statement because funding does not replace the need for ethical clearance; many agencies explicitly require ethical approval from an appropriate committee. Statements A, B, D and E accurately describe typical practices in funded projects, including detailed proposals, budget planning, progress reporting and financial accountability. Thus, C alone contradicts standard expectations about ethics in sponsored research.

Option A:
Option A is correct because it singles out C as the sole incorrect statement and leaves A, B, D and E as accurate descriptions. It recognises that ethical review is an independent requirement, even when financial support has been granted, and that other listed practices are standard.

Option B:
Option B is incorrect because it includes B with C, although B correctly describes the usual components of a research budget. By mislabelling B as wrong, this option fails to distinguish between valid administrative practice and an ethical misconception.

Option C:
Option C wrongly groups D with C, even though D correctly notes that progress reports are often demanded by sponsors. This option therefore misclassifies an accurate obligation as erroneous.

Option D:
Option D is also incorrect because it treats A as wrong, despite A being a correct statement about the central role of detailed proposals in funding decisions. Grouping A with C blurs the distinction between sound procedure and ethical error.

Option E misclassifies E, which rightly emphasises accountability in fund utilisation, as wrong along with C. Since E is correct, this combination cannot represent the true set of wrong statements.

Statistical analysis in quantitative research serves several purposes. Descriptive statistics summarize data through measures like mean, median and standard deviation. Inferential statistics help examine relationships and differences, and test hypotheses about populations based on sample data. Together, these techniques provide an evidence based basis for interpreting findings.

Option A:
Option A suggests replacing numbers with only qualitative description, which ignores the advantages of numerical precision and patterns that statistics reveal.

Option B:
Option B is correct because it combines summarization, exploration of relationships and hypothesis testing, which are central tasks of quantitative data analysis. It recognizes that statistics support but do not replace substantive interpretation.

Option C:
Option C claims that statistics eliminate the need for interpretation, which is false. Researchers must still relate numerical results to theory, context and practical implications.

Option D:
Option D assumes automatic acceptance of hypotheses, whereas statistical testing may lead to either rejection or failure to reject, depending on evidence.

Split-half reliability is obtained by dividing a test into two comparable halves, such as odd and even items, and correlating the scores on these halves. A high correlation indicates that the items within the test are consistently measuring the same construct. This method assesses internal consistency without requiring separate test administrations. Because the stem describes estimating reliability by correlating scores on two halves of the same test, it is clearly referring to split-half reliability.

Option A:
Test–retest reliability involves administering the same test to the same group of individuals on two different occasions and correlating the scores across time. It measures stability over time, not the internal consistency between halves within a single administration. Therefore, test–retest does not match the description in the stem.

Option B:
Parallel forms reliability is established by developing two equivalent versions of a test and correlating the scores obtained on them. It assesses consistency across forms rather than across halves of one form. Since the question mentions halves of the same test, parallel forms is not the correct answer.

Option C:
Split-half reliability directly evaluates how well different items in a single instrument work together to measure a construct by comparing performance on different subsets. This internal consistency focus corresponds exactly to the procedure outlined in the stem, confirming split-half as the appropriate completion.

Option D:
Inter-rater reliability refers to the degree of agreement between different observers or raters when scoring or judging the same behaviour or responses. It deals with consistency across raters rather than across test halves, so inter-rater is not suitable here.

Kurtosis describes the degree of peakedness and tail heaviness of a distribution compared to the normal curve. High kurtosis (leptokurtic) indicates a sharp peak and heavy tails, while low kurtosis (platykurtic) reflects a flatter peak and lighter tails. Understanding kurtosis helps researchers judge whether data meet assumptions for statistical tests and whether outliers may be problematic. Thus, the statistic described in the stem is correctly called kurtosis.

Option A:
Dispersion is a general concept referring to spread in data, including measures like range and variance, but it does not specifically distinguish between peaked and flat distributions.

Option B:
Skewness deals with asymmetry of the distribution, focusing on whether one tail is longer than the other, which is different from the height and tail heaviness measured by kurtosis.

