Data analysis is a technique where statistical or logical methods are used to illustrate and evaluate data.
This analysis involves a complete summary of the information that is further used to collect and present the end result so that it communicates the essential findings or options.
But this process needs you to differentiate between the data you have collected. And to simplify the data analysis process, it’s essential to understand the two methodologies – qualitative data and quantitative data.
Qualitative and quantitative data is something that is created, observed, validated, and collected.
You can view the data in the form of figures, facts, measurements, records, notebooks, videos, images, etc., in digital or nondigital format.
However, there are many differences between the two. And to choose one among them in your projects, you must understand each of them separately and how they differ.
So, let’s learn about the differences between qualitative and quantitative data.
Qualitative vs. Quantitative Data: What are they?
To understand these terms better, first focus on this – the former is interpretive, exploratory, and subjective, while the latter is conclusive, objective, and to the point.
Qualitative Data
Qualitative data is simple information that can’t be measured, expressed in numbers, or counted. The information is gathered from audio, images, text, etc., and shared through visualization tools, such as concept maps, timelines, infographics, and more.
For example, when a user visits your website and adds some of the products into the cart but abandons the shopping cart, your work is to investigate ‘why’ and ‘how’, like why the visitor abandoned the shopping cart and how a user feels about your site or products. Here, “quality” is in the main frame; hence, you need to get insights from the qualitative data.
In the above example, you don’t need any numbers to know the reason behind such user behavior but to ask them about the cause and their experiences. In other words, qualitative data is a label or a term used to describe characteristics of certain things, such as labeling an ice cream flavor as chocolate or describing the sea as blue.
Qualitative data is a nonstatistical form and is typically semistructured or unstructured, which means this type of data doesn’t need you to obtain hard numbers through graphs and charts. Instead, they are characterized by their forms, labels, attributes, properties, and other identifiers.
It can be generated through documents, texts, video recordings, audio recordings, focus groups, interview transcripts, notes, and observations. However, identification numbers like driver’s license numbers or social security numbers come under qualitative data as they are unique and categorical to one person.
Examples: Golden knobs, smooth finish, deep brown, American nuts flavor, built in Italy, Green shirts, blue ocean, beautiful pictures, and so on.
Quantitative Data
Quantitative data is simple information that can be measured or counted in numerical values. This can be referred to as any quantifiable data that researchers use to get statistical analysis and mathematical calculations to make quality decisions based on the derivations.
These types of data answer questions like ‘how many?’, ‘how often?’ and ‘how much?’. Quantitative data can be easily verified and evaluated by using mathematical techniques. Let’s understand with an example:
A person asks the shopkeeper, “How much does that printer cost?”
It is a quantitative question to collect data like the price for different devices. Values are associated with the most measuring parameters like kilograms for weight, pounds, dollars for cost, and more.
Quantitative data is collected for statistical analysis via polls, questionnaires, surveys, etc. In simple terms, you can say the information that can be “quantified” is called quantitative data. It is structured in nature and can be calculated using statistics and reports, which means it is defined and rigid.
Quantitative data is much more closeended and concise that can be generated through tests, surveys, experiments, metrics, market reports, and more.
Examples: Weight in kilograms, number of weeks in a month, height in inches or feet, distance in kilometers or miles, age in years or months, revenue in dollars, length in centimeters, and so on.
Qualitative vs. Quantitative Data: Differences
Qualitative and quantitative data – both have the same place in every field. To understand each better, we need to see where they differ according to their criteria, functions, nature, and more.
Let’s understand some basic differences first:
 The data in which the classification of objects depends upon the quality or attributes is called qualitative data. In contrast, the data that can be counted or expressed in numbers is called quantitative data.
 Qualitative data relies on understanding between persons, emotions, color, etc. On the other hand, quantitative data relies on values, where you will obtain the results in statistics for making decisions.
 In qualitative data, verbal information is collected, but in quantitative data, measurable information is gathered.
 Qualitative data develops an initial understanding, but quantitative data recommends a final action.
Until now, we have understood the main concept behind both data types. Now, we will dig into some key differences between the two.
