UGC NET Questions (Paper – 1)

In research, the population refers to the complete set of individuals or elements that share specified characteristics and about which the researcher wants to draw conclusions. The sample is selected from this population to provide data that, under proper sampling, can be generalised back to it. Defining the population clearly is a critical step in planning a study. Thus, the group described in the stem is called the population.

Option A:
A sample consists of a subset of the population chosen for actual observation or measurement. While findings are based on the sample, they are intended to represent the population, which is broader than the sample itself. Therefore, sample is not the correct completion.

Option B:
The population is sometimes referred to as the universe in statistical writing, but the term population is more commonly used in research methodology. It emphasises the target group for which inferences are intended. This matches the stem’s focus on generalisation, so this option is correct.

Option C:
A parameter is a numerical characteristic of a population, such as the true mean, which is estimated from sample statistics. It is not the group itself.

Option D:
A frame, or sampling frame, is the list of population members from which the sample is drawn. Although related to sampling, it is distinct from the entire group described as the population.

A frequency distribution table lists categories, class intervals or distinct scores along with the number of observations in each. It condenses large amounts of raw data into a form that reveals patterns such as concentration, spread and gaps. This table is fundamental to many descriptive statistical procedures. Because the stem describes a table that shows each category or score and the number of cases, frequency distribution is the correct term.

Option A:
Contingency tables display the joint frequencies of two or more categorical variables, showing how categories intersect. While they present frequencies, they focus on associations between variables rather than on the distribution of a single variable. Therefore, contingency table is not the exact match for the stem.

Option B:
A frequency distribution allows quick visualisation of how values are spread across the range and can be converted into graphs such as histograms or bar diagrams. It supports computation of measures like mode and median. These functions fit the description in the question, supporting frequency distribution as the appropriate answer.

Option C:
Cross tabulation is another term frequently used for contingency tables involving two or more variables, not for a simple one-variable frequency table. Since the stem mentions categories or scores of a single variable, cross tabulation is not the most suitable completion.

Option D:
Correlation refers to a statistical measure of the relationship between two quantitative variables, typically summarised by a correlation coefficient. It is not a type of table listing frequencies for categories or scores. Hence, correlation is an incorrect option here.

Quota sampling involves specifying how many respondents are needed from particular categories of a population, such as a fixed number of males and females or urban and rural residents, and then selecting conveniently available individuals until each quota is filled. It ensures some representation of key groups but does not use random selection. Because selection within categories is left to interviewer judgment or convenience, it is considered a non-probability method. Therefore, the technique described is correctly termed quota sampling.

Option A:
Quota sampling can approximate the distribution of important characteristics in the population, but results may still be biased because individuals within quotas are not randomly chosen. Interviewers might select more cooperative or accessible respondents. These features correspond to the stem’s focus on filling predetermined category numbers, making this option correct.

Option B:
Cluster sampling selects intact groups such as schools or villages, usually with random methods, rather than filling category counts with conveniently chosen individuals.

Option C:
Simple random sampling gives every individual an equal chance of selection by random procedures and does not involve non-random filling of predetermined quotas.

Option D:
Purposive sampling selects information-rich cases based on researcher judgment rather than meeting numerical targets for categories, so it is conceptually different from quota methods.

Research design is the structured framework that indicates the type of study, the sampling strategy, the instruments to be used and the procedures for data collection and analysis. It ensures that the study’s questions are addressed in a coherent and efficient way. A sound design minimises bias and maximises the validity and reliability of findings. Therefore, the comprehensive blueprint for how data will be handled is accurately called the research design.

Option A:
The research problem identifies what issue will be investigated but does not specify the detailed procedures for how the investigation will be conducted. It is the starting point rather than the operational blueprint. Thus, research problem is not the correct completion.

Option B:
The literature review summarises and critiques previous research on the topic to identify gaps and justify the study. It informs the design but is not itself the plan for collecting and analysing new data. Hence, literature review cannot substitute for research design in the stem.

Option C:
A research proposal outlines the intended study, including objectives, design, budget and schedule, usually for approval or funding. While it contains a description of the design, the term proposal refers to the entire document rather than specifically to the blueprint of methods. Therefore, research proposal is not the most precise term here.

Option D:
Research design integrates decisions about approach, sampling, instruments and analysis to create a logical pathway from questions to answers. This integrative planning role matches the description in the question, which is why research design is the correct answer.

