Get topics and a plan for your dissertation. Find out more.   # A Comprehensive Guide on Nominal Data

Published by at August 31st, 2021 , Revised On November 19, 2021

Almost every industry today involves data in one way or the other. And if you are dealing with data in any capacity, you must be familiar with the four data types, also known as levels of measurement. These are Nominal, Ordinal, Interval, and Ratio.

Though this blog will only reflect on what nominal data is, where it is used, and how to analyze it, we will briefly introduce the other three for a quick recap.

## Introduction to Levels of Measurement

Whenever we say different types of data in statistics, know that we are actually referring to the levels of measurement. These various types of data show how precisely variables are recorded. The level of measurement can help you find how and to what extent the data can be evaluated.

### Nominal Data:

Nominal data defines categories and labels, for instance, brown eyes, red hair.

### Ordinal Data:

Ordinal data denotes data that can be ranked and categorized to form a hierarchy. An example would be low to higher grades.

### Interval Data:

This level of measurement can also be categorized and ranked. Note that there are evenly spaced and equal intervals between the categories—for instance, the Fahrenheit temperature.

### Ratio Data:

Ratio data, just like ordinal and interval data, can also be ranked and categorized. The intervals here are, likewise, equally spaced. The only thing that makes this one different from the rest is that ratio data has a true zero. For example, if you measure something in kgs, it is ratio data. Now, if something weighs zero, then it means that it does not weigh anything or most likely does not even exist. However, if the temperature in Fahrenheit or any other temperature scale is zero, that does not imply ‘no temperature.’

Before we get to the actual topic, which is Nominal Data, let us also glance at what different levels of measurement or types of data tell us.

## Significance of Levels of Measurement

The simplest answer to why these levels of measurement are significant in statistics and in our everyday lives is that they can help us analyze data. When we describe the characteristics of our dataset, we will use descriptive statistics, and when testing of various hypotheses is needed, inferential statistics would be required.

Now whether you should use inferential statistics or descriptive statistics will depend on the type of data you have at hand. So, before you get to the data collection process, make sure to be clear about which levels of measurement you might want to use.

## What is Nominal Data?

It is a type of qualitative data that divides variables into groups and categories. Keep in mind that these variables are purely descriptive, meaning they do not have any quantitative nature or value. You can also not put these variables in the form of meaningful order.

Now, depending on the experiment under process, you can define these variables in the form of words or numbers. Were you surprised to hear the numbers? Well, yes, you can use numbers.

To illustrate this with an example, suppose you are collecting information on people’s eye color. What you can do here is, use a numbering system to denote different eye colors. Say, blue is numbered 1, brown 2, black 3, and so on.

In this way, you can use both numbers and words to label different categories.

## Characteristics of Nominal Data

• Nominal data can be categorized and grouped into labels that are not numeric. They are purely descriptive!
• It is categorical, and the categories in nominal data are mutually exclusive. This means that there is no overlapping between the categories
• You cannot place nominal data in any hierarchy or order. One category is not more or less of value than the other

## What are A Few Examples of Nominal Data?

Following are a few examples of nominal data for your understanding:

Eye Color: Black, Brown, Blue, Green, etc.

Blood Group: A negative, B positive, O negative, O positive, etc.

Religion: Christianity, Buddhism, Islam, etc.

Political Affiliation: XYZ, YZE, UIO, OPT, etc.

You might have noticed that in all these examples, the characteristics are descriptive and cannot be denoted in the form of numbers unless you label them yourself.

## What data collection best suits your research?

• Find out by hiring an expert from Research Prospect today!
• Despite how challenging the subject may be, we are here to help you.      ## How do you Collect Nominal Data?

With the help of nominal data, you find out valuable important about a particular population or sample. Learning about your target demographic can benefit you in many ways, whether it is a finding you have been planning to do for years or a theory you believe can be proved right or rejected.

Nominal data can be collected through surveys, interviews, online questionnaires, and so on. You can either have close-ended questions for drawing certain conclusions, or have open-ended questions.

