Data can be ordered in a variety of ways. Researchers, scientists, canvassers, market analysts and all manner of professions and academic disciplines use methodologies to order data for legibility and analysis. Nominal and ordinal data are two such kinds, with distinct characteristics of measurement that affects how they are used.
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Nominal data is, essentially, data that cannot be ordered. Text labels usually signify nominal data. However, numbers can be assigned to different data in a nominal set, but the numbers do not represent a system.
A simple example of nominal data is sex. Male and female are not ranked categories, but are named to distinguish them. A use of numbers in a nominal manner would be race numbers given to the participants in a marathon. They do not rank or order the participants, but they distinguish one from another.
Ordinal data is that which is ranked in a manner whereby the ranking does have meaning. Ordinal measurements typically measure elements in relation to each other, seeking to determine which ranks higher than others.
In a quantitative methodology an ordinal measure would seek to rank data for frequency of incidence. For instance, creating a table of the most common first names in a country. The most common name would be ranked one and those after it given numbers in descending order relative to their frequency.
Ordinal example 2
In quantitative methodologies, a numerical ranking system can determine strength of opinion. For instance, a questionnaire query would ask a respondent to answer a question with a numerical ranking, where one end of the scale indicates strong feeling and the other weak, with other options in between.
There are two other primary forms of data organisation besides nominal and ordinal. Interval data is that which is ranked according to criteria that are clearly delineated. Weights and time would be examples of interval data. Ratio data is that which is assessed on a scale with two endpoints, one of which is zero. An example of ratio data is percentages.
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