Here we go!
Criteria  Qualitative Data  Quantitative Data 
Definition  Qualitative data analysis is a simple technique used to develop an understanding of social and human sciences to get the end result.  Quantitative data analysis is a technique used to generate hard facts and numerical information through logical and mathematical techniques. 
Data  It contains data like religion, nationality, the color of certain things, taste, gender, and more.  It contains weight, mass, size, height, price, and more. 
Approach  It follows subjective analysis, which is involved in nonstatistical data that can’t be calculated.  It follows objective analysis that can be calculated easily through mathematical derivations. 
Analysis  The analysis refers to why a development happens, how a user feels, why the cart is abandoned, etc.  The analysis here explains the number or quantity of development, like the cart abandonment rate. 
Sample  Here, the sample is nonrepresentative and small throughout the whole process.  The sample is massive and can be generalized. 
Collection methods  Qualitative data can be collected using written documents, interviews, observations, etc.  Quantitative data is collected using experiments, interviews, observations, surveys, polls, etc. 
Data type  It is textbased.  It is numberbased. 
Results  Results are aggregated for analyzing the data or simply conferred.  Here, results depend upon the variation through graphs and charts. 
Elements  Words, objects, pictures, etc.  Numerical and graphical information 
RealLife Examples
Example 1: (Office Space)
Office space refers to both data types depending upon the functions of each element.
Qualitative Data
 Large and spacious
 Great natural light
 Large pantry area
 Cool water fountain
 Eyecatching wall paints
 Nice picture frames
 Large space for indoor games
Quantitative Data
 12000 sq feet of floor area
 Number of floors
 Number of windows
 Number of doors
 Number of LEDs
 Number of systems
Example 2: (Article on a website)
Qualitative Data
 Nature of article
 Quality of the article like spelling, grammar, punctuation, etc.
 How readers feel about it
 How well it describes the topic and terms
 Quality of videos and audio used
Quantitative Data
 Word count
 Number of images used
 Engagement like comments, page views, etc.
 Load time
 Number of leads generated
Example 3: (University Area)
Qualitative Data
 Large and dense trees
 Bright colors
 Nextgen architecture
 Smart lights and fans
 Large classrooms
 Cool projector
 Quality books
Quantitative Data
 Number of classrooms
 Number of blocks
 Number of AC vents
 A square meter of the parking area
 Classroom size
 Number of benches
 Number of floors
Qualitative Data Vs. Quantitative Data: Types
Types of Qualitative Data
Statisticians and researchers categorize qualitative data into three types:
 Binary data: When you refer to an item as good or bad, hard or soft, right or wrong, fresh or stale, etc., it is known as binary data. In other words, it is qualitative data that you can characterize through mutually exclusive traits, which means they cannot happen simultaneously. Statisticians use this data to create a model that predicts the nature of that item.
 Nominal data: It is also called labeled, nominal scale, or named data. This is a type of data that you can use to name something without mentioning any numerical value.
For example, if you form a group of items by color, you can directly label each item according to its colors. Researchers use these nominal data to differentiate between the set of information, such as color. This type can also be used by statisticians and researchers to create a multiplechoice survey to know which one is good.
 Ordinal data: Ordinal data is a type of qualitative data that is categorized on a certain scale or order. It is an essential step toward the collection of data.
For example, when a responder inputs the service happiness level on a scale of 110, it collects data according to the inputs. Here, there is no standard scale where the difference between each can be measured. Some examples are the Likert scale, interval scale, etc.
Types of Quantitative Data
Quantitative data is divided into two main types discrete and continuous data. Let’s understand them one by one.
Discrete Data
Discrete data is the type of quantitative data that contains number counting only. This does not involve any type of measurement like length, weight, height, etc.
For example, number of students, number of days, number of ceiling fans, age of an individual, and more.
While identifying discrete data, you need to use several questions to differentiate like:
 Can it be counted?
 Can it be divided?
 Can it be measured?
And so on…
Discrete data is also known as attribute data that can not be broken into smaller parts. You can say it to be either countably finite or infinite.
