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Ordinal data has an inherent order, i.e. ranking, in its possible values. For example 'poor, fair, good, excellent' is ordinal becaused there is an assumption that the four possible values are higher from one to the next. It can be coded as 1,2,3,4 but there is no assumption of equal spacing. Nominal data has no inherent ranking, only labeling-e.g. 'apple, strawberry, orange'. The choices are three levels with no assumed value. Any numerical coding does not reflect any quantitative meaning.

Georgette Asherman, Direct Effects, LLC

Q: What is the difference between ordinal and nominal data?

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its sick blad

illustrate how you can express the age of group of persons as {1}nominal,{2}ordinal data,{3} interval data,{4}ratio data

Neither, age is at a ratio level of measurement.

Bar charts are used to summarise nominal or ordinal data.

I would use Spearman and Kendall

Related questions

It is ordinal.

Gender is nominal. Nominal is categorical only; no ordering scheme. Ordinal level of measurement places some order on the data, but the differences between the data can't be determined or are meaningless.

Occupation is nominal data. There is not an order to the category occupation, so that eliminates ordinal and interval.

Education should be treated as a nominal scale because the years spent between two grades are not same for all the grades. i.e. difference between Jr. College and Sr. College isn't same as between graduation and post-graduation.

its sick blad

ratio

In qualitative variables, nominal data involves categories with no inherent order, such as colors or types of fruit. Ordinal data, on the other hand, includes categories that have a meaningful order or ranking, such as education levels or customer satisfaction ratings.

Age is none of the items listed. Age is ratio data.

illustrate how you can express the age of group of persons as {1}nominal,{2}ordinal data,{3} interval data,{4}ratio data

Kruskal-Wallis H test.

Neither, age is at a ratio level of measurement.

False. Data at the ordinal level can be either quantitative or qualitative. In ordinal data, the categories have a meaningful order or rank, but the difference between the categories is not necessarily equal.