The level of measurement of variables is essential in statistical analysis because it determines how you can analyze your data. The four primary levels of measurement – nominal, ordinal, interval, and ratio provide different levels of detail – nominal provides minuscule detail, while interval and ratio give the maximum detail. 

If you're interested in learning the basics of nominal data, this guide is for you. We'll define what nominal data is, look at the characteristics of nominal data, examples of nominal data, how to analyze nominal data, and nominal vs. ordinal data.   

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What Is Nominal Data?  

Nominal data is qualitative data used to name or label variables without providing numeric values. It is the most straightforward type of measurement scale. Nominal variables are labeled into categories that do not overlap. Unlike other data types, nominal data cannot be ordered or measured; it does not have equal spacing between values or a true zero value. 

Nominal data is the foundation of statistical analysis and all other mathematical sciences. They comprise individual pieces of information recorded and used for analysis. 

For instance, the preferred mode of transportation is a nominal variable since we can sort the data into mutually exclusive categories like a car, bus, train, bicycle, etc. Numbers and words may denote nominal variables, but the number labels do not have any numeric value.

Characteristics of Nominal Data

The main characteristics of nominal data are:

  • Nominal data are categorical, the categories being mutually exclusive without any overlap. 
  • The categories of nominal data are purely descriptive, that is, they do not possess any quantitative or numeric value. Nominal data can never be quantified 
  • Nominal data cannot be put into any definite order or hierarchy. None of the categories can be greater than or worth more than one another. 
  • The mean of nominal data cannot be calculated even if the data is arranged in alphabetical order. 
  • The mode is the only measure of central tendency for nominal data. 
  • In most cases, nominal data is alphabetical. 

Nominal Data Analysis

Most nominal data is collected through open or closed-ended survey questions that provide the respondent with a list of labels to choose from. 

Close-ended questions are used if all data can be captured using a few possible labels. 

On the other hand, if the variable selected has many possible labels, an open-ended question is preferred.  

For example, 

What is your ethnicity? __ (followed by a drop-down list of ethnicities)

Nominal data can be organized and visualized into tables and charts. Thereafter, you can get descriptive statistics about your data set to calculate your data's frequency distribution and central tendency. 

The general steps to be taken to analyze nominal data include:

Descriptive Statistics 

In this step, descriptive statistics will enable you to see how your data are distributed. The most common descriptive statistics methods for nominal data are

  • Frequency Distribution – frequency distribution table is created to bring order to nominal data. Such a table clearly shows the number of responses for each category in the variable. Thus, you can use these tables to visualize data distribution through graphs and charts. 

Central Tendency 

 it is a measure of where most of the values lie. The most commonly used measures of central tendency are the mean, median, and mode. However, since nominal data is purely qualitative, only one mode can be calculated for nominal data. 

You can find the mode by identifying the most frequently appearing value in your frequency table. 

Statistical Tests

Inferential statistics allow you to test scientific hypotheses about the data and dig deeper into what the data are conveying. Non-parametric tests are used for nominal data because the data cannot be ordered in any meaningful way. 

Nonparametric tests used for nominal data are:

  • Chi-square goodness of fit test – this test helps to assess if the sample data collected is representative of the whole data populace. The test is used when data is collected from a single population through random sampling.
  • The Chi-square independence test explores the relationship between two nominal variables. Hypotheses testing allows testing whether two nominal variables from one sample are independent. 

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Examples of Nominal Data

Most nominal data is sorted into categories, where each response fits only into one category. 

Some examples of nominal data are:

1. Which state do you live in? (Followed by a drop-down list of names of states)

2.Which among the following do you usually choose for pizza toppings?

  • Onion
  • Tomatoes
  • Spinach
  • Pepperoni
  • Olives 
  • Sausage
  • Extra Cheese
  • Which is the most loved breed of dog?
  • Doberman - 1
  • Dalmatian - 2
  • Labrador – 3
  • German Shepherd – 4

3. Hair Color (black, brown, grey, blonde)

4. Preferred mode of Public Transport (bus, tram, train)

5. Employment Status (employed, unemployed, retired)

6. Literary Genre (comedy, tragedy, drama, epic, satire)

Nominal vs. Ordinal Data

Ordinal data is a kind of qualitative data that groups variables into ordered categories. The categories have a natural order or rank based on some hierarchal scale. 

The main differences between Nominal Data and Ordinal Data are:

  • While Nominal Data is classified without any intrinsic ordering or rank, Ordinal Data has some predetermined or natural order. 
  • Nominal data is qualitative or categorical data, while Ordinal data is considered “in-between” qualitative and quantitative data.
  • Nominal data do not provide any quantitative value, and you cannot perform numeric operations with them or compare them with one another. However, Ordinal data provide sequence, and it is possible to assign numbers to the data. No numeric operations can be performed. But ordinal data makes it possible to compare one item with another in terms of ranking.  
  • Example of Nominal Data – Eye color, Gender; Example of Ordinal data – Customer Feedback, Economic Status

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Frequently Asked Questions

1. What is nominal or ordinal data?

There are four main data types or levels of measurement – nominal, ordinal, interval, and ratio. Nominal Data is qualitative data used to name or label variables without providing numeric values. It is the most straightforward form of a level of measurement.

Ordinal data is also qualitative data that groups variables into ordered categories. The categories have a natural order or rank based on some hierarchal scale, like from high to low.

While Nominal Data is classified without any intrinsic ordering or rank, Ordinal Data has some predetermined or natural order. 

2. What are nominal data statistics?

In statistics, Nominal data is qualitative data that groups variables into categories that do not overlap. Nominal data is the simplest measure level and are considered the foundation of statistical analysis and all other mathematical sciences. They are individual pieces of information recorded and used for analysis. Nominal data cannot be ordered and cannot be measured. 

3. What are nominal and ordinal data examples?

1. Example of Nominal Data – Which state do you live in? (Followed by a drop-down list of names of states)

2. Example of Ordinal data – Rate education level according to:

  • Elementary
  • High School
  • College
  • Graduate
  • Post-graduate

4. What are the characteristics of nominal data?

The main characteristics of nominal data are:

  • Nominal data are categorical, the categories being mutually exclusive without any overlap. 
  • The categories of nominal data are purely descriptive, that is, they do not possess any quantitative or numeric value. Nominal data can never be quantified 
  • Nominal data cannot be put into any definite order or hierarchy. None of the categories can be greater than or worth more than one another. 
  • The mean of nominal data cannot be calculated even if the data is arranged in alphabetical order. 
  • The mode is the only measure of central tendency for nominal data. 
  • In most cases, nominal data is alphabetical. 

5. Which is an example of ordinal data?

An organization asks employees to rate how happy they are with their manager and peers according to the following scale:

  • Extremely Happy – 1
  • Happy – 2
  • Neural – 3
  • Unhappy – 4
  • Extremely Unhappy – 5 

6. What is an example of nominal data?

Example of nominal data:

  1. Which is the most loved breed of dog?
  • Doberman - 1
  • Dalmatian - 2
  • Labrador – 3
  • German Shepherd – 4

A real estate agent surveys to understand the answer to this question:

Which kind of houses are preferred by the residents of City X?

  • Apartments -A
  • Bungalows – B
  • Villas – C
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Conclusion

This article discussed the basics of nominal data, its definition, examples, variables, and analysis. If you want to learn about these topics in more depth, our Data Analyst Master’s Program Course is perfect for you. It’s also a great way to get certified by industry experts and take your career in data analytics or data science to the next level.

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