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An Introduction to Nominal Variables: Understanding Types of Data

The article provides a comprehensive overview of the characteristics, types and methods to analyze nominal variables.
Jun 2024  · 13 min read

Different data types require specific collection and analysis methods, and understanding data characteristics is essential for exploring distributions, trends, and relationships. Data is categorized into four types: nominal, ordinal, interval, and ratio variables.

This article introduces nominal variables, covering the definition of nominal variables, levels of data measurement, types of nominal variables, methods for analyzing nominal variables, and examples of nominal variables in statistical analysis.

What Are Nominal Variables?

Nominal variable is a type of categorical data that does not possess any quantitative value nor inherent ordering or hierarchy. The categories of nominal variables are mutually exclusive and can be identified as unique labels. This type of data is mainly used in statistical analysis with the objective of providing grouping and classification.

Put simply, a nominal variable is a type of data used to label or categorize things without assigning any numerical value or order. For example, if you're looking at a list of different fruits (like apples, oranges, and bananas), each fruit is a category, and there's no ranking or value assigned to them.

Nominal data is collected through surveys, questionnaires, observations, or existing forms and records. The questions are usually multiple-choice, yes/no, closed-ended, or open-ended.

Examples of Nominal Variables

Below, we’ve included some examples of how nominal variables are collected:

Multiple choice question

Which car brand do you prefer?

a) Toyota

b) BMW

c) Ford

d) Tesla

e)Honda

Yes/No questions

Do you possess a driving license?

Close-ended questions

Would you recommend your current car brand to others?

a) Extremely likely

b) Likely

c) Neutral

d) Unlikely

e) Extremely unlikely

Open-ended questions

What are the best features of your car?

As seen above the answers to the various types of questions will be in the form of words or labels. Analyzing this data can be challenging while collecting responses from a large sample of individuals. However, its applications extend across diverse domains, enabling researchers and stakeholders to make targeted decisions.

Levels of Measurement of Variables

Data analysis can include two types of approaches:

Quantitative data analysis

Quantitative data analysis involves the examination of data that is numeric and tangible in nature. This type of data can be analyzed using straightforward mathematical methods and visualizations. For example, obtaining temperature readings for a week falls under quantitative data analysis.

Qualitative data analysis

Qualitative data analysis focuses on data expressed as labels and descriptions of characteristics. In this approach, patterns and relationships between data variables are analyzed to gain meaningful insights. For instance, analyzing customer purchase behavior over a month is an example of qualitative data analysis.

Nominal and ordinal are classified as qualitative data while interval and ratio are classified as quantitative data. Nominal provides the lowest level of detail while interval and ratio provide the highest level of detail.

Other Types of Variables

Let us briefly look through the characteristics of the other types of data.

Ordinal variables

These are descriptive qualitative data that includes some ordering amongst labels. The main difference between nominal and ordinal data is the presence of hierarchy, which makes ordinal data easier to interpret.

Examples:

  • Income level can be low, moderate, and high with the understanding that low<moderate<high.
  • Customer feedback can be excellent, good, satisfactory, or poor, with an incremental ordering of poor=1 to excellent=4.

Interval variables

Interval data is quantifiable with equal intervals between data points.

An important characteristic is the absence of a true zero point, which implies that zero is treated as a valid reference point.

Examples:

  • Measurement of temperature recorded as 0C is an actual temperature, which can be midway on a scale as temperatures can lower into minus values.
  • The difference between any two academic test scores is meaningful, but the value zero does not imply a lack of academic ability.

Ratio variables

Ratio data is similar to interval data in terms of equal distance between values. However, it differs because of the fact that zero value is considered to be absolute below which no meaningful measurements can be obtained. Due to the absence of negative values, ratio data is most suitable for mathematical operations(addition, subtraction, division and multiplication) and precise statistical analysis.

Examples:

  • The age of an individual, which cannot be zero.
  • Income is measured as a ratio value and zero income represents the absence of earnings. Ratios between the income of two individuals can also be meaningful (income of one is twice that of the other)

Below is a table that summarizes the four data variable types:

Nominal

Ordinal

Interval

Ratio

Classified

🗸

🗸

🗸

🗸

Ordering

🗸

🗸

🗸

Uniform intervals

🗸

🗸

True zero value

🗸

Different Types of Nominal Variables

Nominal variables are further classified into the following types:

Binary variables

Binary variables typically have only two possible categories, implying that the outcome or response can be only one type.

