Nominal Data: Definition, Characteristics and Examples

Nominal Data

Nominal Data Definition

Nominal data is “labeled” or “named” data which can be divided into various groups that do not overlap. Data is not measured or evaluated in this case, it is just assigned to multiple groups. These groups are unique and have no common elements. The order of the data collected can’t be established using nominal data and thus, if you change the order of data its significance of data will not be altered.

In Latin nomenclature “Nomen” means – Name. Nominal data does present a similarity between the various items but details regarding this similarity might not be disclosed. This is merely to make the data collection and analysis process easier for researchers. In some cases, nominal data is also called “Categorical Data”.

If binary data represents “two-valued” data, nominal data represents “multi-valued” data and it can’t be quantitative. Nominal data is considered to be discrete. For example, a dog can be a Labrador or not.

Learn about: Nominal Scale

Characteristics of Nominal Data

Let’s discuss characteristics of nominal data using this question:

What is your ethnicity? –

  • Central Asian
  • Indonesian
  • West Asian
  • Japanese
  • Nominal data can never be quantified: Nominal data will always be in form of a nomenclature, i.e., a survey sent to Asian countries may include a question such as the one mentioned in this case.  Here, statistical, logical or numerical analysis of data is not possible, i.e. a researcher can’t add, subtract or multiply the collected data or conclude the variable 1 is greater than variable 2.
  • Absence of order: Unlike ordinal data, nominal data It can also never be assigned a definite order. In the above example, the order of answer options is irrelevant to the answers provided by the respondent.
  • Qualitative property: Collected data will always have a qualitative property – answer options are highly likely to be qualitative in nature.
  • Can’t calculate Mean: The mean of nominal data can’t be established even if the data is arranged in alphabetical order. In the above-mentioned example, it is impossible for a researcher to calculate the mean of responses submitted for ethnicities because of the qualitative nature of options.
  • Conclude a Mode: Asking a large sample of individuals to submit their preferences – the most common answer will be the mode. In the provided example, if Japanese is the answer submitted by a larger section of a sample, it will be the mode.
  • Data is mostly alphabetical: In most cases, nominal data is alphabetical and not numerical – for example, in the mentioned case. Non-numerical data also can be categorized into various groups.

Learn more: Quantitative Data

Nominal Data Analysis

Most nominal data is collected via questions that provide the respondent with a list of items to choose from, for example:

Which state do you live in? ____ (followed by a drop-down list of states)

Which of the following items do you normally choose for your pizza toppings? (Select all that apply)
1) Spinach
2) Pepperoni
3) Olives
4) Sardines
5) Sausage
6) Extra cheese
7) Onions
8) Tomatoes
9) Other (please specify) _______________

There are three ways that nominal data can be collected. In the first example, the respondent is given space to write in their home state. This is a form of the open-ended question that will eventually be coded with each state being assigned a number. This information could also be provided to the respondent in the form of a list, where they would select one option.

The second example is in the form of multiple response questions where each category is coded 1 (if selected) and 0 if not selected. It also incorporates an open-end component allowing the respondent the option of writing in a category not included in the list. These ‘other please specify’ responses’ will need coding if they are to be analyzed.

Nominal data is analyzed using percentages and the ‘mode’, which represents the most common response(s). For a given question there can be more than one modal response, for example, if olives and sausage both were selected the same number of times.

Multiple response questions, e.g. the pizza topping example listed above, allow researchers the ability to create a metric variable which can be used for additional analysis. In this scenario, the respondent can select any or all options providing you with a variable that ranges from zero (none selected) to the maximum number of categories. This becomes a useful tool for consumer segmentation.

Nominal data is best used for profiling your respondents. Although limited in it statistical abilities this type of data is critical for gaining a deeper understanding of your survey respondents. Next, we will examine ordinal data.

Nominal Data Examples

In each of the below-mentioned examples, there are labels associated with each of the answer options only with the purpose of labeling. For instance, in the first question – each of the dog breeds is assigned numbers, while in the second question – both the genders are assigned corresponding initials, solely for convenience.

  • In the U.S. there is a huge section of people who love and own dogs. For a firm dealing with taking care of dogs while the owners are away, a question such as this can be useful to filter their target market: What is the most loved breed of dogs? –
    • Dalmatian – 1
    • Doberman – 2
    • Labrador – 3
    • German Shepherd – 4
  • For a travel agency looking to launch a travel plan purely for a sample of individuals, this is the most basic question: Who loves to travel more? –
    • Men – M
    • Women – W
  • A real-estate agent based out of New York will be highly inclined to understanding the answer to this question: Which type of houses are preferred by the residents of New York City? –
    • Apartments – A
    • Bungalows – B
    • Villas – C  

Learn about: Types of Variable Measurement Scales