Data types are an important aspect of statistical analysis, which requirements to be understood to correctly apply statistical techniques to her data. There room 2 main species of data, namely; categorical data and also numerical data.

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As an individual that works through categorical data and also numerical data, that is vital to properly understand the difference and similarities between the two data types. This will certainly make it easy for friend to properly collect, use, and analyze them.

The prestige of knowledge the various data species in statistics cannot be overemphasized. Therefore, in this article, we will be examining at the two main species of data- consisting of their similarities and also differences.

What is Categorical Data?

Categorical data is a form of data that deserve to be save on computer into teams or categories with the aid of name or labels. This group is typically made follow to the data characteristics and also similarities of these characteristics through a an approach known as matching.

Also well-known as qualitative data, each aspect of a categorical dataset deserve to be put in just one classification according come its qualities, where each that the category is mutually exclusive. Because that example, gender is a categorical data due to the fact that it have the right to be categorized into male and female follow to part unique characteristics possessed by every gender.

There space 2 main types of categorical data, namely; in the name of data and ordinal data.

Nominal data

This is the data type of categorical data that names or labels. Sometimes referred to as naming data, it has characteristics comparable to the of a noun.

E. G. Name of a person, gender, school graduates from, etc.


Ordinal data

This type of categorical data includes facets that space ranked, notified or have a rating range attached. One deserve to count and order, in the name data, but it can not be measured.

For example, mean a team of customers were asked to taste the arrays of a restaurant"s brand-new menu ~ above a rating scale of 1 to 5—with each level ~ above the rating scale representing strong dislike, dislike, neutral, like, strong like. In this case, a rating the 5 indicates more enjoyment than a rating the 4, making together data ordinal.

What is numerical Data?

Numerical data is a form of data that is to express in terms of numbers fairly than herbal language descriptions. Comparable to that is name, numerical, it deserve to only be built up in number form. Also known as quantitative data, this numerical data type can be provided as a form of measurement, such together a person’s height, weight, IQ, etc.

It can likewise be used to carry out arithmetic operations prefer addition, subtraction, multiplication, and also division.

There space 2 species of number data, namely; discrete data and consistent data.

Discrete Data:

Discrete data is a form of numerical data through countable elements. I.e they have actually a one to one mapping with natural numbers. A discrete data deserve to either be countably limited or countably infinite. Part general examples of discrete data are; age, variety of students in a class, number of candidates in an election, etc.

A countably limited data deserve to be counted indigenous the beginning to the end, when a countably unlimited data can not be totally counted because it has tendency to infinity.

For example, the bags that rice in a store are countably finite while the grains of rice in a bag is countably infinite

Continuous Data

Continuous is a numerical data form with uncountable elements. Castle are stood for as a set of intervals ~ above a real number heat . Similar to discrete data, consistent data can additionally be one of two people finite or infinite

An uncountable finite data set has an end, when an uncountable unlimited data collection tends to infinity.

Continuous data have the right to be further split into expression data and ratio data. Some instances of continuous data are; student CGPA, height, etc.

15 an essential Differences between Categorical & numerical Data


Categorical data is a type of data that is provided to group information with similar characteristics while number data is a type of data that expresses info in the type of numbers. It combine numeric values to depict relevant information while categorical data supplies a descriptive approach to to express information

We can see the the 2 definitions above are different. Therefore, categorical data and numerical data perform not average the very same thing.

Other Names

Categorical data is likewise called qualitative data while number data is additionally called quantitative data. This is due to the fact that categorical data is supplied to qualified information prior to classifying them according to your similarities.

Numerical data is used to express quantitative values and also can additionally perform arithmetic operations which is a quantitative characteristic.

Both numerical and categorical data have other names the depict your meaning. However the surname are yet different from each other.


Categorical data instances include personal biodata information—full name, gender, phone call number, etc. Number data examples encompass CGPA calculator, interval sale, etc.

The examples listed below are examples of both categorical data and numerical data respectively.

What is your hair colour?BlondeBrunette BrownBlackRed


A CGPA calculator the asks students come input their qualities in each course, and the variety of units to output their CGPA.

