UGC NET Questions (Paper – 1)

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.

Statements A, B, D and E describe how ICT dashboards support institutional planning. Dashboards aggregate key indicators, visualisation reveals patterns, triangulation checks findings across sources and governance frameworks define how data are managed. Statement C is false because ignoring context can lead to misinterpretation, and F is false since data accuracy should be regularly verified. Thus the correct combination includes A, B, D and E only.

Option A:
Option A is incomplete because it omits E and so does not mention data governance, which is essential for responsible use of dashboards. A, B and D are true but insufficient.

Option B:
Option B is incorrect because it adds F, which wrongly claims that accuracy checks are unnecessary once a dashboard exists. Including this false assumption invalidates the option.

Option C:
Option C is incomplete because it leaves out A and includes only B, D and E. Without A, there is no explicit mention of the dashboard’s role in aggregating institutional data.

Option D:
Option D is correct because it integrates the descriptive, analytic and governance aspects of dashboard use while rejecting simplistic views that ignore context or data quality. It represents a sound approach to data-informed decision-making.

In this question statements A, B, D and E are accurate descriptions of data collection tools, while statement C is wrong. Questionnaires are indeed useful for reaching large, dispersed populations, structured interviews use a pre-planned schedule, standardised achievement tests measure learning outcomes and poorly prepared tools can threaten data quality. Statement C is false because observation is not only permissible in qualitative research but is actually one of its key methods. Therefore the only wrong statement is C.

Option A:
Option A claims that statement A is the only wrong statement, which is incorrect. Statement A is true because questionnaires are particularly suitable for large, dispersed groups where face-to-face contact is difficult. Since it misidentifies a correct statement as wrong and ignores the real error in C, this option cannot be accepted.

Option B:
Option B correctly singles out statement C as the only wrong statement. It recognises that observations are widely used in qualitative studies to capture natural behaviour and context. By not misclassifying any correct statements as wrong, this option gives an accurate response to the question.

Option C:
Option C groups statements B and C as wrong, but this is not accurate. Statement B is true because structured interviews rely on pre-planned, standardised questions administered in a uniform way. By labelling B as wrong along with C, this option fails to distinguish between accurate and inaccurate descriptions.

Option D:
Option D treats both C and E as wrong statements. While C is indeed false, E is correct since unvalidated or poorly prepared tools can lead to biased or unreliable data. Including E in the set of wrong statements makes this combination inconsistent with the underlying concepts.