Statements A, B, C and E together summarise accepted features of basic, applied and action research. Basic research indeed expands theoretical understanding without immediate application, applied research targets practical problems in specific contexts and action research is practitioner-led and improvement-oriented. Cyclical planning–acting–observing–reflecting is a recognised pattern in action research, so E is also correct. Statement D is false because basic research can be done in laboratories, simulated environments or classrooms, so the correct combination must include A, B, C and E but exclude D.

Option A:
Option A is incorrect because, although A, B and C are true, it omits statement E, which accurately describes the cyclical nature of action research. Leaving out E means not all correct statements are represented. Therefore this option does not capture the full set of correct statements.

Option B:
Option B includes A, C and E but leaves out statement B, which correctly defines applied research as addressing specific practical problems. Because B is also true, excluding it makes the combination incomplete and therefore incorrect.

Option C:
Option C correctly groups B, C and E but excludes statement A, which is a valid description of basic research. Since A is also a true statement, omitting it prevents this option from reflecting the complete pattern of correct statements.

Option D:
Option D is correct because it includes all four true statements A, B, C and E and omits D, which wrongly restricts basic research to natural classroom settings. It therefore contains all and only the correct statements specified in the question.

Option E is wrong because it includes D, which is a false statement, and simultaneously omits C, which is true. By mixing a false statement with true ones and leaving out one correct statement, this option cannot be accepted.

Convenience sampling chooses individuals who are easiest to reach, such as nearby students or colleagues. It does not follow random selection principles and often leads to samples that are not representative of the population. This method is quick and inexpensive but prone to bias. Therefore, selecting respondents because they are easily available is best described as convenience sampling.

Option A:
Systematic sampling selects every kth unit from an ordered list after a random start, which is a probability method. It does not simply pick whoever is easiest to access. So it is not the option described in the stem.

Option B:
Simple random sampling gives each population member an equal and known chance of selection, often using random numbers. It requires a sampling frame and is more rigorous than just asking whoever is nearby. Hence, it is not the correct answer.

Option C:
Stratified sampling divides the population into homogeneous strata and randomly samples from each. It aims at representativeness, not convenience. Therefore, it does not match the description.

Option D:
Option D, convenience sampling, exactly captures the practice of selecting participants because they are readily available, such as sampling friends, colleagues or students in one’s own class. This matches the question, so this option is correct.

A Type I error occurs when a researcher concludes that an effect or difference exists in the population when in fact the null hypothesis is true. It is analogous to a false positive decision. The probability of committing a Type I error is controlled by the significance level alpha, such as 0.05. Therefore, the error of rejecting a true null hypothesis described in the stem is called a Type I error.

Option A:
Type II errors occur in the opposite situation, when a false null hypothesis is not rejected, corresponding to a false negative; this is not the scenario given.

Option B:
Random error refers broadly to unpredictable fluctuations in data due to chance influences, not specifically to the decision error of rejecting a true null hypothesis.

Option C:
Systematic error involves consistent bias in measurement or procedure, which affects estimates but does not directly define the statistical decision about hypotheses.

Option D:
Type I errors are a central concern in hypothesis testing because setting a low alpha reduces their likelihood, though it may increase Type II errors. This link with rejecting true null hypotheses fits the stem exactly, making this option correct.

Statements A, B, C and E are all correct descriptions of different roles that variables can play in research. The independent variable is manipulated, the dependent variable is the measured outcome, mediators explain the mechanism linking the two and extraneous variables can disturb this relationship if not managed. Statement D is false because control variables are not varied; they are held constant to minimise their influence. Thus the correct combination must include A, B, C and E while excluding D.

Option A:
Option A includes A, B and C but omits E, thereby ignoring the important influence of extraneous variables on internal validity. Without recognising the potential confounding effect of extraneous variables, the description of variable types remains incomplete.

Option B:
Option B is correct because it brings together all four accurate statements about independent, dependent, mediating and extraneous variables. It excludes D, which incorrectly suggests that control variables are deliberately varied rather than controlled.

Option C:
Option C wrongly includes D, the false statement about control variables, and omits B, which correctly defines the dependent variable. This combination therefore both adds an incorrect assertion and drops a key correct one.

