What is Nominal Data?
Nominal data is defined as a type of categorical classification where only labels are used that have no particular ranking or order. For example, labeling jars in the kitchen as “tea”, “coffee”, “chocolate powder” etc is using nominal data, as they together represent a category, “raw beverages” in this case, but there is no order of increasing or decreasing among them. They are simply mutually exclusive labels, but together represent a category.
Nominal data is the simplest form of classification in comparison to other scales of measurement. Let us deep dive into its key characteristics to get a clearer understanding:
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Categorical Nature: Nominal data is purely categorical labeling, meaning it consists of only categories of labels that represent different groups or classifications. For example, in a categorical classification of “animal types”, labels like “mammal,” “bird,” and “reptile” only serve in identification of a type of animal without any order.
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Mutually exclusive and no inherent order: Nominal data does not have any specific order or ranking among the category labels and they are mutually exclusive. This means, each label is unique in their own right, they don’t overlap and have no sequence. For instance, work places have unique departments that have no specific order to them, such as, “sales”, “marketing”, “HR”, “accounting” etc, and with no department intrinsically being higher or lower.
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Qualitative nature with counting and frequency analysis: Nominal data is qualitative in nature, meaning the categorical labels are not quantifiable, but the data entries themselves can be counted. For example, in a survey response to the type of work profession field that people are engaged in, the number of people who selected “software engineering” is 50, “medical” is 38, “civil engineering” is 45 etc. This can then be used for frequency analysis, for example, the frequency of medical professionals in a sample size of participants.
Examples and Sources of Nominal Data
Nominal data can be used in a variety of contexts where the primary purpose is to categorize or label information. Here are some common examples and sources of nominal data:
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Quantitative research
Nominal data is often used in surveys and questionnaires for market research, census collection, university studies etc. An example of a survey question that uses nominal data can be:
Q – Please select the type of browser you use the most:
Options: A. Safari B. Chrome C. Edge
Notice that each label is a name, the labels don’t have any order by quality or quantity, each is unique and mutually exclusive. Safari does not consist of Chrome and vice versa, same with Edge.
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Qualitative research
Qualitative research may be in the form of user feedback, focus groups, interviews etc. Here, the labels themselves don’t exist, instead the researchers may look for certain words that are repeated. For example, in a product feedback study with one-on-one user feedback sessions, they may look how often do users use certain types of words such as , “can’t find this feature”, “easy to use”, “smooth interface” etc.
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Record keeping:
Institutions, public service offices, companies, they all use nominal data, among others, for maintaining relevant records. Companies, for instance, may use “user interest” as a nominal data. For example, a dog food supplier may keep record of which of its flavours sell by what quantity, such as “chicken and vegetables”, “rice and chicken” etc. There is, again, no specific order among these flavours and they are unique in their own right.
Benefits of using Nominal Data
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Simplicity in classification: Nominal data is easy to collect, categorize, and understand. It involves labeling or naming data without any quantitative value, making it simple to use for classifying information into distinct categories. For example, gender, colors, or types of vehicles are nominal categories that help in grouping and sorting data effectively.
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Effective for frequency analysis: Since nominal data categorizes information, it is highly suitable for frequency analysis, where the count of occurrences in each category is determined. This is useful in understanding the distribution and prevalence of different categories within a dataset. For example, a survey on favorite colors for cars can show how many people prefer each color.
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Flexibility in data representation: Nominal data can be easily represented through various graphical methods such as bar charts or pie charts, making it easy to visualize and interpret. These visual tools help in conveying the distribution of categories clearly to an audience, aiding in data communication and decision-making processes.
Best Practices for Collecting and Managing Nominal Data
Collecting and managing nominal data effectively requires attention to accuracy, consistency, and ethical considerations. Here are the best practices for collecting and managing nominal data:
- Clear and Consistent Categorization
Ensure that the categories for nominal data are clearly defined and understood by respondents or data collectors, avoiding ambiguous or overlapping categories. For example, when asking respondents for a favorite type of cuisine, list clear options like “Indian”, “Thai”, “Greek”, “Italian” etc, instead of vague and broad labels like “Asian”, or “European” or “Mediterranean”.
- Data Privacy and Ethical Considerations
With growing concerns around data privacy, it’s key to adhere to data protection regulations such as the General Data Protection Regulation (GDPR) in the EU or the California Consumer Privacy Act (CCPA) in the US. Always have a clear user consent form with respondent signatures before using their response data.
- Use of Technology and Tools
Leverage modern tools and technologies for data collection and management. Online survey platforms, mobile data collection apps, and cloud-based data storage solutions can help streamline the process, make it easier to collect user consent and analyze the final data.
- Data Quality Checks
During data collection, ensure to check the information collected for data quality standards. For example, in survey research, this may include checking for completeness of survey responses, form field consistency, proper branching of questions based on previous answers etc.
