What is Nominal Scale?
A nominal scale sorts responses into named categories that have no numeric value or natural order. Examples include language, city, or favourite colour.
A nominal scale is the simplest way to classify data: each response is placed into a category (a label or name) and categories are distinct but not ordered. Nominal data can be counted or compared for frequency, but you cannot meaningfully calculate averages, medians or rank them. Sometimes categories are stored as numbers (for convenience), but those numbers are only identifiers — they do not imply size, order or distance between categories. Nominal scales are common in surveys because many demographic and preference questions (language, gender, yes/no, country, product choice) produce categorical answers.
Usage example
A school sends a parent survey asking “Which language would you prefer for school letters?” with answer options: English, Polish, Arabic, Somali, Other. Those answers form a nominal scale: you can report how many parents chose each language and compare proportions, but you cannot compute a meaningful average language.
Practical application
Recognising a question as nominal determines how you analyse and present results: use counts, percentages, bar charts and cross‑tabulations rather than means or standard deviations. For Hearo this matters because multilingual labels must map consistently across languages (so “Polish” in English equals “Polski” in Polish), and analysis or filters should treat categories as labels, not ranked values. Proper handling of nominal data helps you report who responded, compare groups, and ensure translation and coding remain consistent across languages.
FAQ
Can I calculate an average from nominal data?
No. Nominal categories are names, not numbers, so averages or medians are not meaningful. Use counts, percentages or the mode (most common category) instead.
Is it OK to assign numbers to categories?
Yes for coding and analysis, but those numbers are arbitrary identifiers only. Make a codebook and keep it consistent across languages and survey versions so labels map correctly.
How should I visualise nominal results?
Use bar charts, column charts, pie charts or simple frequency tables. For comparisons between groups, use cross‑tabulations (contingency tables) and tests like chi‑square where appropriate.
What if I have many small categories or free‑text answers?
Consider grouping rare categories into an “Other” category and offer an “Other (please specify)” field for detail. For free text, translate and code responses into nominal categories if you want to summarise them quantitatively.