Nominal data is labelled into mutually exclusive categories within a variable. These categories cannot be ordered in a meaningful way.

For example, preferred mode of transportation is a nominal variable, because the data is sorted into categories: car, bus, train, tram, bicycle, etc.

Levels of measurement

The level of measurement indicates how precisely data is recorded. There are 4 hierarchical levels: nominal, ordinal, interval, and ratio. The higher the level, the more complex the measurement. Nominal data is the least precise and complex level. The word nominal means “in name,” so this kind of data can only be labelled. It does not have a rank order, equal spacing between values, or a true zero value.

Examples of nominal data

At a nominal level, each response or observation fits only into one category.

Nominal data can be expressed in words or in numbers. But even if there are numerical labels for your data, you can’t order the labels in a meaningful way or perform arithmetic operations with them.

In social scientific research, nominal variables often include gender, ethnicity, political preferences or student identity number.

Examples of nominal variables

Variable types that can be coded in only 2 ways (e.g. yes/no or employed/unemployed) are called binary or dichotomous. Since the order of the labels within those variables doesn’t matter, they are types of nominal variable.

How to collect nominal data

Nominal data can be collected through open- or closed-ended survey questions.

If the variable you are interested in has only a few possible labels that capture all of the data, use closed-ended questions.

Examples of closed-ended questions

If your variable of interest has many possible labels, or labels that you cannot generate a complete list for, use open-ended questions.

Examples of open-ended questions

1. What is your student ID number?

2. What is your zip code?

3. What is your native language?

How to analyze nominal data

To analyze nominal data, you can organize and visualize your data in tables and charts.

Then, you can gather some descriptive statistics about your data set. These help you assess the frequency distribution and find the central tendency of your data. But not all measures of central tendency or variability are applicable to nominal data.

Example: Nominal data set

You distribute a survey with a question asking respondents to select their political preferences from a list. Your data set is a list of response values.

Data set

Distribution

To organize this data set, you can create a frequency distribution table to show you the number of responses for each category of political preference.

• Simple frequency distribution Percentage frequency distribution

To create a simple frequency distribution table, list all possible categories in the variable in the left hand column and the number of responses for each category in the right hand column.

Using these tables, you can also visualize the distribution of your data set in graphs and charts.

Central tendency

The central tendency of your data set tells you where most of your values lie.

The mode, mean, and median are three most commonly used measures of central tendency. However, only the mode can be used with nominal data.

To get the median of a data set, you have to be able to order values from low to high. For the mean, you need to be able to perform arithmetic operations like addition and division on the values in the data set. While nominal data can be grouped by category, it cannot be ordered nor summed up.

Therefore, the central tendency of nominal data can only be expressed by the mode – the most frequently recurring value.

Mode

To find the mode of your nominal data set, look for the most frequently appearing value in your frequency table.

Since most participants in your study identify as Democrat, the mode is Democrat.

Statistical tests for nominal data

Inferential statistics help you test scientific hypotheses about your data. Nonparametric statistical tests are used with nominal data.

While parametric tests assume certain characteristics about a data set, like a normal distribution of scores, these do not apply to nominal data because the data cannot be ordered in any meaningful way.

Chi-square tests are nonparametric statistical tests for categorical variables. The goodness of fit chi-square test can be used on a data set with one variable, while the chi-square test of independence is used on a data set with two variables.

The chi-square goodness of fit test is used when you have gathered data from a single population through random sampling. To measure how representative your sample is, you can use this test to assess whether the frequency distribution of your sample matches what you would expect from the broader population.

Chi-square test for goodness of fit

Based on current data about your population, you expect 30% of your sample to identify as Democrat, 30% as Republican and 40% as Independent. Instead, your observed data show that 48% of your sample are Democrat, 33% Republican and 19% Independent.

The goodness of fit test statistic tells you how different what you observe is from what you would expect by chance. If the test statistic is zero, there is no difference between what you expect and what you observe.

With the chi-square test of independence, you can find out whether a relationship between two categorical variables is statistically significant.

Chi-square test of independence

If you collect data on employment status as well as political preferences for each participant, you can test whether there is a relationship between the two variables in your sample. Using hypothesis testing, you can formally assess whether two nominal variables from a single sample are independent of each other.

Bhandari, P. (2023, June 21). Nominal Data | Definition, Examples, Data Collection & Analysis. Scribbr. Retrieved July 10, 2024.

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