Examples of closed questions can be:

Are you over 18 years of age?

Possible answers: Black, Brown, and Ash Grey.

However, if there are lots of possible categories or groups, you can go with open-ended questions.

For instance:

What is your favorite movie genre?

What are some of the languages you can speak or understand?

It looks like we are done with collecting the data section, and now, it is our turn to analyze all the gathered data.

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## How Can You Analyze Nominal Data?

There are some general steps you must follow when assessing and evaluating data to form conclusions. These steps include collecting descriptive statistics, visualizing the data, and then carry out statistical analysis.

### Descriptive Statistics

In order to see how data can be distributed, descriptive statistics can be used. The most common descriptive statistics for nominal data are central tendency and frequency distribution.

Frequency distribution in research is a graph or chart that shows the frequency of occurrence of each possible outcome of an event or process observed. You can bring some order to your nominal data by creating a frequency distribution table. Simply use Microsoft Excel for creating a pivot table, and it will help you deduce results swiftly.

The measure of central tendency identifies the center point of a dataset, which is the most representative of the entire dataset. You must come across mode, mean, and median in school. These are what the measures of central tendency include.

Here, the mode is the most frequent value, the median is the middle value, and the mean is the average. The names pretty much suggest what they do. The mode is the only measure of central tendency you can use in nominal data.

### Visualizing Nominal Data

Presenting your gathered data in a visual format is called visualizing data. You can either use charts or graphs to see what your data is telling, just like we discussed in frequency distribution tables. Again, using Microsoft Excel for this would be convenient and quick.

### Statistical Tests of Nominal Data

So, what comes after summarizing and visualizing data?

Yes, that’s right! Next up is testing the hypotheses so that you can actually dig deeper and see what the data is suggesting.

Two types of statistical tests you can use for testing your hypotheses are:

• Parameter Tests-used for ratio data and interval
• Non-parametric Tests-used for ordinal and nominal data

And that is a wrap for nominal data. If you have any queries or requests, please leave a comment in the comment section below.

Levels of measurements or types of data show how precisely variables are recorded. The level of measurement can help you find how and to what extent the data can be evaluated. There are 4 levels of measurements, namely nominal, ordinal, interval, and ratio.

It is a type of qualitative data that divides variables into groups and categories.

Nominal data can be collected through surveys, interviews, online questionnaires, and so on. You can either have close-ended questions for drawing certain conclusions or have open-ended questions.

Though both levels of measurements are considered categorical, there is a slight difference between the two. There is no order or hierarchy in nominal data, while ordinal data can be grouped into categories following a meaningful order.

There are some general steps you must follow when assessing and evaluating data to form conclusions. These steps include collecting descriptive statistics, visualizing the data, and then carry out statistical analysis.

Descriptive Statistics

In order to see how data can be distributed, descriptive statistics can be used. The most common descriptive statistics for nominal data are central tendency and frequency distribution.

Visualizing Nominal Data

Presenting your data in a visual set-up is called visualizing data. You can either use charts or graphs to see what your data is telling, just like we discussed in frequency distribution tables. Again, using Microsoft Excel for this would be convenient and quick.

Statistical Tests of Nominal Data

Two types of statistical tests you can use for testing your hypotheses are:

• Parameter Tests-used for ratio data and interval
• Non-parametric Tests-used for ordinal and nominal data

Ratio data, just like ordinal and interval data, can also be ranked and categorized. The intervals here are also equally spaced. The only thing that makes this one different from the rest is that ratio data has a true zero. For example, if you measure something in kgs, it is ratio data. Now, if something weighs zero, then it means that it does not weigh anything or most likely does not even exist. However, if the temperature in Fahrenheit or any other scale is zero, then that does not imply ‘no temperature.’ Ingram is a dissertation specialist. He has a master's degree in data sciences. His research work aims to compare the various types of research methods used among academicians and researchers.