Example

Response

Do you possess a driving license?

Yes/no

Outcome of a medical investigation of a disease

Positive/negative

Multiple category variables

These variables can have more than two categories. There exists no fixed ordering amongst categories and each type has equal probability of occurrence.

Example

Response

Select your ethnicity

British, Asian, African, American

Specify your marital status

Married, single, divorced, widowed

Ordered nominal variables

Represent a type of nominal variable with categories that have a ranking order. However, the difference between categories may not be uniform or measured accurately.

Example

Response

Would you recommend our product to others?

Extremely likely, likely, neither likely nor unlikely, unlikely, extremely unlikely

(Extremely likely could have the highest score while unlikely would have the lowest)

What is your highest level of qualification?

Less than high school, high school, bachelor’s degree, master’s degree, doctoral degree

(Here, less than high school could have the lowest rank while a doctoral degree would have the highest rank)

Unordered nominal variables

These variables represent categories without any inherent order or hierarchy. Each type has an equal weightage and there is no specific sequencing that exists.

Example

Response

Select your preferred mode of payment

Cash, credit card, debit card, online bank transfer, PayPal

How did you learn about this job opportunity?

LinkedIn, Indeed, Company website, recruitment agency, others

These examples give a clear understanding of the type of nominal variables.

A detailed analysis of categorical data can be done using various library functions available in Python.

Ways to Analyze Nominal Variables

The type of data investigation techniques employed depend on the research problem, data quality, size of the dataset and various other factors.

Some statistical methods of analyzing nominal variables are listed below:

Frequency distribution

Frequency distribution involves identifying various categories and calculating the number of occurrences under each category. This frequency count can be used to understand data trends and patterns.

Central tendency

Central tendency calculates the mode, which identifies the highest-occurring category in the dataset. This value can highlight the most preferred choice or can be used to reveal differences or similarities across distribution of categories.

Chi-Square test

Chi-square tests are statistical tests that determine the association between two categorical variables. The observed frequency of categories is calculated and compared with the expected frequency of the categories obtained under the assumption of independence.

Contingency table analysis

This is a cross-tabulation method of constructing a table with variables representing rows and columns. For each combination of categories, a frequency count of the occurrence is obtained which highlights the relationship between the two categories. You can learn more in our course, Contingency Analysis using R.

Visualization charts

Bar charts and pie charts are highly effective in communicating nominal data distribution in a visually appealing manner. Check out our data visualization cheat sheet to discover more.

These methods can be implemented by learning detailed approaches to statistics for data analysis.

Tools for Analyzing Nominal Variables

When analyzing nominal variables, several powerful Python tools and libraries can assist in data manipulation, visualization, and statistical analysis:

  • Pandas: Ideal for handling and manipulating datasets. Use groupby() and value_counts() to summarize and analyze categorical data.
  • NumPy: Provides fundamental array operations and mathematical functions to support data analysis.
  • Matplotlib: Useful for creating bar charts and pie charts to visualize the distribution of nominal variables.
  • Seaborn: Enhances data visualization with high-level interfaces, making it easy to create informative count plots and categorical plots.
  • SciPy: Offers statistical functions like chi2_contingency() to perform chi-square tests and assess relationships between categorical variables.
  • Statsmodels: Facilitates detailed statistical modeling and hypothesis testing, useful for analyzing relationships in categorical data.
  • Scikit-learn: Contains tools for preprocessing data, such as LabelEncoder(), and for conducting machine learning analyses on categorical data.

Examples of Nominal Variables Used in Statistical Analysis

Nominal data is widely used across research and business to uncover relationships and useful patterns from the colossal amount of data generated rapidly.

Some useful examples of nominal variables used in statistics is discussed below:

Demographic surveys

Nominal data collected through survey forms is highly useful in understanding the population composition. By grouping individuals based on these defined categories different needs and preferences can be identified that can aid in effective marketing strategies for launching of new products.

Example

Options

Age bracket

under 18, 18-24, 25-34, 35-44, 45-54, 55-64, 65 & above

Preferred mode of receiving marketing information

email, phone call, sms, promotional ads

Gender

male, female, nonbinary, prefer not to say

Income levels

under £35000, £35,000- £54,999,£55,000- £74,999

above £75,000

Relevant Data Analysis Technique: Chi-Square Test

The Chi-Square test can be used to determine if there is a significant association between two categorical variables.

