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Two of the four levels of measurements are the Nominal Scale and Ordinal Scale. In statistical analysis, there are four levels of measurement to gauge the variables. These measurement scales can be categorized as qualitative and quantitative data.
The nominal scale and Ordinal scale are the 1st and 2nd level used in surveys, polls, and other statistical analysis in the field of market research analysis.
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The term nominal originates from the Latin word “nomen” and “nominalis” which implies the meaning “name”. Following the meaning, the nominal scale categorizes variables into distinct classification. The category is based on nomenclature and not on ranks or orders.
The numbers associated with the variables on a nominal scale are used solely to classify the data; it does not indicate rank or order.
For example, in the case of classification of gender in a survey, the responder selects the variable and not the number.
The numbers help to quantify the data for the final analysis and result.
The ordinal scale is the opposite of the nominal scale because in this measurement scale the variables are arranged into ranks and orders. However, the scale is simply used to put the variables into ranks and not examine the degree of difference between the variables.
For example, let’s say you went to a drama theatre and you are asked to fill a survey
On a scale of 1 -5, how much did you like the drama?
The numbers indicate the rank they are used to put the variables into order.
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Let’s say sweet, salty, and spicy when considered individually fall into the category of Nominal Scale.
However, when they are placed on the scale and put into order: Very Sweet, sweet, very salty, salty, very spicy, and spicy, they fall under the Ordinal Scale.
Nominal Scale: Gender, marital status, religion, race, hair color, country, etc are examples of Nominal Scale. They are all examples of the noun which do not require rank or order.
What is your country of origin?
Ordinal Scale: Ranks, customer satisfaction rating and degree, socio-economic status, education qualification, etc. are examples of the Ordinal Scale.
How happy are you with our courier service?
The variables in the nominal scale have no quantitative value associated with them. The variables are attributes and there is no need for arranging them in order or hierarchy.
Since the numbers associated are only used for data collection these data are grouped into categories. The data in the nominal scale are calculated by percentage or mode of distribution. You can also use graphical interpretation such as pie charts and bar charts to represent nominal data.
Four types of tests can be used to examine nominal scale data. These are,
The variables in ordinal scale are associated with numbers and sometimes we assign quantitative values. Although no arithmetic methods can be used to analyze ordinal data, you can use mode, median, and percentiles to measure the data. Table charts and mosaic plots can also be useful to interpret the ordinal data.
Non-parametric methods are also methods that can be used to measure ordinal data:
In order to collect nominal data, you can use a survey with questionnaires that include open-ended, close-ended, and multiple-choice questions. Data in nominal scale are descriptive in nature. The responses explain attributes or qualities.
Open-ended and multiple questions can give the responder freedom to put forth their perspective and not restrict them to a specific option.
Variables and options in ordinal scale require to be put into rank and hierarchy. Surveys that include scales such as the Likert scale, rating scale, and other such scales that can classify the options into ranks are better used to collect ordinal data.
Rating Scale: On a scale of 1 to 10 how much did you like the movie? (1 indicating bad and 10 indicating good)
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