UGC NET Questions (Paper – 1)

Action research is characterised by iterative cycles in which practitioners identify a problem, plan an intervention, act by implementing changes, observe the effects and then reflect on what happened. Reflection is crucial for interpreting results, learning from experience and deciding how to modify the next cycle. This process makes action research dynamic and responsive to context. Therefore, the missing step in the plan–act–observe– sequence is correctly filled as reflect.

Option A:
Generalising from results to wider populations is not the primary focus of action research, which emphasises local improvement rather than broad generalisation. Hence, generalise does not fit the established cycle.

Option B:
Reflect involves critical thinking about what worked, what did not and why, often incorporating feedback from participants and stakeholders. It leads to new plans for subsequent cycles and supports professional growth. This central role in the action research cycle matches the stem exactly, making this option correct.

Option C:
Randomise is a term associated with experimental design for assigning participants to groups, not with the internal reflective process of action research.

Option D:
Stabilise suggests ending change and maintaining a fixed state, which contradicts the cyclical, ongoing improvement orientation of action research.

A Likert scale is constructed by writing several attitude statements related to a construct and asking respondents to indicate their level of agreement or disagreement, typically on a five or seven point scale. Each response category is assigned a numerical value. The item scores are then summed to produce an overall attitude score for each respondent. This summated ratings procedure is central to Likert scaling.

Option A:
Option A correctly describes a Likert scale: multiple statements are rated on a multi point agreement scale, and their scores are summed to obtain a total attitude score. This captures both the structure and scoring method.

Option B:
Option B refers to semantic differential scales, which use bipolar adjective pairs like good–bad or strong–weak on a continuum. Although used for attitudes, this is a different scaling technique from Likert.

Option C:
Option C describes open ended questioning, which does not involve fixed categories or summation of ratings. Open responses are usually qualitative in nature.

Option D:
Option D describes a single yes or no item, which is merely a dichotomous question and not a Likert scale, since it lacks multiple graded response categories and summated scoring.

Content analysis is a research technique used to make replicable and valid inferences from texts or other meaningful matter to the contexts of their use. It involves systematically coding units of content into categories and then counting or analysing these frequencies. This allows researchers to quantify and interpret patterns in communication messages. Therefore, the systematic descriptive and quantitative study of message content is called content analysis.

Option A:
Content analysis requires clear definitions of coding categories and rules so that different coders can classify material consistently. It can be applied to newspapers, television programmes, social media posts and interview transcripts. These features correspond closely to the description in the stem.

Option B:
Ethnography is a qualitative method involving immersion in a cultural or social setting to understand practices and meanings through observation and participation. It does not primarily involve quantitative coding and frequency counts of communication content. Hence, ethnography is not the correct completion.

Option C:
A case study focuses on an in-depth examination of a particular case or small number of cases using multiple sources of evidence; it may use content analysis as one tool but is not itself defined as a communication content technique. Thus, case study is not appropriate here.

Option D:
Phenomenology explores lived experiences and the essence of phenomena from the viewpoint of participants, relying mainly on qualitative descriptions and interpretations rather than systematic quantitative coding of texts. Therefore, phenomenology does not fit the definition in the question.

A sample is a smaller group of units selected from a larger population so that information collected from it can be used to infer characteristics of the whole. Sampling makes research feasible when studying every member of the population is impractical or impossible. The quality of the sample strongly affects the accuracy of conclusions drawn from the study. Since the stem speaks of a subset drawn from the target population for making inferences, the correct term is sample.

Option A:
Population refers to the entire group of individuals, objects or events that share specified characteristics and about which the researcher wants to draw conclusions. It is not a subset but the full set of interest. Because the stem clearly mentions a subset drawn from the population, population cannot complete the statement.

Option B:
A sample is selected using some sampling design so that it reflects the key characteristics of the population as closely as possible. Data are collected from the sample, and statistical techniques are then used to estimate population parameters. This use of a subset for inference corresponds exactly to the description in the question, confirming sample as the correct option.

