UGC NET Questions (Paper – 1)

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.

Data coding involves transforming raw responses, especially to open or complex questions, into numerical or symbolic codes that represent categories or values. This step allows the data to be grouped, tabulated and processed by statistical software. Good coding schemes preserve essential distinctions while simplifying the data structure. Thus, the process described in the stem is correctly called data coding.

Option A:
Data entry refers to the act of inputting coded or numerical data into a computer or database but presupposes that coding has already been done where necessary. It is about transferring information rather than assigning symbols to categories in the first place. Therefore, entry is not the correct completion.

Option B:
Coding links qualitative or categorical information to numeric representations, making it possible to compute frequencies, percentages and other statistics. Clear and consistent coding is critical for valid analysis, which fits precisely with the description given in the question.

Option C:
Data cleaning involves checking the dataset for errors, inconsistencies or missing values and correcting or documenting them before analysis. While it may interact with coding decisions, it is conceptually distinct from the initial assignment of symbols or numbers. Hence, cleaning is not the best answer.

Option D:
Data mining refers to sophisticated techniques for discovering patterns or relationships in very large datasets, often using algorithms. It assumes the data are already coded appropriately and is not the name of the basic categorising process described in the stem.

Coding involves transforming raw responses into a format suitable for analysis, usually by assigning numeric or symbolic codes to each category of response. This step enables efficient data entry into statistical software and facilitates counting, classification and comparison. Predetermined coding schemes ensure consistency across cases and variables.

Option A:
This option describes tabulation, which occurs after coding and refers to arranging coded data in table form; it depends on coding but is not the same process.

Option B:
This option precisely defines coding as assigning symbols or numbers to categories based on a coding frame. It captures the preparatory role of coding in converting raw qualitative or categorical information into analyzable units.

Option C:
Drawing graphs and charts is part of data presentation and sometimes analysis. It uses data that have already been coded and tabulated; it is not itself the coding step.

Option D:
Writing descriptive summaries of interview transcripts is a qualitative analysis activity such as theming or narrative description. It may follow coding but is not what “coding of research data” means in standard quantitative processing.

Statements A, B, C and E correctly outline the nature and importance of coding and classification. Coding converts raw responses into analysable symbols, schemes should cover all possibilities without overlap, open-ended responses often need inductively developed categories and careful coding minimises error. Statement D is false because altering answers to achieve symmetry violates data integrity and leads to misleading results. Therefore, the combination including A, B, C and E but excluding D is correct.

Option A:
Option A includes A, B and C but omits E, thereby failing to state explicitly that precise coding contributes to error reduction and accurate analysis. Without E, the rationale for investing effort in careful coding is not fully conveyed.

Option B:
Option B contains B, C and E but leaves out A, so it does not mention the fundamental step of assigning symbols or numbers to responses. This omission makes the description of coding incomplete, even though the other points are true.

Option C:
Option C is correct because it brings together all four true statements, emphasising both the procedures and the benefits of systematic coding and classification. It rightly excludes D, which suggests unethical alteration of responses.

Option D:
Option D includes A, C and E but omits B, ignoring the requirement that categories be exhaustive and mutually exclusive. Without that requirement, coding categories could overlap or leave gaps, so this option is incomplete.