Data Demystified: Qualitative Data vs Quantitative

Decide whether you’re going to employ a qualitative or quantitative research strategy now. And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be drawn to quantitative approaches because they dislike interacting with people and handling “soft” issues and find numbers and algorithms to be much more comfortable. On the other hand, because they literally have the opposite anxieties, anthropologists are presumably more interested in qualitative methodologies.
But using “fear” as a justification for your research is not a wise course of action. Your methodology needs to be informed by your research aims and objectives, not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methodologies can be mastered (typically a lot faster than you expect) and software simplifies a lot of the complexity of both quantitative and qualitative data analysis. On the other hand, picking the incorrect strategy and attempting to squeeze a square peg into a round hole would only lead to even greater suffering.

In this essay, I’ll discuss the qualitative vs quantitative option in basic, plain language with heaps of examples. Although you won’t become an expert in either field as a result of this, you should have enough of a “big picture” understanding to be able to choose the best research methodology

Qualitative: The Basics

Qualitative data, as contrast to quantitative data, cannot be counted or quantified. It is descriptive and uses language to describe ideas rather than numbers.

For an explanation of “Why?,” researchers frequently turn to qualitative data, or “How?” inquiries. For instance, you might want to analyze why a certain website visitor abandoned their shopping cart three times in one week if your quantitative data indicates that they did so. To do this, you might need to get some type of qualitative information from the user. Perhaps you are interested in knowing how a user feels about a specific product; in this case, qualitative data can offer these insights. In this situation, you’re not just looking at numbers; you’re also asking the user to explain their actions or feelings to you in English.

The terms or labels used to define certain qualities or traits—such as characterizing the sky as blue or designating a specific flavor of ice cream as vanilla—are sometimes referred to as qualitative data.

Let’s take a look at the example below:

The water is hot.
Let’s explore that further. What exactly does the phrase mean? Is it helpful, too?

The response is: well, it depends. You’re out of luck if you want to know the water’s precise temperature. But if you put on your qualitative hat and try to understand how someone feels about the temperature of the water, that line can tell you a lot.

Because of their deeply held, relationship-destroying beliefs about water temperature, many husbands and wives have never shared a bath together (or so I’m told). Additionally, while analyses of the inevitable arguments and disagreements over water temperature would more comfortably fit in the category of “qualitative research,” divorce rates resulting from differences in how people perceive water temperature would more appropriately belong in “quantitative research.” This is because by methodically coding and analyzing the data, qualitative research enables you to comprehend people’s perceptions and experiences.

Those heated debates can be examined in a variety of ways using qualitative research. From focus groups to interviews to direct observation (ideally outside the bathroom, of course). The way the argument develops or the emotional language used during the discussion may be of interest to you as the researcher. You might be more interested in the body language of someone who has been repeatedly dragged into (what they perceive to be) scalding hot water during what was supposed to be a romantic evening than you are in the actual words. Qualitative research can help us better understand all of these “softer” elements.

Qualitative research may be quite rich and thorough in this approach, and it is frequently used as a foundation for developing ideas and spotting trends. In other words, as opposed to confirmatory research, it works well for exploratory research (for instance, when your goal is to learn what individuals believe or feel) (for example, where your objective is to test a hypothesis). To better understand human perception, worldview, and the way we describe our experiences, qualitative research is used. It’s about studying and understanding a large issue, often with very little preconceived beliefs as to what we may uncover.

Quantitive: The Basics

Any information that can be quantified is referred to as quantitative data. Quantitative data can be counted, measured, and assigned a numerical value. Quantitative information can provide you with “how many,” “how much,” or “how often” information. For instance, how many people viewed the webinar last week? How much money did the business bring in in 2019? How frequently does a certain clientele utilize internet banking?

You’ll do statistical studies to examine and interpret quantitative data.

Lets take a look at the example below:

The water is 45 degrees Celsius.

What does this mean, exactly? What is the use of this?

Someone who I am absolutely not married to once informed me that he frequently takes cold showers. This seems completely absurd to me because I’m frightened of anything that isn’t body temperature or above. But this begs the question: what temperature makes the ideal bath? Or, at the very least, what is the average temperature of baths? (Obviously assuming they are bathing in water that is perfect for them.) You must now put on your quantitative hat in order to respond to this question.

We could determine the average temperature for each person if we asked 100 people to record the temperature of their bathwater over the course of a week. Let’s say, for example, that Jane averages 46.3°C. Billy averages 42 degrees Celsius. Some folks might enjoy the unnatural chill of 30°C on a typical weekday. And some of those will be aiming for the 48°C threshold, which is reportedly the legal maximum in England (now there’s an interesting fact for you).

There are many different approaches to analyze this data using a quantitative approach. For instance, we may examine these data to determine the average temperature or to see how widely the temperatures range. We could check to see whether there are major differences in optimal bath water temperature between the sexes or if aging has any bearing on this! We could plot this data on interesting, eye-catching graphs and use esoteric terms like “eigenvalues,” “significant,” and “correlation.” There are countless opportunities to geek out…

This is how quantitative research frequently entails going into the study with some amount of anticipation or comprehension of the results, typically in the form of a hypothesis that you want to test. For instance:

Theoretically, men like taking baths in water that is warmer than women.