Option C:
Parameter is a numerical characteristic of a population, such as the true mean or proportion. It is not itself a group of units but a value that describes the population. The stem is looking for the name of the subset of units, so parameter is not appropriate.

Option D:
Universe is another term sometimes used interchangeably with population to refer to the total collection of elements under study. It does not denote a subset selected for research. Hence, universe cannot be the right completion of the stem.

A frequency distribution lists the distinct values or categories of a variable along with the number of observations in each. It is a fundamental tool in descriptive statistics because it summarises large datasets into a simple, interpretable form. This table can be presented in absolute frequencies, relative frequencies or percentages. Therefore, a table showing how many cases fall into each category is correctly called a frequency distribution.

Option A:
In a frequency distribution, categories may be individual values or class intervals, and the table can be used to create graphs such as histograms or bar charts. It helps identify patterns like skewness, modality and concentration of scores. These characteristics match the description given in the stem.

Option B:
A scatter diagram plots paired scores for two variables on a coordinate plane to visualise their relationship, not the count of cases within categories of a single variable. It does not take the form of a simple table of frequencies. Hence, scatter diagram is not the appropriate completion.

Option C:
A regression line summarises the linear relationship between two quantitative variables in a scatter plot and is used for prediction. It is an analytic tool, not a tabular display of frequencies for categories of a single variable. Therefore, regression line does not fit the question.

Option D:
A contingency table cross-classifies cases into categories of two or more variables simultaneously, showing joint frequencies. Although it is related to frequency distributions, the stem refers specifically to counts for each category of one variable, so contingency table is not the best answer here.

Statement D is wrong because convenience sampling does not guarantee high external validity; it often leads to biased samples that limit generalisation. Statements A, B, C and E are correct: external validity concerns generalisation, narrow or atypical samples restrict it, replication strengthens it and realistic settings can enhance ecological validity. Hence, the only wrong statement is D.

Option A:
Option A is incorrect because it also labels A, which properly defines external validity, as wrong along with D. Treating A as incorrect misrepresents a basic concept in research design.

Option B:
Option B is wrong because it groups B with D as wrong, even though B accurately describes how an atypical sample can limit generalisability. Misclassifying B obscures a key threat to external validity.

Option C:
Option C is also incorrect because it treats C, which rightly notes the value of replication, as wrong. Replication is a standard strategy to test the robustness of findings across contexts.

Option D:
Option D is correct as it singles out D as the only statement that wrongly exaggerates the merits of convenience sampling for generalisation, while implicitly accepting the rest as accurate.

Descriptive research is designed to portray accurately the characteristics or conditions of a situation at a particular point in time. It does not involve deliberate manipulation of variables but rather observes and records what exists. Surveys, opinion polls and observational studies are common descriptive designs. Since the stem refers to describing current status without manipulating variables, the correct classification is descriptive research.

Option A:
Experimental research deliberately manipulates one or more independent variables and observes the resulting changes in dependent variables, usually under controlled conditions. Its main purpose is to establish cause–effect relationships. Because the stem explicitly mentions no manipulation of variables, experimental research is not appropriate here.

Option B:
Historical research deals with past events and processes, relying on documents, records and other sources to reconstruct what happened and why. It does not focus on current status, so it cannot be the correct answer to a question about present conditions.

Option C:
Action research is conducted by practitioners such as teachers to solve immediate problems in their own settings through cycles of planning, acting, observing and reflecting. Although it may describe conditions, its primary aim is change and improvement rather than neutral description. Therefore, it does not fully match the stem.

Option D:
Descriptive research provides detailed information about “what is” in a given context, serving as a foundation for explanation and prediction. It respects the existing conditions and seeks to summarise them systematically. This aligns precisely with the scenario presented in the question, making descriptive research the correct choice.