Understanding customer feedback

Nominal variables can aid businesses in identifying key issues related to customer satisfaction and bring about improvements in services provided.

Based on the different categories of data effective communication can be established through tailored content shared specific to customer groups.

This qualitative customer survey is an effective tool to monitor changing trends, patterns and preferences towards products and services thereby improving customer relationships.

Example

Options

Rating the satisfaction of using the product

excellent, very good, good, average, poor

Usability

very easy, somewhat easy, neutral, somewhat difficult, very difficult

Recommend the product to a friend

very likely, likely, neutral, unlikely, very unlikely

Relevant Data Analysis Technique: Sentiment Analysis

Sentiment analysis helps in categorizing textual feedback into various sentiments like positive, negative, or neutral.

Evaluation of a business

Performance metric can be categorized based on product category, region, time periods to provide a structured approach to analyzing the business performance against competitors or industry benchmarks. Resource allocation based on nominal data helps businesses effectively invest in areas of high returns or draws attention to underperforming sectors.

Example

Options

Rating profit margins

very low, low, average, high, very high

Preferences for resource allocation

sales, marketing, research, operations, customer service, HR

Select revenue growth

exceeded expectations, met expectations, below expectations

Relevant Data Analysis Technique: ANOVA (Analysis of Variance)

ANOVA can be used to compare the means of three or more groups based on nominal variables.

Human resource management

Data can be analyzed to predict future workforce needs based on business growth and identify the most effective recruitment models.

Employee performance can be assessed to reward top performers as well as provide additional training to underperformers.

Talent analytics is also heavily dependent on data to identify critical roles that need to be filled in.

Example

Options

Types of employee benefits

health insurance, retirement plans, bonuses

How inclusive do you perceive the work environment to be?

very inclusive, partly inclusive, not very inclusive, not inclusive at all

Relevant Data Analysis Technique: Logistic Regression

Logistic regression can be used to model the relationship between a binary dependent variable and one or more nominal independent variables.

Medical research

Nominal variables are used in medical research to help identify factors related to occurrence of a disease, analyze patient information and study the overall healthcare system with a goal to improve existing practices or provide new treatment facilities.

Data from healthcare systems can be categorized on the basis of patient details, disease information, diagnostic methods, treatments and outcomes.

Example

Options

Categorize patients based on healthcare insurance

employer-sponsored insurance, individual health plan, medicare, medi-aid, others

Disease classification based on symptoms

fever, cold, runny nose, headache, fatigue, diarrhea

Assessing if healthcare providers have provided adequate care to patients

always, sometimes, rarely, never

Relevant Data Analysis Technique: Crosstab Analysis

Crosstab analysis is used to examine relationships within data that are categorical.

Get Started With Data Analysis

Nominal variables are highly significant in almost every type of data driven application related to business operations, marketing, medical research and many others.

This article gives an overall understanding of nominal variables, their characteristics, types, and examples of usage in different areas of implementation. Each type offers different insights which determine the appropriate statistical methods to be employed.

Next, it would be ideal to learn more about statistics and its uses in the real world through case studies and projects provided by the Introduction to Statistics course. The course can equip you with the skills needed to analyze large datasets and draw useful conclusions.

FAQs

How are nominal variables different from other data types?

A nominal variable is a type of categorical data that does not possess any quantitative value nor inherent ordering or hierarchy. The categories of nominal variables are mutually exclusive and can be identified as unique labels.

What are the different methods of collecting nominal data?

Nominal data is collected by means of surveys ,questionnaires ,observations or existent forms and records. The questions are usually multiple choice, yes/no, closed ended or open ended .

How can nominal variables be analyzed?

Frequency distribution, central tendency, contingency tables, chi square test and visualization charts are used to analyze nominal variables.


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Author
Shona Afonso
Shona Afonso has experience in the field of machine learning, genetic algorithms, data visualization techniques and data modelling algorithms. With a foundation in machine learning, she has implemented various prediction models and algorithms to obtain insights from complex datasets. She has worked on various data visualization techniques to provide useful insights about data patterns in decision making process for conveying information in a concise and understandable manner. With a good academic background, she has research publications to her credit in the domain of machine learning and data science. She also has extensive teaching experience of 12+years and is adept at collaborating with interdisciplinary teams and delivering results in a timely manner. She is a strong communicator and enjoys sharing knowledge through presentations, technical reports, and workshops.
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