UGC NET Exam > UGC NET Videos > Data Interpretation for UGC NET > Sources, Acquisition & Classification of Data (Brief)
Sources, Acquisition & Classification of Data (Brief) Video Lecture | Data Interpretation for UGC NET
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FAQs on Sources, Acquisition & Classification of Data (Brief) Video Lecture - Data Interpretation for UGC NET
| 1. What are the different sources of data acquisition? |  |
Ans. The different sources of data acquisition include primary sources such as surveys, interviews, observations, and experiments, as well as secondary sources such as books, articles, reports, and databases.
| 2. How is data classified in the context of data acquisition and classification? | |
Ans. Data can be classified based on various criteria, such as the type of data (qualitative or quantitative), the level of measurement (nominal, ordinal, interval, or ratio), the nature of the data (continuous or discrete), and the purpose of the classification (categorical or numerical).
| 3. What is the importance of data acquisition in research and analysis? | |
Ans. Data acquisition is crucial in research and analysis as it provides the necessary information for making informed decisions and drawing meaningful conclusions. It helps in identifying patterns, trends, and relationships, which are essential for understanding various phenomena and addressing research questions.
| 4. How can primary data be collected for research purposes? | |
Ans. Primary data can be collected for research purposes through various methods such as surveys, interviews, observations, and experiments. Surveys involve administering questionnaires to a sample of respondents, while interviews involve direct interaction with individuals. Observations involve systematically watching and recording behaviors or events, and experiments involve manipulating variables to measure their impact on a particular outcome.
| 5. What are some common challenges in data acquisition and classification? | |
Ans. Some common challenges in data acquisition and classification include ensuring data reliability and validity, dealing with missing or incomplete data, managing large volumes of data, maintaining data privacy and security, and selecting appropriate classification criteria. These challenges require careful planning, implementation, and analysis to ensure accurate and meaningful results.
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