UGC NET Questions (Paper – 1)

The population in research is the complete set of individuals, events or objects that share certain characteristics and about which the researcher wants to draw conclusions. It forms the universe from which the sample is selected. When findings from the sample are generalised, they are said to apply to this population. Because the stem describes the entire group to which results are to be generalised, the correct term is population.

Option A:
A sample is a subset of the population selected for actual observation or measurement. While inferences are drawn from the sample, the broader group that these inferences target is the population. Since the question explicitly refers to the entire group, not the subset, sample is not the correct choice.

Option B:
An element is a single member of the population, such as one individual respondent or one school. It does not refer to the whole group to which the researcher wishes to generalise. Therefore, element cannot complete the stem properly.

Option C:
A stratum is a homogeneous subgroup of the population created when using stratified sampling. Each stratum contains members who share certain characteristics, but it is only a part of the population, not the entire group. Consequently, stratum is not the right answer.

Option D:
Population encompasses all units that meet the specified criteria, whether they are people, institutions or events. Identifying the population clearly is crucial for designing sampling strategies and interpreting generalisations. This broad target group is exactly what the stem refers to, so population is the appropriate completion.

Non-sampling errors are all sources of survey inaccuracy that do not stem from the act of sampling but from the way data are collected, recorded or processed. They include interviewer bias, faulty questionnaire design, respondent misunderstanding and coding or tabulation mistakes. Such errors can be large and may not decrease simply by increasing sample size. Therefore, errors arising from inaccurate responses or defective tools are correctly termed non-sampling errors.

Option A:
Non-sampling errors encompass diverse problems such as ambiguous questions that confuse respondents, poorly trained interviewers and data entry errors. These issues affect the quality of information gathered from each selected unit and can seriously distort findings. Since the stem emphasises inaccurate responses and processing mistakes, this option is appropriate.

Option B:
Random error refers to unpredictable fluctuations that occur by chance and may be part of both sampling and measurement processes; it is not restricted to the specific systematic problems listed.

Option C:
Proportional error is not a standard category in survey methodology and does not describe the cluster of issues like tool defects and processing mistakes.

Option D:
Experimental error is associated with variability in experimental outcomes and does not specifically refer to survey response or instrument problems, so it is not correct 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.

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.

Population refers to the complete set of individuals, objects or events that possess certain specified characteristics and about which the researcher intends to draw conclusions. In many research texts, the word “universe” is used interchangeably with population to denote this full group. It is from this population or universe that the sample is drawn. Because the stem asks for a synonym of universe in methodology, population is the correct answer.

Option A:
A sample is a subset of the population selected for actual observation or measurement. It is necessarily smaller than the universe and is used to estimate population characteristics. Since universe refers to the whole group rather than a subset, sample cannot complete the stem correctly.

Option B:
Population encompasses all units that meet the criteria established by the researcher, such as all teachers in a university or all households in a district. When researchers say “the universe of study,” they usually mean this same full collection. Thus, population is the appropriate concept that matches the term universe in the question.

Option C:
Parameter is a numerical characteristic of a population, such as the true mean or proportion, not the entire group itself. Although related, parameter describes a value about the population rather than serving as a synonym for the population. Therefore, parameter is not the right choice.

Option D:
Statistic is a numerical characteristic computed from sample data, such as the sample mean, used to estimate parameters. It does not denote the entire set of units being studied and so cannot serve as a synonym for universe in this context. Hence, statistic is not the suitable completion.

A questionnaire is a widely used research tool consisting of a series of written questions or statements to which respondents record their answers. It is especially suitable for collecting data from large samples in surveys where face-to-face interaction may be limited. Questionnaires can contain closed-ended, open-ended or mixed items and are designed to be completed in a standardised way. Hence, the structured written set of questions described in the stem is called a questionnaire.

Option A:
A questionnaire provides uniform questions to all respondents, making comparisons and statistical analysis more straightforward. When properly designed and pretested, it can achieve high reliability and efficiency in large-scale studies. This matches the stem, so this option is correct.

Option B:
A rating scale is a specific type of item within a questionnaire or schedule where respondents indicate their position along a continuum, such as strongly agree to strongly disagree. It is not the entire set of written questions.

Option C:
An interview guide is a list of points or questions used by an interviewer in face-to-face or telephone interviews, and responses are typically oral, not written by the respondent. Therefore, it does not fully match the description.

