UGC NET Questions (Paper – 1)

Ex post facto research, also called causal-comparative research, looks for possible cause–effect relationships by studying groups that differ on some outcome that has already occurred. The researcher does not manipulate the independent variable but observes existing differences and attempts to infer causes. This design is useful when experimental manipulation is unethical or impractical. Therefore, investigating causes after the effect has occurred is characteristic of ex post facto research.

Option A:
Option A correctly names ex post facto research, where “after the fact” indicates that the outcome is already present. Groups are compared on presumed causal factors that have not been controlled by the researcher. Because there is no manipulation and the effect comes first, this option fits the stem and is correct.

Option B:
Experimental research actively manipulates one or more independent variables and controls extraneous factors to test causal hypotheses. It does not wait for effects to occur naturally and then look backward. Thus, experimental research does not match the design described.

Option C:
Correlational research examines statistical relationships between variables without attempting to infer causality in a strong sense. It may be cross-sectional and does not necessarily start from an existing group difference on an outcome. So it is not the best label here.

Option D:
Longitudinal research follows the same participants over time to study change; it may be experimental or non-experimental. It is defined by the time dimension rather than by investigating causes after effects, so it is not the correct answer.

Systematic sampling begins by choosing a random starting point on an ordered list of population elements and then selecting every kth element thereafter, where k is the sampling interval. This procedure is simple to implement and can approximate random sampling if the order of the list is not correlated with the variables of interest. It also ensures a spread of the sample across the entire frame. Therefore, the method described in the stem is correctly called systematic sampling.

Option A:
Option A, systematic sampling, relies on a fixed numeric step to move through the sampling frame, making selection predictable once the start and interval are set but still probabilistic. Its efficiency and simplicity match the description of selecting every kth element.

Option B:
Convenience sampling is a non-probability method where respondents are chosen because they are easy to reach, without using a fixed interval or random starting point. Thus, it is not appropriate here.

Option C:
Cluster sampling selects intact groups or clusters, not individual elements at regular intervals from a list. It is based on groupings rather than on a numeric step, so it does not fit the stem.

Option D:
Purposive sampling is a judgment-based, non-probability method targeting specific information-rich cases and does not involve fixed intervals or an ordered frame. Hence, it is not the correct completion.

Statements A, B, D and E correctly capture the spirit of action research, whereas C is false. Action research is usually conducted by practitioners such as teachers to solve local problems, and involves cycles of acting and reflecting on action. It aims at improving practice and can foster both professional development and institutional change, rather than at statistical generalisation to large populations. Statement C is wrong because generalisation is limited and context-specific.

Option A:
Option A is correct because it includes all four true statements while excluding C, which misrepresents the goal of action research. It reflects the practitioner-led, improvement-oriented and reflective nature of this approach.

Option B:
Option B omits E, thereby ignoring the broader impact of action research on professional growth and institutional learning, even though these are significant outcomes. This omission makes the option incomplete.

Option C:
Option C leaves out A, failing to highlight that the practitioner often functions as researcher in action research, which is a defining feature. Without A, the description is not fully accurate.

Option D:
Option D includes C, the false statement about statistical generalisation, and so cannot be considered correct despite containing other true statements.

Option E retains C and therefore also becomes invalid, as it wrongly suggests that action research necessarily aims at large-scale generalisation.

An abstract is a concise summary of the entire research study, usually presented at the beginning of a thesis or dissertation. It briefly states the problem, objectives, methods, main findings and conclusions. Its purpose is to give readers a quick overview so they can decide whether to read the full document. Because the stem mentions a brief summary placed at the beginning, abstract correctly completes the statement.

Option A:
Abstracts are specifically designed to encapsulate the key elements of a study in a short paragraph or page. They follow academic conventions regarding length and content and are placed at the front of the thesis. Since they function exactly as described in the stem, abstract is the appropriate term for the summary section.

Option B:
A synopsis is usually a detailed proposal or outline of a planned study submitted before the full research is conducted. It may resemble an extended summary but is not typically placed at the beginning of the final thesis as a standard section. Therefore, synopsis does not fit the description given in the question.

Option C:
A preface is a preliminary note where the author expresses personal remarks, acknowledges help and may mention the circumstances of the study, but it does not systematically summarise the research content. It focuses more on background and gratitude than on methods and findings. Hence, preface is not the correct completion.

Option D:
A footnote is an explanatory note placed at the bottom of a page to clarify a specific point in the text. It is brief and local to a particular detail, not a comprehensive summary of the entire study at the beginning. Thus, footnote cannot be the term intended in the stem.

