UGC NET Questions (Paper – 1)

A directional hypothesis states both the existence and the direction of a predicted difference or relationship, such as claiming that one teaching method will produce higher achievement than another. Because it specifies which group will perform better, it allows the use of one-tailed significance tests. Such hypotheses arise when theory or prior research provides a clear expectation about the direction of effect. Hence, the hypothesis described in the stem is correctly termed directional.

Option A:
A directional hypothesis narrows the focus of testing by indicating exactly how groups are expected to differ, for example that experimental scores will exceed control scores. This explicit direction has implications for how critical regions are set in statistical tests. The stem clearly refers to predicting which group will score higher, so this option fits precisely.

Option B:
The null hypothesis usually states that there is no significant difference or relationship between groups and does not specify any direction of difference. It serves as a baseline assumption to be tested rather than a directional claim.

Option C:
A non-testable statement cannot be examined through empirical data because it is too vague or abstract, so it does not function as a proper hypothesis in scientific research. This is the opposite of carefully specifying a directional prediction.

Option D:
The term statistical hypothesis is a general label for any hypothesis formulated for testing with statistical procedures and may include both null and alternative forms. It does not necessarily imply direction, so it is too broad for completing the stem.

An interview schedule is a structured or semi-structured list of questions that the interviewer uses while interacting directly with respondents, either face-to-face or by phone. It ensures that similar information is obtained from all participants while still allowing for clarification when needed. The interviewer records responses during the interaction. Because the stem refers to a set of questions administered by the researcher in personal contact, it is describing an interview schedule.

Option A:
A mailed questionnaire is a self-administered instrument sent to respondents, who complete it on their own without an interviewer present. While it also consists of a set of questions, it does not involve face-to-face or telephone administration. Thus, mailed questionnaire does not match the description in the stem.

Option B:
An interview schedule provides a framework for the interviewer to follow, helping maintain consistency across interviews and reducing the chance of omitting key topics. It is specifically designed for situations in which the researcher directly asks questions and records answers. This is exactly the situation described in the question, so interview schedule is the correct answer.

Option C:
A checklist is a list of behaviours, attributes or items on which the observer records the presence or absence of each element, often in observational studies. It is not primarily a set of questions to be asked verbally to respondents. Therefore, checklist is not the best completion here.

Option D:
An observation schedule contains categories and guidelines for systematically recording behaviours or events as they occur in natural or controlled settings. It is used when the researcher observes rather than questions participants. Since the stem focuses on asking questions face-to-face or by phone, observation schedule is not appropriate.

Statement D is wrong because a systematic review must aim to include all relevant studies that meet the criteria, not just those that support the researcher's preferred position; selective inclusion would bias the review. Statements A, B, C and E are correct: systematic reviews follow explicit procedures, define criteria in advance, may use meta-analysis or narrative synthesis and rely on transparent reporting for credibility. Thus, D alone is the incorrect statement.

Option A:
Option A is incorrect as it treats both A and D as wrong, despite A correctly describing the structured procedures that characterise a systematic review. Rejecting A would undermine the definition of systematic work.

Option B:
Option B is wrong because it pairs B with D, although B accurately states that inclusion and exclusion criteria are set beforehand. Without pre-specified criteria, a review would not be truly systematic.

Option C:
Option C is also incorrect since it includes C with D, even though C correctly explains that both quantitative meta-analysis and qualitative narrative synthesis can be used to integrate findings.

Option D:
Option D is correct because it isolates D as the only misleading statement, implicitly affirming that A, B, C and E reflect the rigorous and transparent nature of systematic reviews.

Evaluation research systematically collects and analyses information about a programme’s activities, characteristics and outcomes to judge its effectiveness and efficiency. Its primary purpose is to inform decisions about continuation, modification or termination. It is highly applied and context-specific. Hence, research that judges the value of a programme for decision-making is correctly termed evaluation research.

Option A:
Basic research aims to expand theoretical understanding without immediate concern for practical decision-making. It is not focused on assessing the value of a specific programme or policy. Therefore, it does not fit the description.

Option B:
Option B, evaluation research, uses indicators such as goal attainment, cost-effectiveness and stakeholder satisfaction to assess merit and worth. It is directly linked to policy or administrative decisions. This matches the stem, so this option is correct.

