Which of the following best describes nominal level measures?

Nominal level measures are indeed characterized by their function of classifying variables without providing any quantitative or ordinal ranking. This means that they are used for labeling variables without any intrinsic order or numeric significance. Examples of nominal level measures include categories such as gender, race, or types of crime—each category is distinct, but there is no mathematical relationship between them, and one category is not considered higher or lower than another.

Furthermore, this characteristic underscores the idea that nominal measures merely collect data into groups (or categories) based on attributes, which cannot be subjected to mathematical operations such as addition or subtraction. Since these measures do not possess any inherent numerical meaning or rank, they do not allow researchers to perform statistical analyses that require order or quantitative assessment, making them distinct from higher levels of measurement like ordinal, interval, or ratio scales.

Understanding this distinction is crucial for correctly applying statistical techniques and interpreting data within the realm of criminal justice research. Thus, categorizing variables without providing mathematical interpretation aptly defines nominal level measures.