Statements A, B and E capture the essential ideas of reliability and validity in measurement. Statement A is correct because reliability refers to the consistency or stability of scores obtained with an instrument. Statement B is true in stating that validity concerns whether the instrument measures what it is supposed to measure. Statement E is also correct since the test–retest approach is a common method of estimating reliability, whereas statement C is false because validity generally presupposes a certain level of reliability and statement D is false because high reliability alone does not guarantee validity.

Option A:
Option A includes two correct statements but omits statement E. Since test–retest is a recognised method for estimating reliability, leaving out E means that the combination does not include all the true statements in the list. Consequently, Option A cannot be considered the correct answer.

Option B:
Option B combines B and E but ignores statement A. Although B and E are true, excluding A leaves out the basic notion that reliability is about the consistency and stability of an instrument’s scores. Because the option fails to include all the correct statements, it is incomplete and hence incorrect.

Option C:
Option C adds statement C to otherwise correct statements A, B and E. However, C is misleading because in psychometric theory an instrument that is not reliable cannot meaningfully be valid; reliability is typically a prerequisite for validity. Including a false or misleading statement makes the whole combination unacceptable.

Option D:
Option D lists A, B and E and excludes both C and D, which are false or misleading statements. It accurately reflects that reliability refers to consistency, validity to what is actually measured, and that test–retest is one way of assessing reliability. Since it contains all and only the true statements, this option is the correct answer.

Type II error, also known as beta error, occurs when a statistical test fails to reject the null hypothesis even though it is actually false in the population. In practical terms, this means the test has missed a real effect or difference that truly exists. The probability of committing a Type II error is related to factors such as sample size, effect size and chosen significance level. Because the stem describes failing to reject a false null hypothesis, it is defining Type II error.

Option A:
Type I error occurs when a true null hypothesis is incorrectly rejected, leading to a false positive conclusion. It is controlled by the significance level alpha and is conceptually different from failing to detect a real effect. Since the stem refers to failing to reject a false null, Type I error is not appropriate.

Option B:
The power of a test is the probability of correctly rejecting a false null hypothesis and is equal to 1 minus the probability of Type II error. While related, power is a desirable property rather than an error itself. Therefore, power of the test does not match the error described in the question.

Option C:
Type II error reflects the situation where the test is not sensitive enough to detect the existing effect, perhaps due to small sample size or high variability. Researchers strive to keep this error reasonably low by designing adequately powered studies. Because the stem explicitly mentions failing to reject a false null hypothesis, Type II error is the correct completion.

Option D:
Effect size measures the magnitude of a difference or relationship, independent of sample size, and indicates practical significance. It is not an error probability and does not describe the decision-making mistake outlined in the stem. Consequently, effect size cannot be the right answer here.

Statements A, B, C and E identify the major sections usually expected in a research proposal. A is correct because a proposal must explain the background and rationale for the study, and B is accurate in noting that clear objectives or questions guide all subsequent decisions. Statement C is true since the proposal must spell out methodology and tools, while E is also true because many formal proposals require a time schedule and budget. Statements D and F are false: ethical issues are typically addressed and proposals are often scrutinised by supervisors, committees or funding agencies.

Option A:
Option A is correct as it selects A, B, C and E, which together reflect the core structure of a research proposal. It intentionally excludes D and F, which wrongly suggest that ethics are ignored and that proposals are not subject to review. This combination therefore contains all and only the true statements given.

Option B:
Option B leaves out statement E, ignoring the fact that proposals, especially in funded research, often include realistic schedules and budget details. Although A, B and C are correct, omitting E makes the set incomplete and not fully representative of standard proposal practice.

Option C:
Option C wrongly includes statement D, which claims that ethical considerations are never mentioned. Modern research standards require explicit discussion of ethics in proposals, so combining D with correct statements produces a flawed option.

Option D:
Option D omits statement A and includes F, which incorrectly suggests proposals are never scrutinised by others. By dropping a true statement about background and rationale and adding a false claim about the proposal’s audience, this combination becomes inconsistent with good research practice.

The independent variable is the factor that the researcher intentionally varies or manipulates in an experiment to determine its causal effect on the dependent variable. It represents the presumed cause in a cause–effect relationship. By changing the levels of the independent variable, researchers can observe corresponding changes in outcomes. Because the stem specifies the variable that is deliberately manipulated to examine its effect on another variable, it correctly refers to the independent variable.

Option A:
Control variables are held constant across conditions to prevent them from influencing the dependent variable, but they are not the primary variables being manipulated to test hypotheses. Their purpose is to strengthen internal validity rather than to represent the main cause under investigation. Thus, control is not the right term for the manipulated variable described in the question.

Option B:
Extraneous variables are any variables other than the independent variable that may affect the dependent variable if not controlled or randomised. They are potential sources of error or alternative explanations for observed effects. Since the stem describes a variable that is deliberately manipulated, extraneous does not fit.