Option C:
Historical research studies past events through documents and records to understand how and why they occurred. It does not usually evaluate current programmes for decision-making. Hence, it is not appropriate here.

Option D:
Ethnographic research tries to understand cultural patterns and meanings through immersion and observation. While it may inform judgments, it is not primarily about formal evaluation of programme effectiveness. So it is not the best option.

Systematic sampling involves ordering the population list, choosing a random starting point within the first k elements and then selecting every kth unit thereafter. This procedure is easier to implement than simple random sampling while still approximating randomness if the list has no hidden periodicity. The constant interval of selection is its hallmark. Hence, the kth selection method described in the stem is known as systematic sampling.

Option A:
In systematic sampling, the sampling interval k is determined by dividing the population size by the desired sample size. Provided that the ordering of units is not correlated with the variable of interest, this method yields a reasonably representative probability sample. These properties align closely with the description given in the question.

Option B:
Simple random sampling gives each unit an equal and independent chance of selection, usually by random numbers, but it does not use a fixed interval to pick every kth unit. Therefore, simple random sampling is not the precise term for the process mentioned.

Option C:
Cluster sampling selects intact groups such as schools or villages, not individual units at fixed intervals from a list. Its logic and implementation differ significantly from the systematic interval approach. Thus, cluster sampling is not correct here.

Option D:
Purposive sampling is a non-probability technique where the researcher deliberately selects cases judged to be most informative, without randomisation or a fixed interval rule. It does not match the ordered list and kth selection method described in the stem.

Quota sampling requires the researcher to identify relevant categories, such as male and female or different age groups, and decide how many respondents are needed in each. Field workers then select convenient individuals until the quotas for each category are filled. While this ensures some diversity, it does not use random selection, so it remains a non-probability method. Therefore, the technique described in the stem is known as quota sampling.

Option A:
Cluster sampling is a probability method involving selection of intact groups such as schools or villages. It does not focus on filling pre-set category counts with conveniently available individuals. Thus, it is not the correct answer.

Option B:
Purposive sampling selects cases judged to be especially informative or typical based on the researcher’s judgment. It does not rely on fixed numerical quotas for categories, so it does not match the description.

Option C:
Option C, quota sampling, is widely used in market research and opinion polls when a full sampling frame is unavailable. It imitates some features of stratification but without randomisation. This matches the idea of filling predetermined numbers in categories, making it correct.

Option D:
Systematic sampling is a probability method selecting every kth unit from a list. It does not aim to satisfy numerical quotas in predefined categories using convenience selection. Therefore, it is not appropriate here.

Ex post facto research, also known as causal-comparative research, attempts to identify possible cause–effect relationships by observing existing differences after the effect has occurred. The researcher selects groups that differ on some outcome and then looks backward to see what factors might be associated with those differences. Because the independent variable is not manipulated and events have already taken place, this design is non-experimental. The stem describes exactly this situation, so it is correctly named ex post facto research.

Option A:
Experimental research involves deliberate manipulation of one or more independent variables and control of extraneous variables to observe their effect on a dependent variable. It requires careful planning of treatments and conditions before measuring outcomes, not investigation after the fact. Since the stem emphasises that the effect has already occurred and there is no manipulation, experimental research is not the correct option.

Option B:
Survey research typically aims to describe attitudes, opinions or characteristics of a population by collecting data from a sample at one point in time. While surveys can investigate relationships among variables, they do not inherently focus on post-hoc causal comparisons based on pre-existing groups. Thus, the survey label does not capture the causal-comparative nature emphasised in the question.

Option C:
Ethnographic research is a qualitative approach that involves immersion in a cultural or social setting to understand participants’ behaviours and meanings from the inside. It is more concerned with rich description and interpretation than with testing causal hypotheses based on existing differences. Consequently, ethnographic research does not fit the stem.

Option D:
Ex post facto research acknowledges that some variables, such as gender or socio-economic background, cannot be manipulated for ethical or practical reasons. Researchers therefore study them after the outcomes have occurred, using careful group selection and statistical controls. This approach is exactly what the stem describes, making ex post facto the correct answer.

