UGC NET Questions (Paper – 1)

Tabulation involves organising raw data into tables that display frequencies, percentages or other summaries in rows and columns. This structured arrangement makes patterns clearer and facilitates subsequent statistical analysis. It is a key step between initial data collection and interpretation of results. Therefore, arranging data into rows and columns is correctly termed tabulation.

Option A:
Coding refers to assigning numerical or symbolic codes to responses for easier handling and analysis, often done before tabulation. While related, it is not the same as arranging data into tables.

Option B:
Tabulation transforms disconnected pieces of data into an organised format where comparisons among categories or groups can be quickly made. It is fundamental for preparing charts, graphs and statistical tests, matching the process described in the stem and making this option correct.

Option C:
Sampling concerns the selection of units from a population and occurs before data are collected, not at the stage of arranging data. Thus, it does not fit the question.

Option D:
Scaling involves designing and applying measurement scales to quantify attitudes or traits, which is different from arranging data into tables after collection.

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.

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.

Statistical analysis in quantitative research serves several purposes. Descriptive statistics summarize data through measures like mean, median and standard deviation. Inferential statistics help examine relationships and differences, and test hypotheses about populations based on sample data. Together, these techniques provide an evidence based basis for interpreting findings.

Option A:
Option A suggests replacing numbers with only qualitative description, which ignores the advantages of numerical precision and patterns that statistics reveal.

Option B:
Option B is correct because it combines summarization, exploration of relationships and hypothesis testing, which are central tasks of quantitative data analysis. It recognizes that statistics support but do not replace substantive interpretation.

Option C:
Option C claims that statistics eliminate the need for interpretation, which is false. Researchers must still relate numerical results to theory, context and practical implications.

Option D:
Option D assumes automatic acceptance of hypotheses, whereas statistical testing may lead to either rejection or failure to reject, depending on evidence.

Standard deviation is defined as the positive square root of the variance and summarises how far, on average, scores deviate from the mean. A larger standard deviation indicates greater dispersion, while a smaller one suggests that scores cluster closely around the mean. It is widely used in both descriptive and inferential statistics to quantify variability and to compute standard errors. Therefore, the spread measure described in the stem is known as the standard deviation.

Option A:
The standard deviation provides a measure in the same units as the original scores, making it easier to interpret than the variance. It plays a central role in normal distribution theory and confidence interval estimation. These properties align precisely with what the question is describing.

Option B:
The mean is the arithmetic average (a measure of central tendency), not a measure of dispersion computed as the square root of variance. It tells where the centre of the distribution lies, but it does not quantify spread around the mean. Therefore, mean does not fit the stem.

Option C:
Quartiles divide an ordered dataset into four parts, and measures based on quartiles (like interquartile range) describe spread differently. “Quartile” itself is not defined as the square root of variance, so it cannot complete the term “_____ deviation” correctly here.

Option D:
The coefficient of variation is the standard deviation divided by the mean (often expressed as a percentage) and is used to compare relative variability across different scales. It is derived from standard deviation rather than being the square root of variance itself. Hence, it is not the correct completion.

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.

Coding is the procedure by which responses, categories or variables are represented by numbers or symbols to facilitate data entry, storage and analysis. For example, “male” might be coded as 1 and “female” as 2, or different response options on a Likert scale may be assigned numeric values. This transformation allows statistical software to handle the data efficiently. Because the stem describes assigning numerical or symbolic values for computer analysis, it is referring to coding.

Option A:
Classifying typically involves sorting data into categories or classes based on shared characteristics, which may precede or accompany coding. However, classification does not necessarily involve assigning specific numeric codes for statistical processing. Thus, classifying is not the most precise completion for the description given.

Option B:
Coding creates a bridge between raw responses and the data matrix used in analysis. Well-designed coding schemes preserve the meaning of responses while making computations feasible. Since the stem explicitly mentions assigning numerical or symbolic values for analysis, coding is the correct answer.

Option C:
Scaling involves constructing measurement instruments that place respondents on a continuum for a particular construct, such as attitude or anxiety. While scaling may involve coded responses, the term refers more broadly to instrument design rather than the mechanical assignment of values for data entry. Therefore, scaling is not the right choice here.

Option D:
Sampling pertains to selecting participants or units from a population before data collection begins. It is unrelated to the post-collection step of assigning codes to responses, so sampling cannot complete the stem appropriately.

Statements A, B, C and E describe standard practices in data analysis, whereas D is false. Quantitative analysis relies on descriptive and inferential statistics, qualitative analysis uses coding and categorisation and mixed-methods studies often analyse each strand separately before integrating findings. In qualitative research, analysis and data collection can overlap, with emerging codes shaping further data collection. Statement D is wrong because quantitative analysis requires data; planning may occur earlier, but analysis itself cannot begin before data exist.

Option A:
Option A includes A, B and C but omits E, missing the important point that qualitative analysis is often iterative, beginning during data collection. Without E, the option does not fully represent the dynamic nature of qualitative analysis.

Option B:
Option B leaves out B, and thus fails to mention coding and categorisation as core qualitative techniques, even though it includes two other correct elements. This omission makes the description incomplete.

Option C:
Option C ignores A, thereby not stating that quantitative analysis normally uses descriptive and inferential statistics, which is fundamental in research methodology. As a result, it cannot be considered correct.

Option D:
Option D is correct because it combines all four true statements and excludes D, which misrepresents the timing of quantitative data analysis. It captures both the distinct and integrated aspects of quantitative and qualitative analysis.

Option E includes D, the false claim that quantitative analysis can start before data collection, and so the option becomes invalid even though it also contains true statements.

Effect size quantifies how strong an association is or how large a difference is between groups, using metrics such as Cohen’s d, Pearson’s r or odds ratios. Unlike p-values, which are heavily influenced by sample size, effect sizes provide information about the practical or substantive importance of findings. They are crucial for interpreting the real-world impact of results and for planning future studies through power analysis. Because the stem emphasises a standardised measure of magnitude that is largely independent of sample size, effect size is the correct term.

Option A:
Effect size allows comparisons across studies using different scales by expressing differences or relationships in common metrics. For example, a large effect size suggests that an intervention has a meaningful impact beyond mere statistical significance. This focus on magnitude matches the description given in the stem.

Option B:
A p-value gives the probability of obtaining a result as extreme as or more extreme than the observed one, assuming the null hypothesis is true. It does not directly convey the size of the effect and can be small even for trivial effects if the sample is very large. Thus, p-value is not the best completion here.

Option C:
Standard error measures the variability of a sample statistic as an estimator of a population parameter and is used in constructing confidence intervals, but it is not itself a measure of the magnitude of a relationship or difference. Therefore, standard error is not the correct answer.

Option D:
Confidence level indicates the degree of certainty associated with confidence intervals, such as 95 percent, and reflects how often intervals built in the same way would capture the true parameter if sampling were repeated. It does not describe effect magnitude, so confidence level cannot complete the stem properly.