Final answer:
The measurement scale in question is the nominal scale level, which groups and labels qualitative or categorical data, reporting frequencies or percentages without implying any order or ability to perform calculations such as taking differences or sums.
Step-by-step explanation:
The measurement scale that groups and labels data only, and reports frequencies or percentages, is called the nominal scale level. This is the lowest level of measurement and is used for data that is qualitative or categorical in nature. Examples of nominal data might include categories like types of fruit (apples, bananas, oranges), colors (red, green, blue), or yes/no responses. It's important to note that nominal data cannot be ordered in a meaningful way, nor can it be used in calculations that involve ranking or taking differences. When we talk about organizing data in frequency tables, we're often referring to how often certain nominal data points appear within a dataset.
An example of nominal data might involve organizing the favorite subjects of students in a class. You could create a frequency table to show how many students prefer Mathematics, English, Biology, etc., and you might find that a certain percentage of the class prefers Mathematics over other subjects. However, it wouldn't make sense to say that Mathematics is 'greater than' or 'less than' English, since these categories don't have an inherent order.
When you collect quantifiable data but report it categorically, like recording quiz scores as grades (A, B, C, D, or F), this is another application of the nominal scale. In other words, each letter grade represents a range of numbers and serves as a label, but you cannot determine the difference in actual scores based on the letter grades alone.
Understanding the levels of measurement such as nominal, ordinal, interval, and ratio is crucial for selecting the appropriate statistical procedures for data analysis. These levels of measurement guide the types of calculations and the interpretations of results that can be derived from a dataset.