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Qualitative & Quantitative Data

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Flashcards: Qualitative & Quantitative Data Flashcard | Data Interpretation for UGC NET

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FAQs on Flashcards: Qualitative & Quantitative Data Flashcard - Data Interpretation for UGC NET

1. What is the difference between qualitative and quantitative data?

Ans. Qualitative data is descriptive and conceptual, often used to capture characteristics, opinions, or experiences. It can include text, images, or sounds and is typically non-numerical. In contrast, quantitative data is numerical and can be measured or counted, allowing for statistical analysis. It often involves structured data such as surveys or experiments that yield numerical results.

2. How can qualitative data be collected for research purposes?

Ans. Qualitative data can be collected through various methods, including interviews, focus groups, observations, and open-ended survey questions. Researchers often use these techniques to gather in-depth insights and understand the perspectives of participants, which can be analyzed for common themes or patterns.

3. What are some examples of quantitative data?

Ans. Examples of quantitative data include measurements such as height, weight, temperature, or survey results expressed in numerical form, such as the percentage of respondents who agree with a statement. Other examples can include sales figures, test scores, and demographic data like age or income levels.

4. Why is it important to differentiate between qualitative and quantitative data in research?

Ans. Differentiating between qualitative and quantitative data is crucial because each type serves different research purposes. Qualitative data provides depth and context, helping to understand the 'why' behind behaviors and opinions, while quantitative data offers breadth and allows for statistical analysis, enabling researchers to generalize findings to larger populations.

5. Can qualitative and quantitative data be used together in research?

Ans. Yes, qualitative and quantitative data can be used together in a mixed-methods approach. This methodology allows researchers to benefit from the strengths of both types of data. For instance, quantitative data can provide a broad overview of trends, while qualitative data can offer deeper insights into the reasons behind those trends, leading to a more comprehensive understanding of the research topic.

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