The Thurstone scale, or equal-appearing interval scale, involves having judges rate a large pool of statements on favourableness toward an attitude object. Statements with diverse scale values are then selected, and respondents indicate which statements they agree with. Their attitude score is computed as the mean or median of the scale values of the agreed statements. Thus, the method described in the stem is correctly called the Thurstone scale.

Option A:
Guttman scaling aims at cumulative ordering of items where endorsement of a higher-level item implies endorsement of all lower-level ones, and it does not rely on expert-assigned interval values in the same way.

Option B:
The Thurstone approach seeks to create intervals that appear equal in favourableness to judges, thereby approximating an interval scale for attitude measurement. Because the stem emphasises expert-assigned values and mean scores based on endorsed items, this option fits exactly.

Option C:
Likert scales rely on respondents’ ratings across ordered categories and summing scores, without expert judgment of individual item scale values, so they do not match the description given.

Option D:
The Bogardus scale focuses on social distance across relational categories and does not employ expert valuations of statements’ favourableness.

In any research study, the population is the complete set of units about which the researcher wants to draw conclusions. Because studying all units is often impractical, a smaller group called a sample is selected from this population. The sample should represent the population so that inferences from sample data can be generalized appropriately. This distinction is fundamental to sampling theory.

Option A:
Option A incorrectly claims that population is always smaller than sample, which is logically impossible because a sample is drawn from the population. Samples are subsets and therefore cannot exceed the population in size.

Option B:
Option B clearly describes population as the full group of interest and the sample as a subset selected for actual study. This matches standard methodological definitions and underscores the logic of generalizing from sample to population.

Option C:
Option C states that sample and population are identical, which contradicts the very meaning of sampling. If they were identical, there would be no need to use sampling techniques.

Option D:
Option D incorrectly restricts the term “sample” to qualitative data and “population” to quantitative data. In reality, both populations and samples can involve any type of data; the distinction is based on whether we consider all units or only a subset.

Qualitative research is concerned with how individuals interpret and make sense of their world. It typically employs methods like interviews, observations and document analysis to capture rich, descriptive data. Studies are often conducted in natural settings to preserve context. Therefore, focusing on meanings and experiences is a hallmark of qualitative approach.

Option A:
Option A describes quantitative research, which uses numerical data and statistical techniques. While valuable, it is conceptually different from qualitative inquiry.

Option B:
Option B correctly emphasizes subjective meanings, participant perspectives and naturalistic context. These features align with interpretivist and constructivist orientations that underpin qualitative research. The aim is depth of understanding rather than numerical generalization.

Option C:
Option C again refers to quantitative research, where large random samples help achieve statistical generalization. Qualitative work usually relies on smaller, purposively selected samples.

Option D:
Option D suggests avoiding contact with participants, which is contrary to most qualitative designs. Qualitative researchers often engage closely with participants to co construct understanding.

Probability sampling is defined by the use of random selection mechanisms that give each member of the population a known, non-zero chance of inclusion. This property allows researchers to estimate sampling error and to generalise findings from the sample to the population with quantifiable confidence. Techniques such as simple random, stratified and cluster sampling fall under this category. Because the stem explicitly mentions a known non-zero chance of selection for every element, it is describing probability sampling.

Option A:
Quota sampling is a non-probability method where the researcher fills pre-specified quotas for certain subgroups, often using convenience within each quota. Individual elements do not have known chances of selection, and probability theory cannot be applied to estimate sampling error. Thus, quota sampling does not satisfy the conditions stated in the stem.

Option B:
Purposive sampling involves selecting cases that are judged to be information-rich or particularly relevant to the research question, based on the researcher’s judgement. It does not assign known probabilities of selection to all population members, so it is not probability sampling. Therefore, purposive sampling cannot be the correct option.

Option C:
Convenience sampling selects units that are easiest to access, such as volunteers or people who are readily available. This method ignores the requirement that every member of the population have a known non-zero chance of selection, and it often yields biased samples. Consequently, convenience sampling does not meet the criteria in the question.

Option D:
Probability sampling uses objective randomisation procedures, such as random number tables or computer-generated random lists, to ensure fairness in selection. Because each element’s chance of being chosen is known, researchers can make statistically grounded inferences to the population. This matches exactly what the stem describes, making probability sampling the correct answer.

