Understanding ID Numbers: Nominal or Ordinal?

The classification of an ID number as either nominally or ordinally is crucial in data analysis and statistical techniques. This article explores the nuances of these classifications and how ID numbers fit into each category.

What is ID Number Data?

ID numbers are unique identifiers assigned to individuals, items, or entities, such as Social Security numbers or employee IDs. These identifiers are primarily used for referencing and do not inherently carry any quantitative or qualitative value. An ID number does not provide any information about the ranking or ordering of the entities it represents.

Nominal Data

Nominal data is a type of categorical data where the categories have no intrinsic order or ranking. An ID number is a perfect example of nominal data. Categories within nominal data are mutually exclusive and can be used to label items or individuals without any quantitative relationship between them.

Examples of Nominal Scales

City of birth Gender Ethnicity Car brands Marital status

In any of these examples, while the categories are distinct and separate, there is no quantitative or qualitative order among them. For instance, a Social Security number is an identifier used for unique referencing and does not have a sequence or ranking.

Ordinal Data

Ordinal data, on the other hand, involves categories that can be ranked, but the differences between the ranks are not quantifiable. Unlike nominal data, ordinal data has a clear order but lacks interval measurements, meaning we cannot determine how much greater one category is compared to another.

Examples of Ordinal Scales

Top 5 Olympic medallists Language ability: beginner, intermediate, fluent Likert-type questions: very dissatisfied to very satisfied

In these cases, the order of the categories is important, but the exact difference between them is not quantifiable. For example, being a beginner in a language is categorically different from being intermediate, but we cannot quantify the exact gap between these categories in terms of proficiency.

ID Number Characteristics

When it comes to ID numbers, they are always classified as nominal data. An ID number serves as a unique identifier and does not imply any order or ranking among the entities it represents. Each individual or item will have a unique ID, and this ID does not change based on any external factors.

For instance, the Social Security number used in the United States is not assigned in any ordered sequence and does not rank individuals. Similarly, some employee numbers may be considered ordinal if they follow a sequential assignment, but in general, ID numbers are not designed for ranking or ordering.

Comparing ID Numbers and Ordinal Data

It's important to note that while ID numbers are classified as nominal data, they can sometimes be extended to ordinal data settings. For example, if an organization assigns employee IDs in a sequential order, the IDs might be treated as ordinal data because they carry a specific ranking. However, in most cases, ID numbers remain nominal.

Ordinal ID Number Assumptions

There is a common misconception that ID numbers can be treated as ordinal data, especially in certain contexts like James Bond ID. In such cases, the ID number might be labeled with a rank or sequence, but the ID itself is still a unique identifier that doesn't imply any ranking among different entities.

For instance, if James Bond IDs are assigned in a specific sequence (007, 008, 009, etc.), it might be tempting to treat them as ordinal data. However, the core purpose of the ID number remains to identify a specific character rather than rank them in any way. Each ID is unique and does not carry any inherent ranking.

Guerrero's Perspective

The term "nominal" is often used in the context of programming and data classification. In this context, nominal data refers to categories that can be distinguished but do not have any inherent numerical order. The term can also be applied to questions and responses in surveys where the order is not meaningful, but the response content is.

For example, in a questionnaire asking about employment status, responses could be labeled 1 (studying), 2 (employed), 3 (self-employed), 4 (unemployed). However, it could equally well be 3, 1, 2, 4, as the order is not significant; only the meaning of each response matters. The ID number, in this case, is a string of digits and may include letters or separators, and it serves as a unique identifier for each respondent.

Conclusion

In summary, ID numbers are best classified as nominal data because they serve as unique identifiers without any inherent order or ranking. While certain scenarios might extend the treatment of ID numbers to ordinal data, in most cases, the primary function of ID numbers remains to uniquely identify individuals or items, not to rank them or indicate any quantitative differences.