In an ordinal scale, how is data treated?

In an ordinal scale, data is treated in a way that allows for ranking, meaning that the values can be arranged in a meaningful order reflecting the relative standing of the observations. However, while this ranking provides insight into which observations are higher or lower than others, it does not convey the exact differences between these ranks. For instance, if you rank customer satisfaction as "satisfied," "neutral," and "dissatisfied," you know that "satisfied" is better than "neutral," but you cannot determine if the difference between "satisfied" and "neutral" is the same as between "neutral" and "dissatisfied." The ordinal scale focuses on the order of categories rather than the precise distance between them.

This characteristic distinguishes ordinal data from other scales. For example, nominal scales categorize data without any intrinsic order, and interval scales provide meaningful distances between values, while ratio scales include a true zero point that allows for direct comparisons of magnitudes. Therefore, the essence of ordinal data lies in its capacity for ranking, reinforcing that the differences between ranks are not meaningful in terms of measurement, making the second choice the correct explanation of how data is treated in an ordinal scale.