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Explanation: The given dataset represents qualitative data as it describes qualities or categories rather than measurable quantities. It is specifically ordinal because the responses have a clear, meaningful order or ranking (e.g., "Very Satisfied" is better than "Satisfied"). Ordinal data is common in surveys and ratings, where the order matters, but the intervals between categories are not uniform or defined. For example, the "satisfaction" levels indicate rank, but the difference between "Neutral" and "Satisfied" may not be equal to the difference between "Satisfied" and "Very Satisfied." This classification helps analysts choose appropriate statistical methods like median or non-parametric tests. Option A: Quantitative and Continuous data involve measurable variables with infinite possibilities (e.g., height, weight), which does not apply here. Option B: Quantitative and Discrete data involve countable variables like the number of cars or students, but the survey responses are not numeric. Option C: Qualitative and Nominal data have no inherent order (e.g., colors, genders). The survey responses here have a hierarchy, making them ordinal, not nominal. Option E: Semi-structured data refers to formats like JSON or XML, which is unrelated to this survey data.
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