All you need to know about Nominal Data

The nominal data is gathered by using questions that offer respondents a list of answer options to choose from. Market research tools can help analyze gathered nominal data. For instance:

Which country do you belong to? ____ (this will be followed by a drop-down of countries)

Which of the following sauces do you prefer for your submarine sandwich? (Select all that apply)

1) Mayonnaise

2) Pesto Mayo

3) Honey Mustard

4) Smoky BBQ

5) Chipotle Southwest

6) Sweet Onion

7) Other (please specify) _______________

The nominal data can be collected in three ways. In the first example shared above, the respondents are asked to pick their country from the dropdown list shared. In this type of question, the answer options are coded as every country has been assigned a different number.  

In the second example, there are multiple responses included in a single question. In this case, every selected category will be coded as 1 and the unselected one will be coded as 0. It also features an open-ended option at the end that enables respondents to write the missing category. To analyze this ‘other please specify’ category, there will be a separate coding needed.  

The nominal data can be analyzed with the help of percentages as well as the ‘mode’, which indicates the highly common answer choice(s). However, there can be more than one mode too, for example, if mayonnaise and honey mustard both were chosen the same number of times.

By using multiple response questions (like the subway sandwich one discussed above), researchers can create a metric variable that can be easily used for performing additional analysis. In this case, a variable ranging from zero (for no selection) to the maximum number of categories can be used depending on the selection that respondents make. It plays a key role in consumer segmentation.

Nominal data is considered to be ideal for profiling your respondents. Despite its limited statistical abilities, this data plays a crucial role in market research and helps to gain a better and insightful understanding of your respondents.  

In the examples of nominal data mentioned below, there are some values associated with every answer option, with the aim of labeling. For example, in the first question – both of the genders are allotted their initials, whereas in the second question – all of the perfume brands are assigned some numbers, merely for convenience.

• For a retail brand planning to launch a new range of pop-colored T-shirts for a sample of individuals, this would be the most basic question: Who loves to wear pop-colored T-shirts?

• Men – M
• Women – W

• In France, there are a lot of people who love perfumes and own a variety of brands. For a firm that sells various perfume brands at their store, a question like this can be useful to understand their target market better:  Which perfume brand would you prefer to buy?

• Calvin Klein – 1
• Yves Saint Laurent – 2
• Dolce & Gabbana – 3
• Versace – 4
• Gucci – 5