In nominal measurement the numerical values just “name” the attribute uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball are measures at the nominal level.

Examples of nominal data include country, gender, race, hair color etc. of a group of people, while that of ordinal data includes having a position in class as “First” or “Second”. Note that the nominal data examples are nouns, with no order to them while ordinal data examples come with a level of order.

Some examples of variables that use nominal scales would be religious affiliation, sex, the city where you live, etc. One example of a nominal scale could be “sex”. For example, students in a class would fall into two possible classes, male or female.

Nominal: nominal is from the Latin nomalis, which means “pertaining to names”. It’s another name for a category. Examples: Gender: Male, Female, Other. Hair Color: Brown, Black, Blonde, Red, Other.

A Nominal Scale is a measurement scale, in which numbers serve as “tags” or “labels” only, to identify or classify an object. This measurement normally deals only with non-numeric (quantitative) variables or where numbers have no value.

Nominal, Ordinal, Interval, and Ratio are defined as the four fundamental levels of measurement scales that are used to capture data in the form of surveys and questionnaires, each being a multiple choice question….Summary – Levels of Measurement.

Height is a ratio variable, because the intervals between numbers are comparable and there is an absolute zero for height. it makes sense to say that a person 6 feet tall is twice as tall as a person who is 3 feet tall.

Nominal scale is a naming scale, where variables are simply “named” or labeled, with no specific order. Ordinal scale has all its variables in a specific order, beyond just naming them. Interval scale offers labels, order, as well as, a specific interval between each of its variable options.

Gender is an example of a nominal measurement in which a number (e.g., 1) is used to label one gender, such as males, and a different number (e.g., 2) is used for the other gender, females. Numbers do not mean that one gender is better or worse than the other; they simply are used to classify persons.

Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale. The essential point about nominal scales is that they do not imply any ordering among the responses.

Four Types of Scale Measurement. Scales of measurement are commonly broken down into four types, nominal, ordinal, interval, and ratio. Define each of the following four scales of measurement. In each of the following four cases, describe an example that in some broader sense may apply to your job situation or personal life.

Nominal scales are therefore qualitative rather than quantitative. Religious preference, race, and sex are all examples of nominal scales. Frequency distributions are usually used to analyze data measured on a nominal scale.

Another way to separate data is to classify it into four levels of measurement: nominal, ordinal, interval and ratio. Different levels of measurement call for different statistical techniques.

Central tendency. The mode, i.e. the most common item, is allowed as the measure of central tendency for the nominal type. On the other hand, the median, i.e. the middle-ranked item, makes no sense for the nominal type of data since ranking is meaningless for the nominal type.