Option C:
Kurtosis provides insight into how concentrated scores are around the mean and how extreme values contribute to tail thickness, which influences the probability of outliers. These properties correspond exactly to the description of peakedness in the stem, so this option is correct.

Option D:
Reliability concerns consistency of measurement across time or items and is unrelated to the shape of frequency distributions of a single variable.

Research objectives describe what the investigator plans to achieve, while hypotheses propose specific, testable explanations or predictions arising from those objectives and the literature. Hypotheses are often phrased in terms of relationships or differences between variables that can be examined empirically. They help determine what data are needed and what analyses should be conducted. Therefore, the tentative answers to the problem mentioned in the stem are correctly termed hypotheses.

Option A:
Variables are measurable characteristics or attributes of units in a study but do not themselves constitute tentative answers to the research problem. They are the elements about which hypotheses are framed rather than being the hypotheses.

Option B:
Assumptions are underlying conditions accepted as true for the purposes of the study without directly testing them. They are not explicit predictions about relationships or differences, so they do not fit the stem.

Option C:
Option C, hypotheses, encapsulates the idea that researchers make provisional claims that can be supported or refuted by data, bridging theory and observation. This matches the description of tentative, testable answers to the problem, making it correct.

Option D:
Indicators are specific empirical measures used to represent a variable, such as test scores or frequency counts. They operationalise concepts but are not themselves predictions, so they are not appropriate here.

A Pearson correlation coefficient ranges from −1 to +1 and indicates the strength and direction of a linear relationship. A value of +0.85 is close to +1, suggesting that as one variable increases, the other tends to increase as well in a fairly consistent manner. Although not perfect, this coefficient reflects a strong positive association between the two variables.

Option A:
Option A correctly labels the relationship as strong and positive (and, in the context of Pearson’s r, linear). A coefficient of +0.85 indicates a substantial positive linear association between the variables.

Option B:
Option B claims there is no relationship, which would correspond to a coefficient near zero rather than +0.85. Such a high value clearly signals substantial association.

Option C:
Option C suggests a perfect negative relation, which would correspond to −1.00, not +0.85. Negative correlations indicate that one variable increases while the other decreases.

Option D:
Option D states that the variables are completely independent of each other, which would imply a correlation close to 0, not a large positive value like +0.85.

Parametric tests operate under assumptions about the parameters of the population distribution, typically including normality of the dependent variable and homogeneity of variances across groups. They are powerful when these assumptions are reasonably met and are widely used for interval or ratio data. Examples include t-tests and analysis of variance. Therefore, tests that rely on such distributional assumptions are correctly termed parametric tests.

Option A:
Non-parametric tests do not require strict assumptions about normal distribution or equal variances and are often used for ordinal data or when parametric assumptions are violated. They include tests such as Mann–Whitney U and Kruskal–Wallis, so they do not match the stem.

Option B:
Descriptive statistics summarise data through measures like mean, median and standard deviation but are not themselves inferential tests requiring distributional assumptions. Thus, descriptive is not the appropriate label here.

Option C:
Option C, parametric, emphasises that these tests estimate and compare population parameters such as means under certain model assumptions. Because the stem cites t-tests and ANOVA as examples, this term fits perfectly.

Option D:
The chi-square test is generally considered a non-parametric test used for categorical data, not a representative example of parametric testing. Therefore, chi-square is not the correct completion.

Delimitations are conscious choices made by the researcher to narrow the scope of a study, such as focusing on a particular age group, setting, time period or set of variables. They define what the study will include and, by implication, what it will not attempt to cover. These boundaries help make the research manageable and align it with the resources and objectives available. Because the stem refers to restrictions that the researcher deliberately sets, it is describing delimitations of the study.

Option A:
Limitations are constraints or weaknesses that the researcher cannot fully control, such as small sample size, measurement error or non-response, which may affect the interpretation of results. While both terms are sometimes confused, limitations differ from deliberate decisions to restrict scope. Since the stem emphasizes boundaries that are consciously set, limitations is not the correct answer.