A research design provides an overall plan that connects research questions with methods of data collection and analysis. It specifies the type of study, sampling strategy, tools, timing and procedures to be used. By planning these elements coherently, the design enhances internal validity and feasibility, ensuring that the study can meaningfully address its questions. Therefore it is often called a blueprint of research.

Option A:
Option A suggests random decisions, which contradict the idea of design as planned and systematic. Randomness at the planning stage can lead to weak or invalid studies.

Option B:
Option B correctly highlights that design is a logical plan linking questions, data and analysis. It underlines the purpose of design as providing structure and direction to the study before implementation begins.

Option C:
Option C confuses design with bibliography. References are important but are not a plan of procedures.

Option D:
Option D refers to raw data, which are outcomes of research activities, not the plan that precedes them.

Basic research, also called fundamental or pure research, seeks to extend theoretical understanding and generate new concepts or principles. It is driven by curiosity about underlying mechanisms and relationships rather than by a specific practical problem. Although its findings may later have applications, that is not its primary goal. Because the stem highlights developing or refining theory without immediate practical concern, basic research is the correct term.

Option A:
Basic research often takes place in controlled or simplified settings that allow precise testing of theories. It lays the groundwork on which applied and evaluative studies can build, making it central to the progress of science. This theoretical orientation fits the description in the stem very well.

Option B:
Applied research focuses on solving concrete, practical problems and informing decisions in real-world contexts. While it may use theories generated by basic research, its main orientation is towards application, not theory building. Hence, applied research is not the appropriate completion here.

Option C:
Action research is conducted by practitioners to improve their own practice through cycles of planning, acting, observing and reflecting. It is heavily practice-oriented and context-specific, differing from the more abstract, theory-focused orientation of basic research described in the stem.

Option D:
Evaluative research assesses the effectiveness or impact of programmes, policies or interventions, aiming to provide judgments or recommendations. It is strongly application-oriented and therefore does not match the definition of basic research given in the question.

Simple random sampling selects units from the population such that each has an equal and known probability of inclusion, often using random number tables or computer-generated lists. This method minimises selection bias and allows straightforward estimation of sampling error. When a sampling frame is complete and accurate, simple random sampling is conceptually the most basic probability technique. Thus, the method described in the stem is correctly called simple random sampling.

Option A:
Systematic sampling chooses every kth unit from an ordered list after a random start, which may still approximate equal probabilities but operates through an interval rather than pure individual random draws. It is therefore not identical to simple random sampling.

Option B:
In simple random sampling, combinations of units are also equally likely, which supports many standard statistical procedures based on randomisation theory. This equality of chance matches the stem’s description and makes this option correct.

Option C:
Cluster sampling selects groups rather than individuals as the primary units and may give individuals within larger clusters different probabilities of selection, depending on design.

Option D:
Purposive sampling is non-probability and relies on researcher judgment in selecting cases, so members do not have equal and known chances of inclusion.

Validity is about the accuracy and appropriateness of inferences made from test scores. An instrument has high validity when it captures the essence of the construct it purports to measure, such as critical thinking or research aptitude. Evidence for validity may come from content analysis, factor structure and relationships with other variables. Without validity, even reliable scores become meaningless.

Option A:
Option A refers to administrative convenience, which is practical but unrelated to the conceptual match between test and construct.

Option B:
Option B correctly expresses the central idea that validity reflects how well the instrument represents what it claims to measure. This focus on construct representation is emphasized in modern measurement theory.

Option C:
Option C deals with reliability, the consistency of scores, which is a necessary but not sufficient condition for validity.

Option D:
Option D points to physical appearance, which may affect motivation but does not determine whether the right construct is assessed.

Limitations are constraints or weaknesses in a study that the researcher cannot fully control, such as small sample size, non-response, measurement error or time constraints. These factors may influence the interpretation and generalisation of the findings. By explicitly acknowledging limitations, the researcher helps readers understand the context and boundaries of the conclusions. Because the stem refers to factors beyond the researcher’s control that may affect results, it is describing limitations.