Option D:
Option D contains D alongside B and E. Since D misrepresents control variables, this option becomes invalid even though it includes two true statements.

Option E offers only C and E, leaving out the basic definitions of independent and dependent variables provided in A and B. Because it fails to include all the true statements, it cannot be the correct answer.

Statements A, B, D and E reflect current thinking about inclusive education research, while C is false. Such research aims at identifying and removing barriers to participation, often involves stakeholders through participatory approaches and uses qualitative case studies to explore lived experiences. Ethical safeguards for vulnerable learners are crucial. Statement C is wrong because inclusive education research can use qualitative, quantitative or mixed methods, not only purely quantitative designs.

Option A:
Option A is correct because it includes all the true statements about focus, participation, qualitative depth and ethics, while excluding C, which unjustifiably restricts the types of data used. It captures both methodological and ethical dimensions of this field.

Option B:
Option B leaves out E, failing to mention ethical obligations regarding dignity and confidentiality, which are especially important in research with vulnerable groups. This omission makes the option incomplete.

Option C:
Option C omits A, which highlights barrier removal as a central concern, and so underplays the core purpose of inclusive education research. As a result, it cannot be considered correct.

Option D:
Option D includes C, thereby adopting the incorrect view that only quantitative data are acceptable, even though qualitative insights are vital in inclusion studies. The presence of C invalidates this combination.

Option E also retains C and thus repeats the same error, even though it contains other true statements, making the option unacceptable.

Primary data are original information gathered first-hand by the researcher through methods such as surveys, experiments, observations or interviews. They are collected specifically to address the objectives of the current study, rather than being reused from previous investigations. Because they originate at the source in direct contact with respondents or phenomena, they are called primary. The stem clearly describes such directly collected data, so primary is the correct term.

Option A:
Secondary data are information that already exist, having been collected earlier by other researchers, institutions or agencies for different purposes. Examples include census reports, published statistics and previous research datasets. Since the stem emphasises data collected directly by the researcher for a specific current study, secondary data do not fit.

Option B:
Derived data might refer to values computed from existing data, such as indices or ratios, rather than raw observations collected in the field. Although derived data can be based on primary data, the stem focuses on original collection rather than on computations, so derived is not accurate here.

Option C:
Primary data are tailored to the research questions and allow control over how variables are defined and measured. They can be more time-consuming and costly to collect but often provide better alignment with the study’s objectives. For these reasons, the type of data described in the stem is correctly labelled primary.

Option D:
Standardised data could suggest information measured using uniform procedures or scales, regardless of whether they are primary or secondary. It is not a standard term for classifying data sources according to whether they are directly collected. Thus, standardised data is not the correct completion.

Longitudinal design involves collecting data from the same participants at two or more points in time, sometimes spanning years, in order to track developmental trends or changes in behaviours and attitudes. This design is particularly valuable for understanding trajectories and causal processes that unfold over time. It can be prospective, following participants into the future, or retrospective, using historical records. Because the stem refers to repeatedly studying the same individuals or groups over an extended period to observe changes, longitudinal design is the correct term.

Option A:
Cross-sectional design gathers data from different individuals at a single point in time, comparing groups such as age cohorts, but it cannot directly reveal within-person change over time. Therefore, cross-sectional design does not fit the longitudinal focus described in the stem.

Option B:
Case study design examines a bounded case such as an individual, institution or programme in depth, often at one or a few time points. While some case studies can be longitudinal, the term case study alone does not specifically denote repeated measurements over an extended period, so it is not the best answer.

Option C:
Longitudinal designs can be demanding in terms of time, cost and participant retention, but they provide powerful insights into stability, change and developmental sequences that are not accessible through one-time measurements. This central feature of observing change over time aligns exactly with the question.

Option D:
Experimental design is characterised by manipulation of independent variables and control over extraneous variables to test causal hypotheses, and may be either cross-sectional or longitudinal. The stem, however, highlights repeated observations over time rather than manipulation, so experimental design is not the correct completion.

The table of contents presents the structure of the thesis by listing major chapters, sections and sometimes subsections with their page numbers. It helps readers locate different parts of the document quickly and understand its overall organisation. This section is typically placed near the beginning after the abstract and acknowledgements. Because the stem refers to a page listing chapter titles and their page numbers, table of contents is the correct term.

