The it couple: qualitative & quantitative data

June 4, 2024

When it comes to how people study and report on data  - the very thing we do at CommonAlly - there is an actual art and science to how collection, analysis, and reporting work.  We asked our data analyst, Olivia Gladu, to give us the download on the difference between qualitative and quantitative data.

Stargazing your way into data insights

Between qualitative and quantitative, the type of data each represents is quite different for two words that sound so similar. An easy way to think of “Qualitative data” (or “qual”) stems from the word “quality.”  Whereas “quantitative data" (or, you guessed it, “quant”) stems from the word quantity or quantify aka numbers.

Here’s another way to think about it: quantitative data is like counting stars in the sky, while qualitative data is understanding the constellations they make up.

Quantitative data provides precise counts and measurements, much like counting individual stars. On the other hand, qualitative data gives context and meaning to these numbers, providing patterns and relationships akin to recognizing constellations formed by those stars.

Why quantitative data matters and how it lacks essential context

As information restricted to numerical values, quantitative data is most beneficial for general statistical analysis. It includes objective and observable information in specified units and is best suited for understanding a large swath of a population.

Quantitative data includes quantifiable figures like:

  • height
  • age ranges
  • temperature
  • income
  • sales figures
  • population size
  • test scores
  • weights

It’s empirical data that does not require opinion. The benefit of quantitative data is that it provides specific, precise results that can be easily communicated using numbers in a common language. 

The limitations of just looking at numbers are that they often lack context and can be difficult to apply in a broader conversation. For instance, let’s say a report came out citing that “City X experienced a 50% rise in crime over the past year.” As a result, you might board up windows, lock all your doors, and search for a new neighborhood. Look a bit deeper, though, one might learn that the number of crimes increased from a total of 10 to 15, the population of City X increased by 20,000 residents, the type of crimes committed were non-violent, and the national crime rate average was up 70% from the previous year.

Without context, data can easily create false narratives.

Reducing research to a series of numbers (albeit important numbers) loses critical context and narrative and drastically shifts the perception of what that 50% actually represents. Without context, the data in this example could easily create the false narrative of a crime wave or unsafe living environment.

How qualitative data provides essential context

Qualitative data is information that cannot be counted, measured, or easily expressed using numbers. Yet, it can help explain the “why” behind the “why” and add context and deeper understanding to quantitative data and is well suited to track:

  • trends
  • gather opinions
  • formulate theories, and
  • understand the experiences of a particular demographic, audience, or location.

A qualitative report might read that “Despite City X crime up 50% from last year, residents feel safer than ever”. It could be determined that this is because of proactive safety measures the city implemented, the surveyed group has a low rate of fear about non-violent crimes, etc. It is the collection of shared experiences or opinions that are gathered and then grouped based on similarities—even if numbers are used to help rate a particular experience. 

Qualitative data is often collected via open-ended survey responses, scale ratings, interview summaries, or multiple-choice questions and can present in a variety of formats such as video testimonials, journal-style entries or any other type of conversational method (our faves!).

The downside of this type of analysis is that it’s time-consuming and vulnerable to bias due to its relatively subjective nature. Sample sizes tend to be smaller than quantitative research.

Qualitative and quantitative data are a powerful combo

Good research includes both qualitative and quantitative data. 

While both together are not always necessary - and dependent on research objectives - the more comprehensive approach includes quantitative and qualitative data. Together, they pack a powerful 1-2 punch that provides a more thorough understanding of trends, impacts, and outcomes and predicts future potential behaviors or consequences.  

Applying both data types to real case studies

Objective

Gauge the general population’s interest in protecting trans rights in the U.S.

How the data gets used

Acquire quantitative data to make targeted generalizations to help assess how much a political campaign should focus on this topic. For example, this study found that 80% of adults surveyed who were Democrat or lean Democrat, were in favor of or strongly favored laws or policies in place or being considered to protect transgender people from discrimination in jobs, housing, and public spaces. 48% of adults surveyed who were Republican or lean Republican shared the same stance. 

Objective

Explore the multifaceted impact of chronic wounds on patients' lives, including emotional, social, and economic challenges.

How the data gets used

Acquiring qualitative data allows you to not just learn how many people are facing emotional, social, or economic challenges due to chronic wounds but also learn about the actual challenges themselves. What are the challenges? Why are these considered challenges? What do patients need to solve these challenges?

Put into practice, here’s an example from research conducted by CommonAlly.

Of 780 chronic wound patients surveyed, the economic impact was a significant factor in decreased quality of life due to chronic wound ramifications. Nearly a quarter of respondents experienced a decrease in household income contribution, with 17% reporting changes in employment status attributed to their wound condition.

These deeper insights into the experiences of wound patients can then be used to inform treatment options and interactions with healthcare providers. Read the full case study here.

Aligning your market research objectives with the appropriate data type is important. When you get it right, robust narratives can be formed leading to a better understanding of your audience. Data doesn’t have to be black and white or binary. There is value in the gray area and power in the nuance.

Olivia Gladu
wrote this,
we didn't tell them to.
They
are typically doing important
is typically doing important
Data Analyst
things.

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