Nominal Data

It is qualitative data used to label variables into distinct, mutually exclusive categories without any intrinsic order or numerical value. Nominal data is often analyzed using frequencies, percentages, or mode. The categories used to label nominal data do not overlap and cannot be ordered or measured. 

When we represent it in a graph, the x-axis represents the categories and the y-axis is the frequency count.

Some of the few characteristics of nominal data are:

• Nominal data are categorical, where the categories are mutually exclusive without any overlapping of the categories.

• Nominal data does not contain any kind of numerical data and are purely descriptive.

• No category can be greater than the other which implies that nominal data cannot be ordered or ranked.

• Even if nominal data is arranged alphabetically, the mean cannot be calculated because it lacks numerical value and mathematical meaning. Mean can only be calculated with numerical data.

• Mode is the only measure of central tendency for nominal data.

Nominal data is a type of categorical data along with ordinal data. Many get confused between nominal and ordinal data. So here are some of the differences between nominal and ordinal data:

It is typically collected through open or close-ended surveys, questionnaires, or interviews. Nominal data can be organized into tables and charts. Once the data is collected we will need to analyze this data, some of the ways to analyze nominal data are:

Descriptive Statistics:

We use descriptive statistics to see how the data is distributed among the categories. One of the most common methods of descriptive statistics is frequency distribution. Frequency distribution is used to bring order and shows the number of responses or the count for the categories in the variable. 

Central tendency:

One of the most common statistical measures to analyze data. It is a measure of where the values lie in the dataset. The most commonly used measures of central tendency are mean, median, and mode. Mode is the most frequently appearing value in the dataset. Since nominal data is strictly qualitative data, the only measure of central tendency we can use is mode. 

Statistical Tests:

To analyze data at a deeper level and test hypotheses, we use statistical tests such as the chi-square test. 
 

Nominal data consists of categories without any order. To represent this kind of data in ways to make it easier to understand, here are some of the ways we use:

• Frequency tables: Frequency tables list categories with corresponding counts, and no order is needed to represent the data. While the data is not graphically represented, it provides a precise summary of the data.

• Bar charts: Bar charts are one of the most straightforward ways to represent data visually. Each category is represented using bars. This method allows for easy comparison between different categories.

• Pie charts: Here, data is represented in a circular format, where each sector or slice represents a category’s proportion relative to the whole dataset. 
   

Nominal data is widely used to conduct research using surveys or questionnaires. Here are some real-world applications that use nominal data:

Market research:

Most companies use surveys or questionnaires to categorize customers based on their gender, age, or location. This helps in developing new marketing strategies for new products.

Education:

To help identify students who need additional support in certain fields or subjects, educational institutions use nominal data. 

Environmental sciences:

Researchers use nominal data to gather any information about pollution or behaviors by taking surveys or questionnaires and then organizing them into categories.