In practical areas such as in statistics it is necessary to have multiple data types for selecting the one data with the help of the right method. One of the common categorical data types included in types of data is nominal data which plays a key role in making categorical data without any order. This is applicable in every aspect of life whether you are making surveys or summarising and analysing the trends, nominal data assist in making groups based on labels and name. In this blog post you will get to know the answer for what is nominal data in statistics?, what types of nominal data are there, along with this post we will grasp in detail about how to analyse nominal data with the help of examples of nominal data.
Let’s begin!
What Does Nominal Data Mean?
Nominal data is nothing but the categorical data on the basis of labels and names that are not ranked or ordered, in short without any numerical value format. The word Nominal is the Latin word which states the word Nomen, for name. Some examples of nominal data include blood type survey, gender, language and nationality. Nominal vs ordinal data has slight differences such as nominal data is without numerical and hierarchical order but the ordinal data is presented by some value in numbers for the data category.
In simple terms the Nominal data definition and examples: It is one of the types of categorical data that consists of different names and labels without any quantitative order and commonly used in demographic research, surveys and day to day tasks.
Key Features of Nominal Data
It is necessary for you to look over the characteristics of nominal data that will help you in knowing nominal data appropriately.
1. No order no and categorical in nature: It categorizes the data in some qualitative form such as male female, national, yes or no, and has no logical sequence.
2. Mutually exclusive and labels only: Value given to data is identifiers of the data not the no of quantity. Also each data is the part of one category only.
3. Represented by numbers and Non-Arithmetic: In nominal data there is no performance of mathematical operations and numerical codes are given to data.
4.These some common characteristics of nominal data differentiate them from other statistics data types like ordinal or interval data.
Common Examples of Nominal Data
To perceive better about nominal data let’s look over some practical examples of nominal data:
| Variable type | Group or categories |
| Nationality | British, Indian, Australian etc.... |
| Eye colour | Green, brown, blue, grey, etc..... |
| Blood type | A, B, AB, etc... |
| Yes/no response | NO, Yes... |
These examples of nominal data show how nominal statistics data types are used in the group data formation, market research, and some trends analysing.
Nominal Data vs. Ordinal, Interval & Ratio Data
Difference between nominal and ordinal data is elaborated following:
| Characteristics | Nominal Data | Ordinal Data |
| Calculations | Not required | Limited like arithmetic operation. |
| Order | No | Yes |
| Examples | Religion, languages, name, gender etc.. | Motivation level, marks, ranks, etc... |
Statistical Data Type:
| Type | For examples | Order sequence | Interval equality | True zero |
| Interval | Temperature | Yes | Yes | No |
| Ratio | Age, weight, height... | Yes | Yes | Yes |
| Nominal | Nationality, language, blood type etc... | No | No | No |
| Ordinal | Survey Ratings | Yes | No | No |
Make sure to know all the difference between nominal and ordinal data make sure for data analysing and interpretation.
How to Collect Nominal Data
Selecting and collecting nominal data includes the process of asking participants to select the one particular category. This thing is common in observational research study, surveys, and questionnaires.
1. Interviews and Surveys: Includes multiple option marking, record responses in verbal category after that code is given to groups.
For example, what is your nationality? () Indian, () Australian, () British.
2. Observation and Administration database process: Classify different forms of traits, behaviours on the basis of observational traits such as brand of product, colour choice, types of fabric. Also the data is collected on the basis of pre record data such as patient medical records, passport nationality and document verification data.
Methods to Analyze Nominal Data
Nominal scale is non-numeric and there is no particular sequence or order no is given to data. It helps in analysing the nominal data based on basic statistical tools. The following is the methods for how to analyse nominal data:
1. Chi-Square Test of independence: Statistical test is included to determine if two nominal scales are related or not. Nominal data plays a crucial role in descriptive statistics and categorical data differentiation.
2. Mode and frequency distribution: In this method observations are counted to make them fall into each category. In mode identify the most common category.
3. Contingency Tables: Try exploring relationships between two or more nominal scales.
4. Pie charts or Bar charts: Try to make the numerical value useful for visualising nominal data in types of nominal data.
When to Use Nominal Data in Research
Researchers select nominal data when their objective is to categorise and label views. It’s widely used in the following fields:
- Market Research: Making groups of customers according to brand choices and preferences.
- Education: Make groups according to learning style, region and school type.
- Health studies: Make groups according to blood type, patient status, and disease type.
- Psychology and sociology: Groups are formed on the basis of gender, religion and ethnicity.
- Fields where the classification of students is required, nominal scale is appropriate to use.
Limitations of Nominal Data
Nominal data includes some of the drawbacks discussed below:
1.No order and ranking: In this method it is difficult to assess trends and progression.
2.It has limited statistical analyses: In this method it is difficult to use advanced techniques and methods like correlation and regression.
3. No quantitative Value: In this method you cannot subtract, add, and only make average nominal categories.
4.Limited statistical analysis: Advanced techniques are not applicable in this method such as correlation and regression analysis.
Conclusion
At last nominal data is one of the crucial parts of categorical data types in statistics. In this blog it was summarised nominal scale classify data on the basis of names and labels. Despite some of the drawbacks, it provides a foundation for meaningful insights in data. It is useful for everyone whether designing a survey, conducting demographic research or organising a database. Understanding what is nominal data in statistics and how to analyse nominal data is a crucial stage for grouping of the data. By recognising its features, exploring examples of nominal data and grasping into how to use it in research, it is easy to handle.
Frequently Asked Questions
Q1. How is nominal data coded in datasets?
Nominal data is coded with the use of the numerical labels and name for ease of entry and analysis. There is no mathematical value but codes that are given to categories. For instance “Male”1 and “Female”2.
Q2. Can we use mean or median for nominal data?
No, nominal data cannot use the mean and median due to lack of the ordered structure and there is no arithmetic calculation made on such data. The only suitable central tendency is the mode which helps in knowing the most frequently occurring category.
Q3. How do researchers interpret nominal data results?
Researchers interpret the nominal data by looking towards proportions, frequencies, and relationship between categories mentioned. Some of the tools of statistics help in summarising and categorising groups such as pie charts, bar charts, and chi square charts.
Q4. In what fields is nominal data most commonly used?
Nominal data is most commonly used in fields like psychology, marketing, social sciences, education, and healthcare. It helps classify data into distinct categories such as gender, ethnicity, brand preference, or medical diagnoses. These categories have no inherent order or ranking.
Q5. What are the limitations of nominal data?
Nomination data cannot be ranked and ordered or used in calculation. It lacks numerical figures and cannot include any type of arithmetical calculations. Sometimes misinterpretation of coded values given to categories can lead to inaccurate and wrong outcomes.