Ordinal
Age group voting patterns in the last presidential election would be an example of nominal data. Take the age groups and describe if they tended to vote Democrat, Republican, Independent, and so on. Another example is take the same age groups and determine the brand of cars they typically purchase such as GM, Ford, Toyota, Kia and so forth. Ordinal data example would be to take the same age groups as above and determine highest percentage of education level (say from the Department of Education statistics) from the group such as High School, Technical School, Bachelor Degree, Master Degree, or Doctorate Degree (MD, PhD). Or, take the age same groups and ask then how they think Obama is doing as president: Good, Neutral, or Poor.
It depends how we have computed %age. By and large, percentage is a summary statistic. Its a categorical variable (may be nominal or ordinal). That way its a discrete. In case of assay or yield computations it becomes a continuous variable. Naresh K Chawla nkchawla@gmail.com
ordinal
It is a ratio scale of measurement.
ordinal
Ordinal. Though more likely interval or even ratio scale.
illustrate how you can express the age of group of persons as {1}nominal,{2}ordinal data,{3} interval data,{4}ratio data
Year of birth is interval level of measurement; age is ratio.
Ordinal
Neither, age is at a ratio level of measurement.
I am not sure if I understand your question. I will rephrase it to: Should data collected on the ages of persons in a group be consider as nominal, ordinal, interval or ratio data? It is ratio. Now, let's try another question. A study finds that people with names beginning with the letter "a-k" are older than people with letters "l-z". In this case, the data collected on names in nominal data, but the ages are still ratio data.
Age is typically considered to be at the ordinal level of data, as it represents a certain order or ranking of individuals based on their age, but it does not have a true zero point. However, in some cases, it can also be treated as interval data depending on the context and analysis being conducted.
The data will be interval if you set up the age groups like 20-29; 30-39; 40-49 etc. If it is in groups such as teens, young people, middle aged, baby boomers, Gen X, etc it will be ordinal data.
Interval Data: Temperature, Dates (data that has has an arbitrary zero) Ratio Data: Height, Weight, Age, Length (data that has an absolute zero) Nominal Data: Male, Female, Race, Political Party (categorical data that cannot be ranked) Ordinal Data: Degree of Satisfaction at Restaurant (data that can be ranked)
A person's age is a ratio scale because we can say person A's age is twice older than person B's. Equal difference ages on a ratio scale all have exactly the same size. Moreover in age, 0 (zero) exists, which is feature of a ratio scale.
Age group voting patterns in the last presidential election would be an example of nominal data. Take the age groups and describe if they tended to vote Democrat, Republican, Independent, and so on. Another example is take the same age groups and determine the brand of cars they typically purchase such as GM, Ford, Toyota, Kia and so forth. Ordinal data example would be to take the same age groups as above and determine highest percentage of education level (say from the Department of Education statistics) from the group such as High School, Technical School, Bachelor Degree, Master Degree, or Doctorate Degree (MD, PhD). Or, take the age same groups and ask then how they think Obama is doing as president: Good, Neutral, or Poor.