When collecting your data for research, it is important to know the form of your data to interpret and analyze it effectively. In a research study, there are mainly two types of data: Categorical and numerical.
It is important to identify both based on their differences and similarities. In this article, we will focus on categorical and numerical data, what they are, and how they differ from each other. Let’s get started.
Let us first begin by understanding the two types of data by looking at their definitions.
Categorical data refers to a data type that can be stored and identified based on the names or labels given to them. A process called matching is done to draw out the similarities or relations between the data and then grouped accordingly.
The data collected in the categorical form is also known as qualitative data. Each dataset can be grouped and labeled depending on their matching qualities under only one category. This makes the categories mutually exclusive.
Numerical data refers to the data that is in the form of numbers, and not in any language or descriptive form. Often referred to as quantitative data, numerical data is collected in number form and stands different from any form of number data type due to its ability to be statistically and arithmetically calculated.
It doesn’t involve any natural language description and is quantitative in nature and it is used to measure quantities like a person’s height, age, IQ, etc.
Here, we’ll look into their sub-categories to make it easier for you to identify which data you have gathered or have been gathering in your research.
There are two subtypes of categorical data: Nominal and Ordinal.
This is also called naming data. This type names or labels the data, and its characteristics are similar to a noun. Example: person’s name, gender, school name.
Questions to gather nominal data look like this:
This includes data or elements of data that are ranked, ordered, or used on a rating scale. You can count and order ordinal data, but it doesn’t allow you to measure it.
Example: seminar attendants are asked to rate their seminar experience on a scale of 1-5. Against each number, there will be options that will rate their satisfaction, such as “very good, good, average, bad, and very bad.”
It also has two subtypes known as Discrete data and Continuous data.
Discrete data is used to represent countable items. It can take both numerical and categorical forms and group them into a list. This list can be finite or infinite, too.
Discrete data takes countable numbers like 1, 2, 3, 4, 5, and so on. In the case of infinity, these numbers will keep going on.
Example: counting sugar cubes from a jar is finite countable. But counting sugar cubes from all over the world is infinitely countable.
As the name says, this form has data in the form of intervals. Or simply said ranges. Continuous numerical data represent measurements; their intervals fall on a number line. Hence, it doesn’t involve taking counts of the items.
For example, in a school exam, students who scored 80%-100% come under distinction, 60%-80% are first-class, and below 60% are second-class.
Continuous data is further divided into two categories: Interval and Ratio.
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| Features | Categorical data | Numerical data |
| Definition | Categorical data refers to a data type that can be stored and identified based on the names or labels given to them. | Numerical data refers to the data that is in the form of numbers, and not in any language or descriptive form. |
| Alias | Also known as qualitative data as it qualifies data before classifying it. | Also known as quantitative data as it represents quantitative values to perform arithmetic operations on them. |
| Examples | What is your gender?
|
What is your test score out of 20?
|
| Types | Nominal data and Ordinal data. | Discrete data and Continuous data. |
| Characteristics |
|
|
| User-friendly design | Can include long surveys and has a chance of pushing respondents away. | Survey interaction is easy and short, hence fewer survey abandonment issues. |
| Data collection method | Nominal data: open-ended questions Ordinal data: multiple-choice questions | Mostly collected through multiple-choice questions and sometimes through open-ended questions. |
| Data collection tools | Questionnaires, surveys, and interviews | Questionnaires, surveys, interviews, focus groups and observations |
| Analysis and interpretation | Median and mode Eg: univariate statistics, bivariate statistics, regression analysis | Descriptive and inferential statistics Eg: measures of central tendency, turf analysis, text analysis, conjoint analysis, trend analysis |
| Uses | Used when a study requires respondents’ personal information, opinions and experiences. Commonly used in business research | Used for statistical calculations as a result of the potential performance of arithmetic operations |
| Compatibility | It is not compatible with most statistical analysis methods, hence researchers avoid using it most of the times | It is compatible with most statistical calculation methods. |
| Visualization | Can be visualized using only bar graphs and pie charts. | Can be visualized using bar graphs, pie charts as well as scatter plots. |
| Structure | Is known as unstructured or semi-structured data It can use indexing methods to structure data like Google, Bing, etc. | It is structured data and can be quickly organized and made sense of |
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It can be considered as a crossover between categorical and numerical data. Even though ordinal scale is generally identified as a subtype of categorical data, it can be called numerical data too.
Numerical and categorical approaches when used for research and statistical analysis, are going to yield similar results.
Researcher sometimes decides to use them both together in a survey to find out different ways to approach the data.
Example:
A seminar organizer wants to know the reviews of people who attended the seminar. He can ask them questions in two ways:
Q1) Rate our seminar on a scale of 1-5
Q2) Can you explain your reason for the score?
Both categorical data and numerical data are most commonly collected through methods like surveys, questionnaires, and interviews.
Surveys are the most common data collection method used by researchers. It can be designed to gather categorical data and numerical data.
You can either ask your participants to answer with yes/no or use Likert scale questions to gather numerical data. You can also use open-ended questions to gather necessary information from the target audience.
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Data is an integral element of any research, be it market research, academic research, or social research. Your data has to be accurate and precise to generate meaningful insights.
To ensure you take advantage of both categorical and numerical data the best way is to use both types in your research. For example, follow up an NPS® question with a qualitative question to gather in-depth information from your audience.
If you want to know how you can gather customer intelligence in categorical data and numerical data you can contact us.
Net Promoter®, NPS®, NPS Prism®, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Satmetrix Systems, Inc., and Fred Reichheld. Net Promoter Score℠ and Net Promoter System℠ are service marks of Bain & Company, Inc., Satmetrix Systems, Inc., and Fred Reichheld.
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