In Statistics, data measurement scales are of four types namely as nominal, ordinal, interval, and ratio. Each data type has its different way of gathering the data, and hence different mathematical and logical operations are applied to them. Different analysis are performed on each data type. So, the researcher needs to understand the data type and the possible applicable analysis on it. In this article, Nominal and Ordinal data types are discussed. The major difference between these types is the “degree difference” from one classification to another.
Data on no degree classification means that the data is nominal. Data with degree classification (in an orderly manner) means that the data is ordinal. The following article shared by experts of PhD dissertation writing services aims to provide detailed insight into the ordinal and nominal data types along with examples. The major and minor differences between nominal and ordinal data will be provided further as well.
The data in the above examples have a specific order, but the exact difference is not certain among the ordinal variables. When ordinal data is used in statistical analysis, they are known as ordinal variables. From ordinal variables, conclusions may be drawn whether there exists a difference in the ordinal variables or not. They are frequently found within questionnaires and surveys. A Likert scale is commonly used in the collection of ordinal data. The descriptive statistics are applied to the ordinal variables, including proportions, frequencies, percentages, central point (median or mode), and summary statistics. They can be visualised by pie and bar charts.
The data in the above examples can only be labelled and categorised, but it cannot be ordered. When nominal data is used in statistical analysis, they are known as nominal variables. Nominal variables are only observed. No mathematical or logical operations are applied to them. The information inferred from nominal variables is that they are equal or not equal to one another. The information thus retrieved is used to group them. Descriptive statistics are applied to the nominal variables including proportions, frequencies, percentages, and central point (mode). They can be visualised through pie and bar charts.
Differences between Nominal and Ordinal data.
Data on no degree classification means that the data is nominal. Data with degree classification (in an orderly manner) means that the data is ordinal. The following article shared by experts of PhD dissertation writing services aims to provide detailed insight into the ordinal and nominal data types along with examples. The major and minor differences between nominal and ordinal data will be provided further as well.
Ordinal Data:
The type of data which can be classified and ordered is the ordinal data. Order is naturally taking place among variables, but the difference is unidentified. Ordinal data can only be observed. They cannot be measured and are ordered. They have no purposeful zero and are nonequidistant. There is a “subtype” of the ordinal data, which consists of only two ordered categories, known as “Dichotomous Data”. Examples of the ordinal data sets include the following; Good (Very Good, Excellent, Outstanding,….).
 Size (Small, Medium, Large, Extra large,…).
 Exam Grade (A+, A, B, C,….).
 Motivation degrees (High, Moderate, Low).
 Attitude degrees (Favourable, Neutral, Unfavourable).
 Measuring satisfaction from a scale (0 to 10).
Figure 1: Examples of the Ordinal Scale. Source: (Types of Data Measurement Scale, Ordinal Data). 
The data in the above examples have a specific order, but the exact difference is not certain among the ordinal variables. When ordinal data is used in statistical analysis, they are known as ordinal variables. From ordinal variables, conclusions may be drawn whether there exists a difference in the ordinal variables or not. They are frequently found within questionnaires and surveys. A Likert scale is commonly used in the collection of ordinal data. The descriptive statistics are applied to the ordinal variables, including proportions, frequencies, percentages, central point (median or mode), and summary statistics. They can be visualised by pie and bar charts.
Nominal Data:
The type of data which can be categorised or labelled (named), is the nominal data. The word nominal originated from the Latin language, “Namen, meaning name”. The data can be categorised based on names only. Nominal data can only be observed. It cannot be measured or ordered. They have no purposeful zero and are nonequidistant. There is a "subtype" of the nominal data, which consists of only two labelled categories, known as “Dichotomous Data”. Examples of the nominal data include the following; Nationality (British, Austrian, French, American,….).
 Gender (Male, Female, Transgender).
 Music style (Classical, Rock, Jazz, HipHop,….).
 Religion (Muslim, Jew, Christan, Jew,…).
 Favourite Weather (Winter, Autumn, Spring, Summer).
 Favourite Country (Spain, Egypt, Turkey, Germany,…..).
 Favourite Day of the Week (Monday, Friday, Saturday, Sunday,….).
Figure 2: Examples of the Nominal data. Source: (Types of data Measurement Scale, Nominal Data). 
The data in the above examples can only be labelled and categorised, but it cannot be ordered. When nominal data is used in statistical analysis, they are known as nominal variables. Nominal variables are only observed. No mathematical or logical operations are applied to them. The information inferred from nominal variables is that they are equal or not equal to one another. The information thus retrieved is used to group them. Descriptive statistics are applied to the nominal variables including proportions, frequencies, percentages, and central point (mode). They can be visualised through pie and bar charts.
Differences between Nominal and Ordinal data.
Account 
Nominal 
Ordinal 
Description 
Variables are differentiated based on their nomenclature. 
Order is naturally taking place among variables, but the difference is unidentified. 
Sequence 
No implied sequences among variables. For instance, the sequence of the Marital status may be (Single, Married, Divorced, Widowed). 
Implied sequence but cannot be calculated. For instance, the
order of size is Small, Medium, Large, ExtraLarge. But small, the medium is not equal to large, extralarge. 

Data can be categorised
and labelled (named). 
Data can be
classified and ordered. 
Grouping 
“= or not =” 
“= or not =” and
“< or >” 
Descriptive Statistics 
The central point
(Mode). 
The central point
(Median or Mode). 
Degree of values 