Option D:
An observation schedule is a checklist or coding scheme used to record behaviours or events observed by the researcher, not a set of written questions answered directly by respondents.

Descriptive research aims to portray an accurate profile of persons, events or situations, often answering questions like what, where, when and how much, rather than why. Surveys, case studies and observational methods are commonly used to gather data on current conditions.

Option A:
This option correctly highlights the purpose of descriptive research as depicting current features of a phenomenon, providing a factual and systematic description without necessarily analyzing causal relationships.

Option B:
This option refers more to analytical or experimental research, which attempts to establish causal links between variables by manipulating conditions; that goes beyond simple description.

Option C:
Option C states that variables are always manipulated, which again characterizes experimental designs rather than descriptive studies that observe variables as they naturally occur.

Option D:
Option D confines attention to historical events. Historical research is a separate type; descriptive studies frequently focus on present conditions, not only the past.

A mailed questionnaire allows the researcher to send standardised questions to many respondents spread over wide geographic areas without needing to meet them face-to-face. Respondents can complete the instrument at their convenience and return it by post or electronically, making it relatively economical in terms of time and travel costs. This tool is particularly suitable for large-scale surveys where personal contact with each participant is difficult. Therefore, in the situation described in the stem, a mailed questionnaire is the most appropriate tool.

Option A:
A structured interview involves direct, often face-to-face interaction in which an interviewer asks a fixed set of questions and records responses. While it can yield rich data and permit clarification of doubts, it becomes time-consuming and expensive when the population is large and dispersed. Thus, it does not meet the requirement of low cost for a geographically scattered group.

Option B:
Focus group discussion brings a small group of participants together for guided discussion under the direction of a moderator. It is useful for exploratory, qualitative insights but is not an efficient method for collecting standardised written responses from large, scattered samples. Hence, it is not suited to the specific needs mentioned in the question.

Option C:
Participant observation requires the researcher to be physically present in the setting and engage with participants while observing their behaviour. This method is intensive and location-bound, making it impractical for large, widely dispersed populations. As a result, it does not satisfy the conditions described in the stem.

Option D:
A mailed questionnaire can be duplicated and distributed widely, and responses can be quantified for statistical analysis. Although limitations such as low response rates exist, it remains the best match among the options for collecting written input from a large, geographically scattered group at low cost.

Cross sectional studies examine a phenomenon at one point or short span of time, using data from different individuals or groups. They provide a snapshot of characteristics, opinions or behaviors in a population. Such designs are common in survey research when the primary interest is in describing current status rather than change over time.

Option A:
Option A describes longitudinal design, where the same participants are followed and measured across multiple time points to study change. This is conceptually distinct from cross sectional design.

Option B:
Option B correctly identifies cross sectional research as involving a single time of measurement across different participants. It captures the snapshot nature and the absence of repeated measures on the same individuals.

Option C:
Option C characterizes experimental research, which focuses on manipulation and control to infer causality, not merely on time dimension.

Option D:
Option D suggests a purely qualitative case study emphasis, which can be cross sectional or longitudinal but is not inherent to the definition of cross sectional design.

Statement C is the only wrong statement because survey data are not always free from response bias; they can be affected by social desirability, recall errors and misunderstanding. Statements A, B, D and E accurately describe survey research’s ability to reach large samples, use multiple modes, provide both descriptive and analytical information and study attitudes and self-reports. Therefore, C alone contradicts accepted knowledge about survey limitations.

Option A:
Option A is correct because it isolates C as the single misleading statement and affirms the truth of A, B, D and E. It recognises that response bias is a central concern in interpreting survey data, even when surveys are well designed.

Option B:
Option B is incorrect because it adds A to the list of wrong statements, even though A accurately states that surveys can reach relatively large numbers of respondents. Treating A as wrong underestimates one of the main advantages of survey methods.

Option C:
Option C is also wrong because it includes D as a wrong statement, despite D correctly noting that analytical surveys can explore associations and patterns beyond simple description. Misclassifying D fails to acknowledge analytic potential.

Option D:
Option D is incorrect because it groups B, C and E as wrong and therefore misrepresents both the available modes and the substantive focus of surveys. B and E are in fact correct descriptions of how surveys are conducted and what they can study.

A sampling unit is the basic unit that can be selected for inclusion in the sample, such as an individual, household or school. It is the entity about which the researcher collects data. Clearly defining sampling units is essential for precise sampling and analysis. Thus, the element about which information is collected is correctly termed the sampling unit.