Option C:
Independent variables are central to experimental design because manipulating them allows researchers to test causal hypotheses in a controlled way. For example, different teaching methods can serve as independent variables when studying their impact on achievement. This focus on deliberate manipulation matches the description in the stem, making independent the correct answer.

Option D:
A moderator variable is one that affects the strength or direction of the relationship between an independent and dependent variable, but it is not necessarily the variable being experimentally manipulated in the main design. Moderators help explain under what conditions a particular effect occurs. Therefore, moderator is not the term the stem is seeking.

Statements A, B, C and E accurately describe the two designs. Cross-sectional studies take a snapshot of different groups at one time, whereas longitudinal studies repeatedly observe the same individuals, making them suitable for developmental questions. Longitudinal designs indeed face challenges such as participant attrition. Statement D is false because cross-sectional designs are less appropriate than longitudinal ones for examining change within individuals over time.

Option A:
Option A is incomplete because it omits E, thereby failing to recognise a major practical limitation of longitudinal research, namely attrition. Without E, the comparison between the two designs lacks a key practical dimension.

Option B:
Option B is incorrect because it includes D, which contradicts the accepted view that longitudinal designs are more appropriate for studying individual change, and it also omits E. This combination therefore misrepresents the strengths and weaknesses of the designs.

Option C:
Option C is correct as it groups all the true statements about how data are collected, what questions can be answered and what difficulties may arise in longitudinal research. It excludes D, which wrongly elevates cross-sectional designs for studying individual change over time.

Option D:
Option D is wrong because it includes D and omits A, thereby misclassifying the suitability of cross-sectional designs for change studies and neglecting a basic definition of cross-sectional data collection. This leads to an inaccurate comparison of the two designs.

The power of a statistical test is the probability that it will detect an effect and reject the null hypothesis when the null is actually false. High power means a lower chance of committing a Type II error, or false negative. Power is influenced by factors such as sample size, effect size, variability in the data and the chosen significance level. Because the stem describes the probability of correctly rejecting a false null hypothesis, it is defining the power of the test.

Option A:
Significance level, often denoted by alpha, is the probability of committing a Type I error—that is, rejecting a true null hypothesis. It sets the threshold for how much risk of a false positive the researcher is willing to accept. Since the stem is about correctly rejecting a false null, significance level is not the correct answer.

Option B:
Effect size quantifies the magnitude of a relationship or difference, providing information about practical importance rather than decision error probabilities. While larger effect sizes generally increase power, effect size itself is not the probability of correct rejection. Thus, effect size does not fit the description in the question.

Option C:
Confidence level refers to the proportion of times that confidence intervals constructed from repeated samples would contain the true population parameter. It is related to estimation rather than hypothesis testing power. Therefore, confidence level is not the term that describes the probability of correctly rejecting a false null hypothesis.

Option D:
Power reflects the sensitivity of a test to detect real effects, guiding researchers in planning sample sizes to avoid underpowered studies that may miss meaningful results. Given that the stem explicitly refers to correctly rejecting a false null hypothesis, power is the appropriate completion.

Statements A, B and D describe essential properties and roles of a good hypothesis, whereas C and E are false. A sound hypothesis must be testable and open to falsification, be clearly written and guide the selection of data and analytic strategies. It is not required that the hypothesis be proven true; disconfirmation can also be informative. Moreover, correlational studies often do formulate hypotheses about the direction and strength of relationships, so E is incorrect.

Option A:
Option A includes only A and B, omitting D, which explains how hypotheses guide data and analysis decisions. Without D, the option does not fully reflect the functional role of hypotheses in research design.

Option B:
Option B is correct because it gathers together the three statements that reflect both the logical and practical aspects of hypothesis formulation. It excludes C, which overemphasises “proving true”, and E, which understates the use of hypotheses in correlational designs.

Option C:
Option C includes C, which wrongly insists that hypotheses must always be proven true, and omits A, which emphasises testability and falsifiability. This combination therefore distorts the logic of hypothesis testing.

Option D:
Option D brings in E, which incorrectly claims that correlational studies do not require hypotheses, and omits B, thereby failing to stress clarity and conciseness. It cannot be accepted as correct.

Option E includes E and adds it to a set of otherwise correct statements, which undermines the validity of the combination. The presence of this false statement makes the option incorrect.

Quantitative research is characterised by the use of structured instruments, numerical data and statistical procedures to test hypotheses or answer questions. It often seeks to establish patterns, relationships or differences among variables in large samples. The emphasis is on objectivity, measurement and generalisability. Hence, research that focuses on numerical data and statistical analysis is appropriately called quantitative research.

Option A:
Historical research involves systematic study of past events using documents and records, and it may use both qualitative and quantitative data. However, it is defined by its temporal focus rather than by emphasis on measurement and statistics alone. Therefore, historical research is not the best completion.