Statistical analysis can then be used to examine this hypothesis. The data might support the hypothesis or might show that there are some subtleties in terms of people’s preferences. Men, for instance, might prefer a hotter bath on particular days.

As you can see, each qualitative and quantitative research method serves a distinct purpose. They are merely different tools for a variety of tasks.

What are the main differences between quantitative and qualitative data?

Quantitative and qualitative data differ primarily in what they reveal, how they are gathered, and how they are examined. Before delving deeper into each distinction, let’s briefly review the main differences:

Quantitative information is measurable or countable and has to do with numbers. Qualitative data is linguistically descriptive.
Quantitative data provides information about quantity, amount, or frequency (for example, “20 people subscribed to our email newsletter last week”). Qualitative data, such as “The mailbox is red” or “I joined up for the email newsletter because I’m extremely interested in hearing about local events,” might help us understand the “why” or “how” behind particular behaviors. It can also just describe a particular aspect.
While qualitative data is ephemeral and changeable, quantitative data is static and “universal.” For instance, the fact that something weighs 20 kilograms can be regarded as an objective fact. However, the qualitative accounts of how two people experience the same incident may vary greatly.
The collection of quantitative data involves measuring and counting. Qualitative data is gathered through observation and interviewing.
While qualitative data is evaluated by categorizing it into relevant categories or topics, quantitative data is analyzed through statistical analysis.

The difference between quantitative and qualitative data:

Example:

Let’s look at an example to show the distinction between quantitative and qualitative data. Think about how you would characterize your best friend. What kind of information could you collect or use to draw a clear picture?

You could start by describing the person’s physical characteristics, such as height, weight, hair color and style, and foot size. You might then go over some of their more notable personality traits. Additionally, you could mention their residence, where they live, how frequently they go swimming, and how many siblings and animals they have (their favorite hobby).

The following information will all fall into either the quantitative or qualitative categories:

Quantitive data:

My closest pal is 5 feet 7 inches tall.
Their feet are a size 6.
They are 63 kilos heavy.
One of my best friend’s siblings is older, and she has two younger siblings.
They own two felines.
My closest friend is located 20 miles away.
Each week, they swim four times.

Qualitative data:

Curly brown hair belongs to my best pal.
Their eyes are green.
My best friend is noisy, witty, and an excellent listener.
Additionally, they might occasionally be impetuous and irritable.
A crimson automobile is driven by my best friend.
They exude friendliness and have an infectious chuckle.

Of course, you’ll deal with a lot more complicated data than the ones we’ve provided when working as a researcher or data analyst. But maybe our “best buddy” example has helped you recognize the difference between quantitative and qualitative data.

Different types of quantitative and qualitative data

When considering the difference between quantitative and qualitative data, it helps to explore some types and examples of each. Let’s do that now, starting with quantitative data.

Types of quantitative data (with examples)

Quantitative information might be continuous or discontinuous.

Data that is discrete quantitatively expressed has set numerical values and cannot be further subdivided. When you count something, like the number of individuals in a room, you are using discrete data. 32 people make this a fixed and finite number.
Quantitative data that is continuous can be divided indefinitely into smaller units and plotted on a continuum. It can be any number; for instance, a piece of string could be 20.4 cm long or the temperature in the room could be 30.8 degrees.

What are some real-world examples of quantitative data?

Typical instances of quantitative data include the following:

dimensions like weight, length, and height
Counts like the volume of sales, internet visitors, or email signups
computations, like revenue
Projections, such as anticipated sales or a forecast percentage growth in revenue
Quantification of qualitative data, such as obtaining an overall customer satisfaction score by having customers assess their satisfaction on a scale of 1 to 5

Types of qualitative data (with examples)

Nominal or ordinal data types can be used to classify qualitative data:

Certain variables are labeled or categorized using nominal data without being given any kind of quantitative value. For instance, you could want to know where your target audience resides if you were gathering data on them. Are they based in Australia, Asia, the USA, the UK, or another country? These geographical divisions are all considered notional data. Another straightforward illustration is the description of eye color using terms like “blue,” “brown,” and “green.”
When the categories used to categorize your qualitative data fall into a natural order or hierarchy, it is said to have ordinal data. It’s obvious that “outstanding” is better than “poor,” but there’s no way to measure or quantify the “distance” between the two categories. As an illustration, if you wanted to investigate customer satisfaction, you might ask each customer to select whether their experience with your product was “poor,” “satisfactory,” “good,” or “outstanding.”

Ordinal and nominal data frequently appear when conducting questionnaires and surveys. But qualitative data also includes unstructured data, such as what individuals say in an interview, what they write in a product review, or what they post on social media. It is not just restricted to labels and categories.

What are some real-world examples of qualitative data?