Statements A, B, C, E and F together provide a sound comparison between experimental and survey research. A is true because manipulation of an independent variable is central to experiments, and B correctly notes that surveys often use structured questionnaires with relatively large samples. Statement C is accurate in highlighting that experiments focus on cause–effect links, while E is true since both designs may use random sampling. Statement F reflects that surveys are commonly used to study attitudes, opinions and self-reported behaviours, whereas D is false because surveys are usually conducted in natural or field settings rather than always in laboratories.

Option A:
Option A excludes statement F, even though surveys routinely investigate attitudes and self-reports, making F a correct statement. Omitting F means that the description of survey research is incomplete and does not fully portray its scope. Therefore Option A cannot be the correct answer.

Option B:
Option B leaves out statement E, which recognises that random sampling can be applied to both types of designs. While A, B, C and F are correct, failing to include E results in a partial characterisation of sampling possibilities. This makes the combination incomplete.

Option C:
Option C omits statement A, ignoring the basic defining feature of experimental research, namely manipulation of an independent variable. It also includes B, C, E and F but fails to capture the full set of true statements about both designs. Hence this option is not acceptable.

Option D:
Option D is correct because it includes A, B, C, E and F, all of which are accurate descriptions of experimental and survey research. It properly excludes statement D, which wrongly confines surveys to laboratory contexts. As a result, this combination presents all and only the true statements in the item.

Deductive reasoning proceeds from the general to the specific. In research, it usually starts with an existing theory or general principle and then derives specific hypotheses that can be tested empirically, allowing researchers to check whether observations support or contradict theoretical expectations.

Option A:
This option correctly describes deduction as starting with a general theoretical framework and narrowing down to specific statements about what should be observed. It reflects the classic sequence used in many quantitative studies.

Option B:
This option illustrates inductive reasoning, where patterns and theories emerge from specific observations; the researcher builds generalizations after examining particular classroom situations.

Option C:
Option C is another example of induction. Generating categories from interview transcripts without prior assumptions involves moving from detailed qualitative data to broader concepts or themes.

Option D:
Option D also reflects an inductive, exploratory approach in which the researcher enters the field without a guiding theory and allows patterns to emerge from the data, rather than deriving specifics from a prior general principle.

A Type I error, also known as alpha error, happens when the null hypothesis is actually true in the population but the sample evidence leads the researcher to reject it. In other words, the test concludes that an effect or difference exists when in reality it does not. The probability of committing this error is controlled by the chosen significance level, such as 0.05.

Option A:
Option A describes failing to reject a false null hypothesis, which is a Type II error (false negative), not Type I.

Option B:
Option B matches the formal definition of Type I error: incorrect rejection of a true null hypothesis, leading to a false positive conclusion.

Option C:
Option C refers to accepting (or failing to reject) a true null hypothesis, which is the correct decision and not an error.

Option D:
Option D portrays correct rejection of a false null hypothesis, which is the desired outcome of hypothesis testing rather than an error.

A moderator variable influences the nature of the relationship between an independent and a dependent variable by strengthening, weakening or even reversing the effect under different conditions. For example, the impact of a teaching method on achievement might differ depending on students’ prior knowledge, which acts as a moderator. Recognising moderators helps researchers understand under what circumstances a treatment works best. Hence, a variable that alters the strength or direction of a relationship is correctly called a moderator variable.

Option A:
An intervening, or mediating, variable explains the mechanism through which an independent variable affects a dependent variable, focusing on the process rather than on altering the strength of the relation, so it is conceptually distinct from a moderator.

Option B:
Moderator variables are incorporated into analysis through interaction terms, allowing researchers to test whether the effect of the independent variable changes across levels of the moderator. This property aligns exactly with the description in the stem, making this option correct.

Option C:
The dependent variable represents the outcome or response being measured and does not modify the relationship between other variables; instead, it is influenced by them.

Option D:
Extraneous variables are uncontrolled factors that may influence the dependent variable and threaten internal validity, but they are not intentionally studied as variables that systematically change the strength or direction of the main relationship.