Option B:
Quantitative research commonly uses experiments, surveys and quasi-experiments to collect data that can be summarised with descriptive and inferential statistics. Its strength lies in producing generalisable evidence and estimates of effect sizes. This orientation aligns exactly with the description in the stem.

Option C:
Phenomenological research is a qualitative approach that explores lived experience and the essence of phenomena from participants’ perspectives. It typically uses in-depth interviews rather than extensive numeric measurement, so phenomenological is not the correct answer.

Option D:
Ethnographic research immerses the researcher in a cultural or organisational setting to understand practices and meanings, relying heavily on qualitative observations and narratives. It is not primarily about numerical measurement and statistical testing, so ethnographic does not fit the stem.

Triangulation involves combining multiple data sources, methods, theoretical perspectives or investigators to study the same phenomenon. The rationale is that if different approaches converge on similar conclusions, confidence in the findings increases. Triangulation can be methodological (using surveys and interviews), data-based (using data from different times or settings) or investigator-based (having multiple researchers analyse the data). Because the stem highlights using multiple methods or sources to enhance credibility, it is describing triangulation.

Option A:
Standardisation refers to making procedures uniform across participants or settings, such as using the same instructions or instruments, to reduce variability and bias. While helpful for reliability, it does not by itself involve combining multiple sources or methods. Therefore, standardisation is not the right term for the process described in the stem.

Option B:
Triangulation helps offset the weaknesses of one method with the strengths of another, providing a more nuanced and robust understanding of the research problem. For example, quantitative survey data might be complemented by qualitative interviews that explain underlying reasons. This deliberate use of multiple approaches to reinforce credibility is exactly what the question refers to, making triangulation the correct answer.

Option C:
Stratification is a sampling procedure in which the population is divided into homogeneous subgroups (strata) and samples are drawn from each. Its purpose is to ensure adequate representation of subgroups, not to combine diverse methods or sources for credibility. Consequently, stratification does not fit the description given.

Option D:
Randomisation is the process of assigning participants to groups purely by chance, usually to control for confounding variables in experiments. While it contributes to internal validity, it does not involve the use of multiple methods or investigators as triangulation does. Hence, randomisation is not the appropriate completion here.

A list of tables and figures is placed near the beginning of a thesis and shows the titles and page numbers of each table and figure used in the document. It helps readers quickly locate specific visual displays of data or diagrams without scanning the full text. This list is particularly important in lengthy research reports. Because the stem refers to a preliminary section indicating page-wise positions of tables or figures, list of tables and figures is the correct answer.

Option A:
Bibliography, or sometimes references, lists the sources consulted or cited in the thesis and is usually placed at the end, not at the beginning. It does not show the page numbers of tables and figures but of books and articles. Therefore, bibliography is not the appropriate term here.

Option B:
The list of tables and figures provides an organised overview of the visual elements of the thesis, mirroring the function of the table of contents for chapters. It improves navigability for readers who may need to refer directly to specific data displays. This matches the description in the stem, so this option is accurate.

Option C:
Acknowledgements is a brief section where the researcher expresses gratitude to individuals or institutions that supported the work. It does not systematically list tables or figures, and its purpose is more personal than organisational. Hence, acknowledgements is not the correct completion.

Option D:
A glossary is a list of key terms and their definitions used in the thesis, often placed near the end. While helpful for clarity, it is unrelated to locating tables or figures by page number. As a result, glossary does not fit the stem.

Mixed methods research explicitly combines quantitative and qualitative approaches in a coherent design to capitalise on the strengths of both while compensating for their weaknesses. Researchers may collect numeric data through surveys and detailed narrative data through interviews, then integrate the findings during analysis and interpretation. This approach is often guided by a pragmatic philosophy that values multiple ways of knowing. Because the stem mentions purposeful integration of quantitative and qualitative methods, mixed methods research is the correct term.

Option A:
Qualitative research alone focuses on non-numerical data such as words, images or observations to understand meanings and experiences in depth. It does not by itself integrate quantitative methods, so qualitative research is not the best completion here.

Option B:
Mixed methods designs can be sequential, where one type of data collection follows the other, or concurrent, where both are collected simultaneously, but they always seek integration at some stage. This deliberate integration is exactly what the stem points to.

Option C:
Experimental research primarily uses quantitative methods to test causal hypotheses through manipulation and control, but it does not necessarily include qualitative components unless designed as part of a mixed methods strategy. Therefore, experimental research is not the appropriate answer.

Option D:
Ethnographic research is a qualitative approach that involves long-term immersion in a cultural setting to understand norms and practices, and while it may include some quantitative data, it is not inherently defined by systematic integration of methods in the mixed methods sense. Hence, ethnographic research is not the correct term.