For example, 1. Above the categorical data come be built up is nominal and also is built up using an open-ended question. Example 2. Is a number data type.

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Categorical data is separated into 2 types, namely; nominal and also ordinal data while numerical data is categorised right into discrete and consistent data. Continuous data is now further split into expression data and also ratio data.

Although they space both the 2 types, these data varieties are not similar.

Data Characteristics

The characteristics of categorical data include; lack of a standardized order scale, organic language description, takes numeric values through qualitative properties, and visualized making use of bar chart and pie chart.

Numerical data, on the various other hand, has actually a standardization order scale, numerical description, takes numeric values through numerical properties, and also visualized making use of bar charts, pie charts, scatter plots, etc.

User-centred Design

Numerical data collection method is more user-centred 보다 categorical data. Many respondents execute not want to invest a most time pour it until it is full out develops or surveys i m sorry is why questionnaires offered to collect numerical data has actually a lower abandonment rate compared to that of categorical data.

This is since categorical data is mostly built up using open-ended questions.

Data collection Methods

Categorical data have the right to be gathered through various methods, which may differ from categorical data types. Because that instance, nominal data is mostly accumulated using open-ended inquiries while ordinal data is mostly gathered using multiple-choice questions.

Numerical data, top top the various other hand, is mostly accumulated through multiple-choice questions. We observe that it is mostly built up using open-ended questions whenever over there is a need for calculation.

Data repertoire Tools

Data collectors and also researchers collection numerical data making use of questionnaires, surveys, interviews, emphasis groups and observations. Categorical data is accumulated using questionnaires, surveys, and also interviews.

Data repertoire is usually straightforward with categorical data and hence, does not call for technical tools choose numerical data. Because that example, number data of a participant"s score in various sections of one IQ test may be compelled to calculate the participant"s IQ.

When collected using virtual forms, this may require some technical additions to the form, unlike categorical data which is simple.

Analysis & Interpretation

There space 2 approaches of performing numerical data analysis, namely; descriptive and inferential statistics. Some examples of these 2 techniques include; measures of central tendency, turf analysis, text analysis, conjoint analysis, tendency analysis, etc.

There are likewise 2 approaches of evaluating categorical data, namely; median and mode. In part cases, we see that ordinal data Is analyzed using univariate statistics, bivariate statistics, regression analysis, etc. Which is provided as an alternative to calculating mean and standard deviation.


Numerical data is greatly used for calculation difficulties in statistics because of its capacity to perform arithmetic operations. For example, when creating a CGPA calculator, one may need to include commands that permit for the addition, subtraction, division, and also multiplication.

Categorical data, on the various other hand, is mostly used for performing research study that calls for the usage of respondent"s an individual information, opinion, etc. That is generally used in organization research.


Numerical data is compatible with many statistical analysis methods and also as such makes it the most used among researchers. Categorical data, ~ above the various other hand, does no support many statistical evaluation methods.

There are choices to some of the statistical evaluation methods not sustained by categorical data. However, they deserve to not provide results that are as specific as the original.


Numerical data evaluation is largely performed in a standardized or managed environment, which may hinder a proper investigation. This is because natural factors that may affect the results have been eliminated, leading to the results not to be fully accurate.

Numerical data collection is additionally strictly based upon the researcher"s suggest of view, limiting the respondent"s affect on the result. This is not the instance with categorical data.

Nominal data catches human emotions to an level through open-ended questions. However, the setback through this is the the researcher might sometimes have actually to attend to irrelevant data.



Numerical data is compatible with many statistical techniques of data analysis, yet categorical data is incompatible v the bulk of these methods. Therefore, hindering some type of study when dealing with categorical data.

More reasons why many researchers like to use categorical data.


Categorical data can be visualized using just a bar chart and pie chart. The bar graph is used when measuring because that frequency (or mode) when the pie chart is offered when handling percentages. Numerical data, top top the various other hand,d have the right to not just be visualized utilizing bar charts and pie charts, yet it can likewise be visualized utilizing scatter plots.