Examples of qualitative data are as follows:

Transcripts of interviews or audio files
Text that appears in emails or social media posts
Product evaluations and client endorsements
Descriptions and observations, such as “I saw the teacher was wearing a red jumper.”
Survey and questionnaire labels and categories, such as choosing whether you are content, dissatisfied, or neutral with a specific good or service

How are quantitative and qualitative data collected?

The method of data generation or collection is one of the main distinctions between quantitative and qualitative data.

How is quantitative data generated?

Calculations, measurements, and counting of particular things are used to provide quantitative data. Typical techniques for gathering quantitative data include:

Surveys and questionnaires: This approach is particularly helpful for acquiring a lot of data. You could send out a survey asking workers to assess various aspects of the company on a scale of 1 to 10, if you wanted to collect quantitative information on employee satisfaction.
Analytics tools: Data scientists and analysts collect quantitative data from a variety of sources using specialized tools. For instance, Google Analytics collects data in real-time, enabling you to quickly analyze all the most crucial website metrics including traffic, page views, and average session length.
Environmental sensors: A sensor is an electronic device that monitors changes in the environment and transmits that data to another electronic device, typically a computer. This data is numerically transformed, resulting in a steady stream of quantitative information.
Manipulation of previously collected quantitative data: Researchers and analysts may also produce new quantitative data by analyzing or calculating previously collected quantitative data. For instance, you could create new quantitative data by calculating the entire profit margin if you have a spreadsheet with information on the quantity of sales and expenditures in USD.

How is qualitative data generated?

Through observations, surveys, and interviews, qualitative data is acquired. Let’s examine these techniques in greater detail:

Interviews are a terrific method to find out what people think about any subject, whether it be their experiences with a certain service or their thoughts on a new product. You will eventually receive interview transcripts after conducting interviews, which you may then examine.
Additionally, questionnaires and surveys are employed to collect qualitative data. If you wanted to gather demographic information about your intended audience, you could invite them to fill out a survey where they could either choose their answers from a range of alternatives or just type them down in freeform.
Observations: Collecting qualitative data doesn’t always need you to interact with individuals directly. Additionally, analysts will look at “naturally occurring” qualitative data, such as comments made in product reviews or things individuals post on social media.

Analysis techniques for qualitative versus quantitative data

The manner in which they are examined is another significant distinction between quantitative and qualitative data. While qualitative data is typically evaluated by classifying it into useful categories or themes, quantitative data is better suited for statistical analysis and mathematical calculations.

Quantitative data analysis

The type of data you’ve collected and the insights you hope to glean will determine how you analyze your quantitative data. In addition to many other things, statistical analysis can be used to spot trends in the data, determine whether a group of variables are related in any way (e.g., does social media spending correlate with sales), calculate probability in order to precisely predict future outcomes, comprehend how the data is distributed, and much more.

Some of the most popular methods used by data analysts include:

Regression analysis
Monte Carlo simulation
Factor analysis
Cohort analysis
Cluster analysis
Time series analysis

What are the advantages and disadvantages of quantitative vs qualitative data?

When conducting any kind of study or gathering data for analysis, it’s critical to keep in mind the benefits and drawbacks of each type of data. We’ll now list the key benefits and drawbacks of each.

What are the advantages and disadvantages of quantitative data?

Quantitative data has the advantage of being generally rapid and simple to gather, allowing you to work with huge samples. Quantitative data is objective and less prone to bias than qualitative data, making it simpler to reach trustworthy and applicable generalizations.

Quantitative data’s fundamental drawback is that it sometimes lacks context and depth. It’s not always clear from the stats what’s going on; for instance, you might find that you lost 70% of your newsletter subscribers in a single week, but you won’t know why unless you look into it further.

What are the advantages and disadvantages of qualitative data?

Qualitative data excels where quantitative data fails. The main benefit of qualitative data is that it provides in-depth, comprehensive insights and enables you to investigate the context of a particular topic. If you want to understand how your target audience behaves and run any kind of organization, you need to be able to accurately evaluate how people feel and why they do certain things through qualitative data.

However, gathering qualitative data can be more difficult and time-consuming, so you might end up using fewer samples. It’s critical to be mindful of bias when conducting qualitative analysis since qualitative data is prone to interpretation due to its subjective nature.

When should I use qualitative or quantitative data?

Simply said, your data analytics project will determine whether you employ qualitative, quantitative, or a combination of both types of data. We’ll talk about which projects work best with different types of data here.

To decide whether to employ qualitative data, quantitative data, or a mixed methodologies approach to gathering data for your project, you can generally use the following criteria.

Do you want to comprehend a certain idea, event, or point of view? Use qualitative information.
Do you want to validate or put to the test a theory or a hypothesis? Utilize numerical data.
Do you conduct research? A mixed approaches approach to data collection could be advantageous.

You might discover that both sorts of data are frequently employed in projects to get a clear overall picture—integrating both the human and numerical sides of things.

Thoughts:

We defined quantitative and qualitative data and described how they differed throughout this piece. In essence, qualitative data is descriptive and has to do with language, whereas quantitative data is countable or quantifiable and has to do with numbers.

One of the very first stages to being a data specialist is comprehending the distinction between quantitative and qualitative data.