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The primary data type used for qualitative measurement, such as names and labels, is categorical data. Categorical data consists of values that represent distinct groups or categories rather than numerical values. For instance, names of cities, types of animals, or labels for different objects are all examples of categorical data.

Categorical data is further classified into nominal and ordinal types; nominal categories have no intrinsic ordering (like colors or names), while ordinal categories imply a rank order (like satisfaction levels). This type of data is essential for organizing information into meaningful categories, making it easier to analyze trends or relationships based on classification.

In contrast, numeric data refers to quantitative measurements and is used for calculations, while structured data is organized in a predefined format that can be easily processed by algorithms. Unstructured data, on the other hand, refers to information that does not follow a specific structure, often requiring more complex methods to analyze it. Thus, categorical data is the most suitable choice for qualitative measurements like names and labels.