Categorical data can be thought about as unstructured or semi-structured data. It is loosely formatted with very small to no structure, and also as together cannot be collected and analyzed using traditional methods.

Although there space some techniques of structuring categorical data, that is quiet quite complicated to make ideal sense of it. This an approach is had actually to do with indexing, i m sorry is what find engines like Google, Bing, and Yahoo use.

Numerical data, top top the various other hand, is taken into consideration as structured data. It is formatted in such a method that it have the right to be quickly organized and searchable within relational databases. E.g. Numbers and also values uncovered in spreadsheets.

Similarities in between Categorical & number Data

Ordinal Data

Although proven come be much more inclined come categorical data, ordinal data can be classified as both categorical and also numerical data. In part texts, ordinal data is identified as an intersection in between numerical data and also categorical data and also is thus classified as both.


Numerical and also categorical data can not be used for research and statistical analysis. They might, however, be provided through various approaches, but will offer the same result.

Researchers sometimes discover both categorical and also numerical data when investigating to explore various paths come a solution. Because that example, an organization may decide to investigate which type of data collection technique will assist to reduce the abandonment price by experimenting the 2 methods.

Hence, the organization may ask these 2 inquiries to inspection the response rate.

Question 1:

What do you think about our product? ____

Question 2

Rate ours product ~ above a range of 1 come 5.

12345Numerical Value

Both numerical and also categorical data can take numerical values. Categorical data can take values favor identification number, postal code, phone number, etc. The only distinction is that arithmetic operations cannot be perform on the worths taken by categorical data.

Collection tools

Numerical and also categorical data deserve to both be accumulated through surveys, questionnaires, and also interviews.

What Is The ideal Tool for Collecting numerical & Categorical Data?

It is not sufficient to recognize the difference in between numerical and also categorical data to use them to perform better statistical analysis. You also need to usage dearteassociazione.orgus, the best tool for collecting numerical and also categorical come get far better results.

dearteassociazione.orgus has 30+ kind fields that allow you come ask different types of questions from your respondents. You likewise have access to the form analytics function that shows you the type abandonment rate, variety of people who regarded your type and the gadgets they regarded them from.

Hence, make it possible for you come track whereby your data comes from and ask much better questions to get far better response rates. That doesn"t matter whether the data is being collected for organization or research purposes, dearteassociazione.orgus will aid you collect much better data.

Why use dearteassociazione.orgus to collection Numerical and also Categorical Data?


Work with real data & analytics that will help you reduce type abandonment rates. V dearteassociazione.orgus, you deserve to analyze respondents" data, learn from their behaviour and also improve your type conversion rate.

The kind analytics function gives zero room because that guess games. That is, friend strictly work-related with real data—know the variety of people that fill out your form, wherein they’re from, and also what tools they’re using.


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Respondents in remote areas or locations without a trustworthy internet connection can to fill out develops while offline. The data will be automatically synced when there is an internet connection.

You can additionally use conversational SMS to fill forms, there is no needing internet accessibility at all. This also helps to alleviate abandonment rates and also increase audience reach because it allows people without internet access.

Storage/File Management

Store your digital forms, data and all documents in the limitless cloud storage detailed by dearteassociazione.orgus. The way, her data is not only kept safe and also secure, but you can additionally easily accessibility it anywhere and from any device.

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Save and also Resume

Allow respondents to save partly filled forms and continue at a later on time through the save & Resume feature from dearteassociazione.orgus. Respondents can pick to save the kind and send the attach to their email and also continue from wherein they quit later.

This is a good way to avoid kind abandonment or the pour it until it is full of untrue data when respondents perform not have an instant answer come the questions.


Statistical evaluation may it is in performed utilizing categorical or numerical methods, depending upon the kind of study that is being brought out. A researcher may choose to technique a trouble by collecting number data and also another by collecting categorical data, or even both in part cases.

See more: Category: Japanese Boy Names That Start With D, All Japanese Names Beginning With The Letter D

During the data arsenal phase, the researcher might collect both numerical and also categorical data when investigating come explore various perspectives. However, one demands to recognize the differences between these 2 data types to effectively use that in research.