Option B:
Assumptions are conditions that the researcher takes to be true without direct evidence in the study, such as assuming that respondents answer honestly or that the instrument is understood as intended. They are not explicit boundaries on scope but underlying beliefs. Therefore, assumptions do not match the description of restricting the study to certain variables or locations.

Option C:
Variables are characteristics or attributes that can vary and are studied in research, such as age, test scores or teaching methods. They are elements within the study, not the boundaries that define its scope. The stem is concerned with how the researcher limits which variables or contexts will be included, so variables is not the right term.

Option D:
Delimitations are usually described in the methodology section of a thesis or report so that readers understand the intended coverage of the study and its designed boundaries. They help prevent overgeneralisation by clarifying the precise focus of the investigation. Given that the stem discusses deliberate restrictions of scope, delimitations is the accurate completion.

Correlational research measures variables as they naturally occur and uses statistical techniques such as correlation coefficients to assess the strength and direction of associations between them. It does not involve manipulation or random assignment and therefore cannot definitively establish causality. Nonetheless, it is valuable for identifying patterns, making predictions and generating hypotheses. Thus, the design described in the stem is correctly called correlational research.

Option A:
Experimental research manipulates independent variables and uses control groups to test causal claims, which is not the case when variables are simply observed. Therefore, experimental does not match the stem.

Option B:
Correlational designs may show that two variables vary together, but alternative explanations such as third variables or reverse causation may still exist. Because of this, researchers are cautious not to interpret correlations as proof of cause and effect. These characteristics align exactly with the description, so this option is correct.

Option C:
Historical research deals with past events and sources, not primarily with statistical association among contemporaneous variables.

Option D:
Action research focuses on practical problem solving and improvement in local settings, often using cycles of intervention, and is conceptually distinct from correlational association studies.

Statement C is the only wrong statement because quantitative research does not always provide deeper insights into meanings than qualitative research. Qualitative approaches are specifically designed to explore meanings, experiences and contexts in depth. Statements A, B, D and E are correct: quantitative designs rely on structured tools, qualitative designs use open-ended techniques, mixed-methods integrate both strands and the qualitative researcher often functions as the main data collection instrument.

Option A:
Option A is correct as it identifies C alone as the misrepresentation of comparative insight between approaches. It recognises that both quantitative and qualitative methods have strengths, but depth of meaning is particularly associated with qualitative designs. The option also affirms that mixed methods and the researcher’s role are described accurately.

Option B:
Option B is incorrect because it labels A along with C as wrong, implying that quantitative designs do not typically use structured instruments. In fact, structured tests and questionnaires are hallmarks of many quantitative studies, so treating A as wrong is misleading.

Option C:
Option C is also wrong because it groups D with C, even though D correctly defines mixed-methods designs as intentional combinations of qualitative and quantitative components. By misclassifying D, this option undermines an established definition.

Option D:
Option D incorrectly adds B and E to the set of wrong statements. Both B and E accurately describe qualitative techniques and the researcher’s role. Including them with C suggests that qualitative procedures are misrepresented, which is not the case.

Statements A, B, C and E correctly describe typical qualitative sampling practices. Purposive sampling is widely used to select cases that yield rich information, and the goal is depth rather than statistical representativeness. Theoretical sampling helps refine emerging categories, and sample size is often guided by saturation, when new data no longer add substantially new insights. Statement D is false because probability sampling is not mandatory in qualitative research and is often not used.

Option A:
Option A is correct because it brings together the four true statements and excludes D, which misrepresents qualitative sampling requirements. It portrays qualitative sampling as flexible, concept-driven and oriented toward information richness rather than probability.

Option B:
Option B is incomplete because it omits E, ignoring how the concept of saturation influences decisions about when to stop collecting data. Without E, the dynamic nature of sample size determination in qualitative work is underplayed.

Option C:
Option C is incorrect because it includes D, the statement that probability sampling is mandatory, which contradicts common qualitative practice. Including D alongside true statements creates a misleading picture of qualitative methodology.