Example: The countably finite data is the arbitrary set of A = {1,2,3,4,….,n; where n is a number less than infinity}. The countably infinite data is the arbitrary set B = { 1,2,3,….}.
Continuous Data
It is a quantitative data type that can be placed on a measurement scale, which means this takes on numeric values that can be broken into smaller parts. You can refer to continuous data as uncountably finite and uncountably infinite.
For example, the CGPA of students is measured on a 10point scale. Here, you can say that a student can score between 0 to 10 points, including 8.5, 1.57, 4.65, 2.68, 9.8, etc. This data can be classified as uncountably finite continuous data since it has an upper and lower limit.
Similarly, you can take the example of uncountably infinite data. It is the set of real numbers, R = {….,1,0,1,….}. In this scenario, the data has neither an upper limit nor a lower bound.
Continuous data is again divided into two types:
 Interval data
 Ratio data
Interval data is a simple technique that can be measured along a scale where every point is placed at the same distance from one another. On the other hand, ratio data is the extension of interval data. It has the ultimate use when we talk about the exact data measurement. Ratio data tells about the order, exact distance, and more.
Ways to Generate Qualitative and Quantitative Data
Before we go deeper into the different data collection methods for qualitative and quantitative data, let’s try to understand the types of data collection first.
Data collection methods are as follows:
 Surveys, questionnaires, and quizzes
 Interviews
 Focus groups
 Direct observations
 Documents
Data collection methods can be classified into quantitative and qualitative data types.
Qualitative Data Collection Methods
 Questionnaires and openended surveys: It is the most used method to collect or gather data through different questionnaires and openended surveys. It allows the respondents to give answers much more flexibly. It doesn’t contain any predefined answers or options that allow a user to write freely.
 1on1 Interviews: This is also called a facetoface interview which is used as a common type of data collection method for qualitative data. Here, you can easily gather data from the interview. This technique is specially used when you want to collect highly personalized data.
 Focus Groups: Focus group is also an interview method. But instead of a 1on1 interview, it carries a group discussion. Here, the resources are not limited in terms of money, time, etc. It comes in very handy.
For example, if you carry out a study on the rehabilitation of teenage drug users, all the members in the group have to be teenagers who are recovering from drug addiction.  Direct Observation: This is the most passive way of collecting data. The data collector takes the place of the participant, observes the setting thoroughly, and records the audio or video also photos. This leads to bias in nature as it involves direct observation.
Quantitative Data Collection Methods
 Online quizzes and closeended surveys: This method is based on questions that allow respondents to choose from the options. It is divided into types – categorical and ratio/intervals.
Categorical questions can be categorized as dichotomous (yes or no), checkbox questions, and multiple choice questions. In comparison, interval questions consist of a Likert scale, matrix questions, rating scale, etc.
Advantages of Quantitative Data over Qualitative Data
Quantitative data has many advantages over qualitative data because you can measure and prepare a chart or report out of it easily. It also takes less time as compared to the qualitative data. This technique is used when you don’t know what to expect.
Let’s understand the advantages of quantitative over qualitative data:
 More scientific
 Control sensitive
 Less objective
 Focused
 Can deal with larger samples
 Arranged in a simple method
 Repeatable
 Relatable
 Structured
 Generalizable
 Consistent
 Fast and saves time
 Useful for quality decision making
 More acceptable data
 Highly reachable
 Can use random samples
 Doesn’t require direct observation
Conclusion
Qualitative data is difficult to analyze as compared to quantitative data. It uses common approaches like qualitative content analysis, thematic analysis, and discourse analysis. Quantitative data, on the other hand, is based on numbers or values that use SPSS, R, or Excel to calculate things like average scores, the number of times a particular question is asked, validity, etc. The results are reported in tables or graphs.
This post should help you understand the difference between qualitative and quantitative data and what to choose for which application.

Durga Prasad Acharya is a Freelance Technical Writer who loves writing on emerging technologies, such as AI & ML, Cybersecurity, Web Hosting, SaaS, Cloud Computing, and more. Besides writing, he’s a web designer and is passionate about… read more