All you need to know about Nominal Data nominal data

All you need to know about Nominal Data

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What is nominal data

What is Nominal data?

Nominal data is known as “named” or “labeled” data that can be categorized into numerous groups that do not overlap. It is the type of data that cannot be measured or quantified, rather it can be only assigned to various groups that are unique and do not include any common elements. As the order of the gathered data can’t be established with the help of nominal data so, in case you change the order of data its significance will not be modified.

According to Latin nomenclature, the literal meaning of “Nomen” is – Name. While nominal data can be used to present a similarity that exists between various items but the details related to that similarity would not be disclosed. It is mainly used to streamline the data collection & analysis process and make it easy for the researchers. In certain cases, nominal data is referred to as “Categorical Data” too.

As binary data is known to represent “two-valued” data, nominal data is used to represent “multi-valued” data that cannot be quantitative. This data is discrete in nature. For instance, a dog can be a german shepherd or not. Today’s market research tools can help use nominal data in your research.

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What are the characteristics of Nominal Data?

Characteristics of nominal data

Let’s understand the characteristics of nominal data with the help of the following question:

What’s your ethnicity?

  • Asian
  • Black/African-American
  • Hispanic
  • White

Not quantifiable: As nominal data comes in the form of nomenclature, so it’s impossible to perform logical or statistical analysis of the data. For instance, a survey sent by a company to understand the ethnicity of its employees will include a question mentioned above. In this case, researchers couldn’t conclude anything by adding, deleting, or multiplying any variables from the collected data.

Lack of order: The nominal data doesn’t come in a definite order which makes it different from ordinal data. Even in the question shared above, the order of answer choices has nothing to do with the answers that your respondents provide.

Qualitative property: As answer choices are more likely to be qualitative, so the gathered data will always possess a qualitative property.  

No Mean Value: No matter how the data is arranged, it’s impossible to calculate the mean of nominal data. Consider the question example mentioned above, it is not possible for any researcher to compute the average of responses due to the qualitative nature of answer options (ethnicities).

Mode Conclusion: On surveying a large group of audience and asking them to submit their choices, the most common answer choice picked by most of them is known as mode. For example, in the above question, if most of the respondents submit Asian as their answer, then Asian will be the mode.

Data is alphabetical: The nominal data is mainly alphabetical and there’s no numerical data present. For instance, you can refer to the question listed above. This non-numerical data is further categorized into numerous groups.

How to analyze 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.  

What are the examples of Nominal Data?

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
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