Final answer:
Value labels are essential for nominal and ordinal variables to clarify the nature of the categorical or ordered data. They are less necessary for scale variables, such as interval and ratio, where mathematical operations are pertinent.
Step-by-step explanation:
Value labels are most likely to be necessary for nominal variables and ordinal variables. Nominal variables refer to data that is qualitative and categorical, such as names or labels, where the order is not meaningful.
On the other hand, ordinal variables have a set order, like rankings or grades, but the exact differences between the rankings are not measurable. While value labels can be used for all types of variables, they are especially pertinent for these types since they help in understanding the categorical or ordered nature of the data.
Scale variables, such as interval and ratio scales, have numerical values where mathematical operations are meaningful, thus reducing the necessity for labels compared to nominal and ordinal data.
For instance, labeling the categories with descriptive names in nominal data (like political affiliation) or providing a clear rank order in ordinal data (such as classifying athletic ability as superior, average, above average) allows for a better understanding and analysis of the data.
Therefore, c. Value labels are always necessary for all types of variables is correct.