Option A:
Option A clearly names the sampling unit as the fundamental unit of selection and observation. It may coincide with the unit of analysis in many studies. Because the stem asks for the basic element from which data are collected, this option is correct.

Option B:
Sampling error refers to the natural discrepancy between sample statistics and population parameters due to observing only a subset. It is an error term and not the basic element itself. So it does not fit the description.

Option C:
Sampling ratio is the proportion of the population that is included in the sample. It is a statistic, not the element on which information is collected. Hence, this option is not appropriate.

Option D:
Sampling interval is the fixed distance between selected units in systematic sampling, such as every 10th name. It is a procedure parameter rather than the unit of data collection. Therefore, it is not the correct answer.

Statements A, B, C and D identify key error sources in surveys, and F points to one mitigation strategy. A is true because coverage error arises when parts of the population are missing from the frame, and B correctly defines non-response error. C is accurate in linking measurement error to poor wording or misunderstanding, while D recognises that different modes of administration can affect responses. F is also true since repeated contacts and follow-ups can reduce non-response, whereas E is false because social desirability bias can distort answers and undermine validity.

Option A:
Option A omits statement F, thereby failing to recognise a practical method for reducing non-response error. Although A, B, C and D are correct, the lack of F makes the option less complete in terms of both diagnosis and remedy.

Option B:
Option B leaves out statement A, ignoring the important concept of coverage error, which can prevent some population members from being sampled at all. Without A, the picture of survey validity threats is incomplete, so this option is not acceptable.

Option C:
Option C excludes statement B, neglecting the impact of non-response error on survey conclusions. Even though it includes A, C, D and F, missing B means the combination does not cover all the major threats listed.

Option D:
Option D is correct because it includes A, B, C, D and F, summarising coverage, non-response, measurement and mode effects as well as follow-up strategies. It excludes E, which wrongly claims that social desirability bias has no effect on validity. Therefore, this combination contains all and only the true statements.

A cross-sectional study gathers data from a sample or several groups at one specific time, providing a snapshot of the population’s characteristics or relationships. It is widely used in survey research to estimate prevalence and compare subgroups. Because it does not follow participants over time, it is efficient and relatively economical. Therefore, a design where data are collected at a single point in time from different groups is properly termed a cross-sectional study.

Option A:
A longitudinal study follows the same group of participants over an extended period, allowing the researcher to examine changes and trends. It does not restrict data collection to a single time point, so it does not match the snapshot nature described in the stem. Hence, longitudinal study is not the correct answer.

Option B:
Cross-sectional studies can compare different age groups, institutions or demographic segments simultaneously, approximating developmental or group differences without waiting for time to pass. They are particularly useful for estimating current status indicators such as literacy rates or attitudes. These features align exactly with the description in the question.

Option C:
An experimental study manipulates an independent variable and typically involves at least two conditions or groups to test causality, often with pre- and post-measures. While it may sometimes use a single time of measurement, its defining feature is manipulation, not the single time-point snapshot. Therefore, experimental study is not the best completion.

Option D:
A case study is an in-depth investigation of a single case or a small number of cases using multiple data sources, often over a period of time. It does not necessarily involve drawing inferences about a larger population from a single time-point survey, so case study does not fit the stem.

Non-response bias occurs when the characteristics of non-respondents differ in important ways from those of respondents, leading to distorted estimates of population parameters. For example, if less satisfied individuals are less likely to answer a satisfaction survey, the results will be overly positive. This bias persists even with large sample sizes if non-response is systematic. Therefore, the bias described in the stem is correctly termed non-response bias.

Option A:
Sampling bias refers more broadly to systematic errors in the way the sample is selected, such as excluding certain groups entirely, but it does not specifically focus on the difference between respondents and non-respondents. Non-response is one particular source of sampling error, so sampling bias is too general here.

Option B:
Measurement bias arises when instruments or questions systematically overestimate or underestimate the true values, for instance due to poorly worded items or faulty devices. It is unrelated to whether people choose to respond or not. Hence, measurement bias is not the appropriate completion.

Option C:
Non-response bias undermines the representativeness of survey findings because missing data are not random but correlated with variables of interest. Strategies such as follow-up contacts or weighting adjustments are often used to reduce its impact. These aspects fit exactly with the description in the question.

Option D:
Response-set bias involves patterns such as acquiescence or social desirability in the way respondents answer questions, affecting the quality of obtained responses rather than whether people respond at all. Therefore, response-set bias does not match the situation described in the stem.