Solution

Qualitative ordinal data is used to categorize data into ordered levels, as with “Positive,” “Neutral,” and “Negative” in this case. While this type of data doesn’t have exact numerical value or intervals, it does imply a ranking or order that defines one category as greater or lesser than another. For instance, “Positive” can be seen as a higher level of satisfaction than “Neutral” or “Negative,” indicating an order that is critical for certain types of analyses, especially in sentiment analysis and customer feedback. Qualitative ordinal data allows data analysts to determine trends over time, such as an increase or decrease in positive feedback, which can aid in strategy adjustments. The other options are incorrect because: • Quantitative Continuous Data (Option 1) refers to data measured on a continuum, like height or weight, not qualitative categories. • Quantitative Discrete Data (Option 2) includes whole number counts, such as the number of purchases, which does not fit the descriptive, non-numeric nature of customer feedback categories. • Qualitative Nominal Data (Option 3) is non-ordered categorical data (e.g., types of fruits), so it lacks the inherent order present in ordinal data. • Structured Data (Option 5) refers to organized data formats like tables with rows and columns, which is not about categorizing sentiments with implied order.