Nominal Data and Ordinal Data

Nominal Data and Ordinal Data 

Main characteristic of qualitative data is data is gotten by the way of calculating, so that will not have decimal value. Example of qualitative data is gender data, blood group data, residence data, or work type data.

For example, gender data contains man and woman, contents of data is what is man amounts and woman. For the purpose will be done counting process, for example 15 mans and 25 womens. Here result of counting is not possibly is gotten number 15,4 mans and 25,1 womens, because man amounts and woman doesn't have decimal.

This elementary difference with quantitative data which able to have decimal value, because process gets data by the way of measuring.

To can be done process at data non metric number or xenon or qualitative data, the data must be turned into number, this process named categorisation process.

First qualitative data type is Data Nominal, with example of gender variable. At data with contents of gender someone, can be done giving of code for type gender, takes example code " 1" for man and code " 2" for woman.

Another example is residence data, with example : code " 1" for California, code " 2" for Texas, code " 3" for Arizona and code " 4" for Florida.

Other qualitative data is Ordinal Data, differs from nominal data, this data has order or sequence. For example there is position data of consumer, contents of the data logically can be compiled as follows : 

a. Hardly Agreing (code 1)

b. Agrees (code 2)

c. Neutral (code 3)

d. Disagree (code 4)

e. Hardly Disagree (code 5)

This shows elementary difference with nominal data which the data not necessarily be sorted. At nominal data, gender is having type man with code " 1" and gender is having type woman with code " 2" earns is just returned that is code man " 2" and code woman " 1", but at atypical ordinal data if sequence is random promiscuouslyly.