All questions of Sources, Acquisition & Classification of Data for UGC NET Exam

Understanding Quantitative Data
Quantitative data refers to information that can be quantified and expressed in numerical form. This type of data is essential for statistical analysis, allowing researchers to draw conclusions based on measurable evidence. Below are key characteristics of quantitative data:
Measurable and Numerical
- Quantitative data is always measurable, meaning it can be counted or measured.
- It is typically expressed in numbers, making it suitable for various forms of analysis, including averages, percentages, and statistical tests.
Types of Quantitative Data
- Discrete Data: Represents countable items, such as the number of students in a class.
- Continuous Data: Represents measurable quantities, such as height, weight, or temperature, which can take any value within a range.
Applications of Quantitative Data
- Used in various fields such as economics, psychology, and health sciences for data analysis.
- Enables researchers to conduct experiments and surveys, providing a solid basis for empirical research.
Comparison with Other Data Types
- Qualitative Data: This refers to non-numerical information that captures descriptions or qualities, like opinions and experiences.
- Categorical Data: A subtype of qualitative data, it includes categories or groups, such as gender or color, without numerical values.
In summary, option 'C', quantitative data, is the correct answer because it encapsulates all measurable data that can be expressed numerically, distinguishing it from qualitative or categorical data types. This distinction is crucial for effective data analysis and interpretation in research contexts.

A literature review involves reviewing existing academic journals, books, and publications to gather relevant data and insights. It helps researchers identify existing knowledge gaps, theoretical frameworks, and findings from previous studies related to their research topic.

Assessing the authenticity of secondary data sources is crucial because the data is collected for purposes other than the researcher's specific objectives. Researchers need to verify the reliability, validity, and currency of secondary data to ensure it aligns with their research needs.

Quantitative classification organizes data based on numerical variables, enabling statistical analysis and measurement. It categorizes data into discrete groups or intervals defined by numerical values, facilitating comparisons, trend analysis, and making statistical inferences.

Surveys involve administering structured questionnaires or interviews to gather responses directly from respondents. This method allows researchers to collect quantitative or qualitative data by asking specific questions related to their research objectives.

Security classification categorizes data based on its sensitivity to ensure appropriate protection measures are applied. It aims to safeguard data integrity, confidentiality, and availability, addressing potential risks associated with unauthorized access, disclosure, or manipulation.

Qualitative data classification captures subjective experiences, behaviors, or descriptive categories, providing rich contextual insights and understanding. It focuses on exploring meanings, perceptions, and social phenomena that influence human behavior and decision-making.

A literature review involves reviewing existing academic journals and books to gather relevant data and insights related to a specific research topic or area of study. It helps researchers identify existing knowledge gaps and theoretical frameworks to inform their research design and objectives.

Primary data offers authenticity because it is collected firsthand by researchers to address specific research objectives. This ensures data accuracy and relevance, enabling researchers to directly control data collection methods and processes.

Secondary data is cost-effective because it is already collected and available from existing sources such as government publications, academic journals, or industry reports. Researchers can access a broad scope of information without the time and resources required for primary data collection.

Qualitative classification categorizes data based on attributes or qualities to capture subjective experiences, behaviors, or descriptive categories. It focuses on understanding meanings, perceptions, and contexts that influence human behavior and decision-making.

Quantitative classification organizes data into measurable units based on numerical variables, facilitating statistical analysis, trend identification, and making quantitative comparisons across different groups or variables.

Primary data collection can be costly in terms of time, effort, and financial resources required to conduct surveys, experiments, or interviews to gather firsthand data.

Secondary data is cost-effective because it is already collected and available from existing sources such as government publications, academic journals, or industry reports.

Observations involve systematically recording behaviors, events, or phenomena as they occur in real-world settings. This method allows researchers to gather qualitative insights by observing and documenting natural behaviors without manipulating variables, providing rich contextual information for analysis.

Security classification categorizes data based on its sensitivity to ensure appropriate protection measures are applied, safeguarding data integrity, confidentiality, and availability.

Primary data is collected firsthand by researchers from original sources to address specific research objectives. This data is directly relevant and tailored to the research needs, ensuring authenticity and enabling researchers to control data collection methods and processes.