Option A:
An index appears at the end of some books and lists key topics alphabetically with page references, helping readers find specific concepts scattered throughout the text. It is not the standard front-matter listing of chapter titles. Therefore, index does not match the description in the question.

Option B:
The table of contents functions as a navigational map for the reader, showing the logical flow of argument and the relative placement of different chapters. It is a required element of formal theses and dissertations. These characteristics correspond exactly to the stem’s description, confirming table of contents as the right answer.

Option C:
Acknowledgements are placed near the front of the thesis but serve to thank individuals and institutions that supported the work; they do not list chapters or page numbers. Hence, acknowledgements is not the appropriate completion.

Option D:
A glossary is an alphabetical list of specialised terms and their definitions, usually at the end of the document. It clarifies vocabulary rather than guiding readers to chapter locations. Consequently, glossary does not fit the stem.

Statements A, B, C, D and F correctly describe aspects of quantitative data analysis, while E is false. Statement A is true because descriptive statistics use measures like mean and standard deviation, and statement B correctly notes that inferential statistics enable generalisation. Statement C is accurate since graphs such as histograms summarise distributions, and statement D is true because data often need to be coded before software analysis. Statement F is also correct in stating that test choice depends on measurement level and research questions, whereas E is false because even rigorous quantitative analysis cannot guarantee correctness if design is flawed.

Option A:
Option A includes only A, B and C, omitting D and F. While the included statements are correct, leaving out the need for coding and the dependence of test choice on measurement level and questions results in an incomplete description. Therefore this option cannot be the correct answer.

Option B:
Option B adds D but still omits F, failing to mention that test selection depends on measurement scales and the nature of questions. Although it captures more aspects than Option A, it still does not include all the true statements from the list.

Option C:
Option C contains B, C, D and F but omits A. Since A is a foundational statement about descriptive statistics, excluding it weakens the completeness of the combination. A correct answer must encompass all the true statements A, B, C, D and F.

Option D:
Option D correctly groups A, B, C, D and F and excludes E, which is the only false statement. It recognises both descriptive and inferential aspects, the role of coding, graphical summaries and principled test selection. As it contains all and only the true statements, this option is the correct answer.

Skewness indicates whether a distribution is symmetric around the mean or whether one tail is stretched out more than the other. Positive skewness means the tail is longer on the right side with more high-end outliers, while negative skewness means the tail is longer on the left. Assessing skewness helps determine whether parametric tests that assume normality are appropriate or whether transformations might be needed. Therefore, the statistic described in the stem is correctly called skewness.

Option A:
Kurtosis refers to the peakedness or flatness of a distribution relative to normal, not the degree of asymmetry between tails. Although related to distribution shape, it addresses a different aspect than the one in the stem.

Option B:
Skewness summarises how scores pile up toward one side of the mean and taper off on the other, affecting measures like mean and standard deviation. When skewness is large, median and non-parametric methods may give more robust results. These features match the description of asymmetry in the stem, so this option is correct.

Option C:
Variability is a general term for spread in data and is quantified by measures like variance and standard deviation; it does not specifically capture tail asymmetry.

Option D:
Central tendency statistics such as mean or median describe typical values, not the balance of tails, so central tendency is not the correct term here.

A flowchart is a schematic diagram that represents a process through a series of boxes, usually indicating activities or decisions, connected by arrows that show the order of steps. In research, flowcharts are often used to depict sampling procedures, data-processing steps or logical sequences in the design. They help clarify complex processes at a glance. Because the stem mentions boxes and arrows showing sequence of steps or decisions, flowchart is the correct term.

Option A:
A histogram is a bar-type graph used to display the frequency distribution of continuous data, not a representation of procedural steps. It focuses on data values rather than the logic of a process. Thus, histogram does not match the description in the question.

Option B:
A flowchart helps researchers and readers visualise how different stages in a study connect, reducing ambiguity and supporting clear planning. It can show alternative decision paths and outcomes. These features align perfectly with the stem, confirming flowchart as the appropriate completion.

Option C:
A pie chart shows proportions of categories as slices of a circle, useful for depicting relative shares but not for representing sequences of actions or decisions. Therefore, pie chart is not suitable for the situation described.

Option D:
A scatterplot displays pairs of quantitative data points on two axes to explore relationships between variables. It does not show procedural steps or decision paths. Hence, scatterplot is not the correct term here.