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If you’re considering a career in data—or in any kind of research field, like psychology—you’ll need to get to grips with two types of data: Quantitative and qualitative. Quantitative data is anything that can be counted or measured; it refers to numerical data. Qualitative data is descriptive, referring to things that can be observed but not measured—such as colors or emotions. In this post, we’ll define both quantitative and qualitative data in more detail. We’ll then explore all the key ways in which they differ—from how they are collected and analyzed, to the advantages and disadvantages of each. We’ll also include useful examples throughout. By the end, you’ll have a clear understanding of the difference between qualitative and quantitative data, and a good idea of when to use which. Want to skip ahead to a specific section? Just use this clickable menu: Without further ado, let’s jump in. 1. What is the difference between quantitative and qualitative data?When it comes to conducting research and data analysis, you’ll work with two types of data: quantitative and qualitative. Each requires different collection and analysis methods, so it’s important to understand the difference between the two. What is quantitative data?Quantitative data refers to any information that can be quantified. If it can be counted or measured, and given a numerical value, it’s quantitative data. Quantitative data can tell you “how many,” “how much,” or “how often”—for example, how many people attended last week’s webinar? How much revenue did the company make in 2019? How often does a certain customer group use online banking? To analyze and make sense of quantitative data, you’ll conduct statistical analyses. Learn more: What is quantitative data? A complete introduction What is qualitative data?Unlike quantitative data, qualitative data cannot be measured or counted. It’s descriptive, expressed in terms of language rather than numerical values. Researchers will often turn to qualitative data to answer “Why?” or “How?” questions. For example, if your quantitative data tells you that a certain website visitor abandoned their shopping cart three times in one week, you’d probably want to investigate why—and this might involve collecting some form of qualitative data from the user. Perhaps you want to know how a user feels about a particular product; again, qualitative data can provide such insights. In this case, you’re not just looking at numbers; you’re asking the user to tell you, using language, why they did something or how they feel. Qualitative data also refers to the words or labels used to describe certain characteristics or traits—for example, describing the sky as blue or labeling a particular ice cream flavor as vanilla. What are the main differences between quantitative and qualitative data?The main differences between quantitative and qualitative data lie in what they tell us, how they are collected, and how they are analyzed. Let’s summarize the key differences before exploring each aspect in more detail:
The difference between quantitative and qualitative data: An exampleTo illustrate the difference between quantitative and qualitative data, let’s use an example. Imagine you want to describe your best friend. What kind of data might you gather or use to paint a vivid picture? First, you might describe their physical attributes, such as their height, their hair style and color, what size feet they have, and how much they weigh. Then you might describe some of their most prominent personality traits. On top of that, you could describe how many siblings and pets they have, where they live, and how often they go swimming (their favorite hobby). All of that data will fall into either the quantitative or qualitative categories, as follows: Quantitative data:
Qualitative data:
Of course, when working as a researcher or data analyst, you’ll be handling much more complex data than the examples we’ve given. However, our “best friend” example has hopefully made it easier for you to distinguish between quantitative and qualitative data. 2. Different types of quantitative and qualitative dataWhen 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 data is either discrete or continuous:
What are some real-world examples of quantitative data?Some everyday examples of quantitative data include:
Types of qualitative data (with examples)Qualitative data may be classified as nominal or ordinal:
Nominal and ordinal data tends to come up within the context of conducting questionnaires and surveys. However, qualitative data is not just limited to labels and categories; it also includes unstructured data such as what people say in an interview, what they write in a product review, or what they post on social media. What are some real-world examples of qualitative data?Some examples of qualitative data include:
3. How are quantitative and qualitative data collected?One of the key differences between quantitative and qualitative data is in how they are collected or generated. How is quantitative data generated?Quantitative data is generated by measuring or counting certain entities, or by performing calculations. Some common quantitative data collection methods include:
How is qualitative data generated?Qualitative data is gathered through interviews, surveys, and observations. Let’s take a look at these methods in more detail:
4. Quantitative vs qualitative data: methods of analysisAnother major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes. Quantitative data analysisHow you analyze your quantitative data depends on the kind of data you’ve gathered and the insights you want to uncover. Statistical analysis can be used to identify trends in the data, to establish if there’s any kind of relationship between a set of variables (e.g. does social media spend correlate with sales), to calculate probability in order to accurately predict future outcomes, to understand how the data is distributed—and much, much more. Some of the most popular methods used by data analysts include: You’ll find a detailed explanation of these methods in our guide to the most useful data analysis techniques. Qualitative data analysisWith qualitative data analysis, the focus is on making sense of unstructured data (such as large bodies of text). Given that qualitative data cannot be measured objectively, it is open to subjective interpretation and therefore requires a different approach to analysis. The main method of analysis used with qualitative data is a technique known as thematic analysis. Essentially, the data is coded in order to identify recurring keywords or topics, and then, based on these codes, grouped into meaningful themes. Another type of analysis is sentiment analysis, which seeks to classify and interpret the emotions conveyed within textual data. This allows businesses to gauge how customers feel about various aspects of the brand, product, or service, and how common these sentiments are across the entire customer base. Traditionally, qualitative data analysis has had something of a bad reputation for being extremely time-consuming. However, nowadays the process can be largely automated, and there are plenty of tools and software out there to help you make sense of your qualitative data. To learn more about qualitative analysis and what you can do with it, check out this round-up of the most useful qualitative analysis tools on the market. 5. What are the advantages and disadvantages of quantitative vs qualitative data?Each type of data comes with advantages and disadvantages, and it’s important to bear these in mind when conducting any kind of research or sourcing data for analysis. We’ll outline the main advantages and disadvantages of each now. What are the advantages and disadvantages of quantitative data?A big advantage of quantitative data is that it’s relatively quick and easy to collect, meaning you can work with large samples. At the same time, quantitative data is objective; it’s less susceptible to bias than qualitative data, which makes it easier to draw reliable and generalizable conclusions. The main disadvantage of quantitative data is that it can lack depth and context. The numbers don’t always tell you the full story; for example, you might see that you lost 70% of your newsletter subscribers in one week, but without further investigation, you won’t know why. What are the advantages and disadvantages of qualitative data?Where quantitative data falls short, qualitative data shines. The biggest advantage of qualitative data is that it offers rich, in-depth insights and allows you to explore the context surrounding a given topic. Through qualitative data, you can really gauge how people feel and why they take certain actions—crucial if you’re running any kind of organization and want to understand how your target audience operates. However, qualitative data can be harder and more time-consuming to collect, so you may find yourself working with smaller samples. Because of its subjective nature, qualitative data is also open to interpretation, so it’s important to be aware of bias when conducting qualitative analysis. 6. When should I use qualitative or quantitative data?Put simply, whether you use qualitative or quantitative data (or a combination of both!) depends on the data analytics project you’re undertaking. Here, we’ll discuss which projects are better suited to which data. Generally, you can use the following criteria to determine whether to go with qualitative data, quantitative data, or a mixed methods approach to collecting data for your project.
You may find that more often than not, both types of data are used in projects, in order to gain a clear overall image—integrating both the numbers side and human side of things. 7. Key takeawaysThroughout this post, we’ve defined quantitative and qualitative data and explained how they differ. What it really boils down to, in very simple terms, is that quantitative data is countable or measurable, relating to numbers, while qualitative data is descriptive, relating to language. Understanding the difference between quantitative and qualitative data is one of the very first steps towards becoming a data expert. If you’re considering a career in data, you’ll find links to some useful articles at the end of this post. Had enough theory and want some action? Check out our list of free data analytics courses for beginners, or cut to the chase and simply sign up for a free, five-day introductory data analytics short course. |