Option A:
Limitations describe the practical and methodological challenges that could not be overcome despite reasonable efforts. They are not deliberate choices but inherent constraints of the research situation. This corresponds closely to the stem’s emphasis on factors beyond the researcher’s control, confirming limitations as the correct answer.

Option B:
Delimitations, in contrast, are boundaries that the researcher consciously sets, such as restricting the study to a particular region or age group. They are intentional decisions to narrow the scope, not uncontrollable factors. Therefore, delimitations does not fit the description in the question.

Option C:
Assumptions are conditions that the researcher accepts as true without direct evidence in the current study, such as believing that respondents answer honestly. While they can influence interpretation, the stem specifically highlights uncontrollable factors affecting results, which is better captured by limitations.

Option D:
Hypotheses are testable propositions about relationships between variables rather than descriptions of weaknesses or constraints. They guide the study’s analytic focus and are evaluated using data. Hence, hypotheses is not an appropriate completion for the stem.

Probability sampling methods ensure that every element in the population has a known, non zero chance of being selected. Simple random sampling is a fundamental probability method where each member has an equal chance of inclusion, often achieved by lottery or random number procedures. This enhances representativeness and allows the use of statistical inference.

Option A:
Option A, convenience sampling, is a nonprobability method where respondents are chosen purely on ease of access. There is no known probability of selection for each member of the population, so it does not qualify as probability sampling.

Option B:
Option B, purposive sampling, involves selecting cases based on researcher judgment and is also nonprobability in nature. It is suitable for specialized studies but does not provide equal chance to all population elements.

Option C:
Option C clearly matches the definition of probability sampling because it selects from a complete list using random procedures. This ensures each unit has a known probability of selection and supports generalization within calculated error limits.

Option D:
Option D, quota sampling, fixes proportions for certain categories but usually relies on convenience within those categories. Because selection within quotas is not random, it is considered nonprobability sampling.

Everyday teaching often reveals recurring difficulties, such as low engagement or poor achievement, that signal underlying issues worth investigating. When a teacher reflects on these difficulties and frames them in researchable terms, they become the basis for a research problem. This problem then guides systematic inquiry aimed at understanding causes and testing solutions. Thus, classroom difficulties are a rich source for identifying a research problem.

Option A:
A statistic is a numerical summary computed from sample data, such as a mean or percentage, and is derived during analysis rather than at the stage of identifying issues. It cannot by itself capture the complexity of classroom difficulties.

Option B:
A parameter is a numerical characteristic of a population, not the conceptual challenge that motivates the study. It is estimated after data are collected rather than serving as the origin of the research.

Option C:
A research problem grows out of felt difficulties that are expressed in clear, specific and feasible terms, such as exploring why a particular teaching strategy fails for certain learners. This transformation of experience into a problem statement aligns exactly with the stem.

Option D:
A sample consists of selected individuals or units that will be studied after the problem and objectives are defined. Sampling decisions depend on the nature of the problem, so sample is not the correct completion here.

Formulating a research problem begins with recognizing a broad area of concern that needs investigation. The researcher narrows this area by defining it clearly in terms of concepts, context and potential questions. Only after identifying and delimiting the problem area can detailed objectives and hypotheses be framed. Thus, problem identification and definition is the logical first step.

Option A:
Option A jumps to writing conclusions, which can only be done after the entire research process is completed. Treating it as a starting point reverses the natural order of research activities.

Option B:
Option B correctly focuses on recognizing and defining the broad problem area as the starting action. Without this step, later tasks such as review of literature, selection of method and data collection cannot be coherently planned. It aligns with standard sequences described in research methodology.

Option C:
Option C suggests collecting data before formulating the problem, which can lead to unfocused and irrelevant information. Data collection should follow clear objectives derived from a defined problem, not precede them.

Option D:
Option D deals with preparing references, which is part of report writing after the study. Citation style decisions are not central to the conceptual formulation of the problem itself.