Option D:
Option D is wrong because it retains D and drops B, failing to acknowledge that depth of understanding, rather than representativeness, is the usual goal. This combination therefore distorts both the purpose and the technique of qualitative sampling.

Confidentiality means that researchers keep participants’ data secure and do not disclose information in a manner that would allow others to identify individual participants. This may involve using codes, storing data securely and reporting findings in aggregated form. It is a core ethical obligation in research involving human subjects. Because the stem describes the assurance that information will not be disclosed in a way that reveals identity, confidentiality is the correct term.

Option A:
Anonymity goes a step further by ensuring that even researchers cannot link responses to individuals, whereas confidentiality allows researchers to know identities but restricts disclosure. The stem focuses on assurance about disclosure to others, not on whether the researcher can link data, so anonymity is not the best answer.

Option B:
Debriefing is the process of explaining the true nature of a study to participants after it has concluded, particularly when deception was used. It does not pertain directly to protecting identifiable information in reports or records, so debriefing does not match the description in the question.

Option C:
Confidentiality requires clear procedures for who can access raw data, how long it will be stored and how it will be destroyed. These practices ensure that personal information is not exposed unnecessarily, aligning closely with the ethical assurance mentioned in the stem.

Option D:
Plagiarism concerns using others’ work without acknowledgment and is a violation of academic integrity, but it is unrelated to safeguarding participant identities. Thus, plagiarism is not the appropriate completion here.

Nonresponse bias arises when certain types of individuals are less likely to participate or return questionnaires. If the nonrespondents differ in important ways from respondents on variables of interest, estimates based on the responding sample will be distorted even when probability sampling was used.

Option A:
This option describes an ideal situation of full response, in which nonresponse bias would not occur because everyone participates.

Option B:
This option correctly explains that bias occurs when nonrespondents are systematically different from respondents; their absence skews the results and undermines representativeness.

Option C:
Using simple random sampling helps avoid selection bias at the sampling stage but does not remove the possibility that some selected individuals will refuse or fail to respond.

Option D:
Whether a questionnaire has open or closed items affects the type of data collected, not whether responders and nonresponders differ systematically.

The abstract is a brief overview placed at the beginning of a report or article. It informs readers about the research problem, objectives or questions, methodology used, main results and major conclusions. This allows potential readers to decide quickly whether the full text is relevant to their interests. Thus, its primary function is to provide a concise and accurate summary of the study.

Option A:
Option A incorrectly emphasises detailed tables and statistical calculations. Such material belongs in the results or appendices, not in the abstract, which must remain brief and nontechnical.

Option B:
Option B correctly identifies the abstract as a short summary containing core elements of the research—purpose, methods, findings and conclusions. This matches standard journal and thesis guidelines.

Option C:
Option C confuses the abstract with the full literature review. While an abstract may briefly mention background, it does not reproduce the entire review or all references.

Option D:
Option D implies that reading only the abstract is sufficient and the rest of the report is unnecessary. In reality, readers must consult the full text to understand methods, limitations and detailed interpretations.

Computers and statistical packages greatly speed up tasks such as data entry, cleaning, coding and complex calculations, reducing human error in routine processing and allowing researchers to handle large datasets. However, software cannot decide whether methods are appropriate or what the results mean; human judgment is needed for design, analysis choice and interpretation.

Option A:
This option implies that understanding of statistics is unnecessary, which is dangerous. Without conceptual knowledge, researchers may misuse software and misinterpret output.

Option B:
This option suggests that thinking can be automated, which overestimates the capability of software; programs can follow procedures but cannot understand context or theoretical implications.

Option C:
This option is correct because it treats computers and software as tools that handle storage and computation efficiently while leaving responsibility for interpretation and critical decisions with the researcher.

Option D:
At present, software cannot independently generate meaningful research problems. Identifying worthwhile questions still depends on human creativity and domain knowledge; computers may assist with literature searches but do not replace the researcher.