Nominal data is the simplest level of measurement, but that does not make it unimportant. A huge proportion of variables collected in social research, healthcare, education, and market research is nominal, and handling it incorrectly is one of the most common statistical mistakes in student work.
What Makes Data Nominal
Nominal measurement classifies observations into distinct, mutually exclusive categories. The categories have no natural order or hierarchy, which means that you cannot meaningfully rank them. Nominal data has three main characteristics.
- Categories are mutually exclusive, each observation belongs to exactly one category.
- Categories are exhaustive, every possible observation has somewhere to go.
- No ordering is implied, one category is not more or less than another.
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If numbers are assigned to categories, they are labels only. Coding male = 1 and female = 2 does not mean that female is twice anything. Swapping the codes to male = 5 and female = 3 would change nothing statistically.
Examples Of Nominal Data
| Research Area | Nominal Variable | Possible Categories |
|---|---|---|
| Education | Type of school attended | State comprehensive, grammar, independent, sixth-form college |
| Healthcare (NHS) | Method of referral | GP referral, A&E, self-referral, emergency transfer |
| Housing | Tenure type | Owned outright, mortgaged, rented (private/council/HA), other |
| Employment | Employment status | Full-time, part-time, self-employed, unemployed, student, retired |
| Transport | Main mode of commute | Car, train, bus, bicycle, walking, working from home |
| Social research | Religious affiliation | Christian, Muslim, Hindu, Jewish, No religion, Other |
Appropriate Statistics For Nominal Data
Because nominal categories have no mathematical relationship, most standard statistics do not apply. Here is what you can and cannot do:
| Statistic / Test | Appropriate | Notes |
|---|---|---|
| Frequency count | Yes | The primary descriptive tool for nominal data |
| Percentage | Yes | Convert frequencies to proportions of the total |
| Mode | Yes | Only appropriate measure of central tendency |
| Mean | No | Categories have no mathematical relationship |
| Median | No | Cannot order categories without imposing an arbitrary ranking |
| Chi-square test | Yes | Tests association between two nominal variables |
| Cramér’s V | Yes | Effect size for chi-square, measures strength of association |
| Pearson correlation | No | Requires at least interval data |
| Independent t-test | No (as outcome) | Use logistic regression for nominal outcomes instead |
Visualising Nominal Data
- Bar chart – the standard choice. Each bar represents a category; the height shows frequency or percentage. Bars should be in a meaningful order (e.g., descending frequency, or a conventional order like alphabetical).
- Pie chart – useful when you want to show proportions of a whole, but becomes difficult to read with more than four or five categories.
- Stacked bar chart – helpful for comparing nominal distributions across groups (e.g., mode of transport broken down by gender).
- Avoid histograms – these are for continuous or at least ordered data.
Example: A study comparing NHS referral routes across five hospitals would produce a bar chart with referral type on the x-axis (GP referral, A&E, self-referral, etc.) and percentage of patients on the y-axis. Each hospital could be a separate bar cluster for comparison.
Coding & Entering Nominal Data
In quantitative data files, nominal categories are usually assigned numeric codes: England = 1, Scotland = 2, Wales = 3, Northern Ireland = 4. This is necessary for software to handle the data, but remember, those numbers are codes, not quantities.
- In SPSS: set the ‘Measure’ column to ‘Nominal’ to tell the software how to treat the variable.
- In R: wrap nominal variables in factor() to define them as categorical.
- In regression analysis with 3+ nominal categories: use dummy coding, creating separate binary (0/1) variables for each category except one (the reference category).
Student Tip: If you have a nominal predictor variable with four categories in a regression model, create three dummy variables. The omitted category becomes your baseline, and the coefficients for the other three show the difference between each category and the baseline. This is a standard technique but surprises many students the first time they encounter it.
Nominal Vs Ordinal
Before treating data as nominal, ask: can these categories be meaningfully ranked? If yes, the data is ordinal, not nominal.
- Employment status (employed / unemployed / student) – no inherent ranking. Nominal.
- Highest qualification (GCSE / A-level / undergraduate / postgraduate) – clearly ordered. Ordinal.
- Type of accommodation (halls / private rent / family home / other) – no natural ranking. Nominal.
- Satisfaction with accommodation (very dissatisfied … very satisfied) – clearly ordered. Ordinal.
Frequently Asked Questions
It is a type of qualitative data that divides variables into groups and categories.
Nominal data can be collected through surveys, interviews, online questionnaires, and so on. You can either have close-ended questions for drawing certain conclusions or have open-ended questions.
Though both levels of measurements are considered categorical, there is a slight difference between the two. There is no order or hierarchy in nominal data, while ordinal data can be grouped into categories following a meaningful order.
There are some general steps you must follow when assessing and evaluating data to form conclusions. These steps include collecting descriptive statistics, visualizing the data, and then carry out statistical analysis.
Descriptive Statistics
In order to see how data can be distributed, descriptive statistics can be used. The most common descriptive statistics for nominal data are central tendency and frequency distribution.
Visualizing Nominal Data
Presenting your data in a visual set-up is called visualizing data. You can either use charts or graphs to see what your data is telling, just like we discussed in frequency distribution tables. Again, using Microsoft Excel for this would be convenient and quick.
Statistical Tests of Nominal Data
Two types of statistical tests you can use for testing your hypotheses are:
- Parameter Tests-used for ratio data and interval
- Non-parametric Tests-used for ordinal and nominal data
Ratio data, just like ordinal and interval data, can also be ranked and categorized. The intervals here are also equally spaced. The only thing that makes this one different from the rest is that ratio data has a true zero. For example, if you measure something in kgs, it is ratio data. Now, if something weighs zero, then it means that it does not weigh anything or most likely does not even exist. However, if the temperature in Fahrenheit or any other scale is zero, then that does not imply ‘no temperature.’
