Nominal Variables

A nominal variable classifies data into separate groups that do not have a specific ranking or order. It is used with nominal data as it is non-numerical. For instance, let us consider the variable hair color. Black, white, and brown are the possible categories, and there is no inherent order to these categories. Nominal variables can be used for codes but not for arithmetic operations. Some characteristics of a nominal variable are listed below:
 

• Nominal variables categorize data into unique categories that lack any order or ranking. 
   
• They represent names, labels, or categories rather than any numerical values. 
   
• Each observation falls into just one category. No categories overlap with one another. 
   
• Mathematical operations such as addition, subtraction, multiplication, or division are not applicable to nominal data. 
   
• Nominal variables are qualitative. They describe the characteristics or qualities and are not quantified. 
   
• Nominal variables are commonly used to categorize data for surveys, databases, and research. 

Categorical variables are classified into nominal, ordinal, and dichotomous variables. Nominal variables classify data without any numerical value or ranking, while ordinal variables are used to categorize data with a specific order or ranking. The main differences between the nominal variable and the ordinal variable are listed below:
 

• A nominal variable is qualitative and lacks numerical properties, whereas an ordinal variable is quantitative and follows a specific numerical order.  
   
• The data of an ordinal variable can be ordered or ranked. For example, data from research or a survey can be considered ordinal data. 
   
• The data of a nominal variable can be categorized into distinct categories without any inherent ordering or numerical value. 
   
• Nominal variables are used to classify and label variables, such as name, color, zip code, and so on. 
   
• An ordinal variable contains variables like educational and economic status, and customer satisfaction level. 
   
• Mode is the only measure of central tendency used with nominal variables. Ordinal variables can also be analyzed using the median, mode, quantiles, and percentiles.  
   

Nominal variables categorize data without any specific order or hierarchy. Therefore, nominal variables are analyzed by different techniques such as comparing the frequencies, grouping, and counting. The methods of nominal variable analysis vary depending on the size of the dataset, research problem, and data quality. The most common techniques to analyze nominal variables are:

• Frequency distribution
  
  This method includes finding the different categories and counting how many times each category occurs. To understand the data patterns and trends, we can use this frequency count. For a better understanding, have a look at this example. We took a survey of 50 people about their favorite ice cream, and we found the following results:
  
  20 people: Vanilla 
  12 people: Chocolate
  18 people: Strawberry 
  
  Through the frequency distribution, we can see which category is the most or least common. 

• Central tendency
  
  This technique involves finding the most frequently occurring category in a dataset. Central tendency identifies the mode, which represents the most popular category. Also, it is used to express how different and similar the distributions of categories are in the sample. For instance, if 60 people choose “orange”, 30 choose “apple”, and 10 choose “watermelon”, then orange is the mode. Because it appears the most in the given dataset. 

• Chi-Square Test
  
  To check the relationship between two nominal variables, we use the chi-square test. This statistical test is used to determine the significant connection between two categorical variables. The actual number of times each category appears is calculated and then compared with the expected number of times each category appears. It helps to determine whether they are related or randomly distributed. For example, we can find answers to questions like, does a person’s age affect their favorite flavor of ice cream?

• Contingency table analysis
  
  This is a cross-tabulation technique that involves comparing two variables to see their relationships. This is a method of constructing rows and columns with variables. For each combination of categories, there is a frequency count of occurrences that highlights the relationship between the two categories. For instance, if we analyze favorite movies vs. gender, the table might show that more men prefer action movies while more women prefer feel-good movies. 

Nominal variables play a crucial role in different fields, such as marketing, healthcare, and social sciences. Understanding how to represent nominal variables is important for categorizing data for a clear interpretation. Some common methods for representing nominal data are:

Frequency tables

Frequency tables give a list of every category along with its frequency (count). Frequency tables provide a clear and accurate representation of nominal data. For example, if we survey 60 people about their favorite ice cream flavor, we can create a frequency table.

This table will help us understand and compare the responses for each category. 
 

Bar chart 

We use a bar chart to express the category frequencies. Bar charts are one of the simplest forms to represent nominal data. It is used to symbolize each category, and the bar’s height and length reflect the count or frequency of each category. For example, if we ask 200 people about their favorite fruits, then they respond with Apple, Banana, Orange, Strawberry, and Grape. So the bar chart is as follows: 

Pie charts

Pie charts explain the category proportions as slices of a circle. It represents the data in a circular format. To represent the parts of a whole dataset, we can use pie charts. For instance, if we survey 60 people about their favorite ice cream flavor and the data is as follows:  

So, the pie chary of the data is:

Nominal variables are categorical variables without any order or numerical value. The process of collecting nominal data involves gathering qualitative variables for a dataset, and it involves several methods. They are:

 
Surveys and Questionnaires: 

Create surveys that include multiple-choice questions, so that the respondents can choose their answer from the predefined categories.

For example, which mobile phone brand do you prefer? 

Samsung
Apple 
Google Pixel 
OnePlus

Interviews: 

Ask structured or semi-structured questions to get categorical responses. For instance, asking interviewees about their educational status or marital status. Also, we can ask open-ended and closed-ended questions to the respondents. 

Observation: 

Observe and record data about targeted respondents and categorize them for future purposes. For instance, observing youngsters and their clothing preferences and trends. 

Administrative records:

Using the existing records or documents to collect the nominal data. For example, school records with the student’s name and education level can be utilized for obtaining categorized information.

Online forms:

Design online forms with radio buttons to collect categorical responses. For instance, a Google form asking for education level, with options, high school, higher secondary, undergraduate, and graduate. 

Nominal variables help us to categorize data into categories without any ranking. The real-life applications of nominal variables are countless. They are:
 

• To learn about the trends and patterns in a market industry, businesses use nominal variables like favorite brands, color, nationality, and gender to divide their customers. 
   
• A medical professional uses different nominal variables such as the patient’s blood type, disease type, or medication type. It will help them to segment their patients and decide on better treatment plans. 
   
• To analyze player performance, nominal variables such as team name, jersey color, or position are used for classification purposes. 
   
• In the fields of political science, e-commerce, social media marketing, and even law enforcement professionals use different nominal variables to categorize their data.