Characteristics of Qualitative Research

Qualitative research is performed by businesses that acknowledge the human condition and want to learn more about it. Some of the key characteristics of qualitative research that enable practitioners to perform qualitative inquiry comprise small data, the absence of definitive truth, the importance of context, the researcher’s skills and are of interests.

Absence of a definitive truth

One of the most obvious characteristics of qualitative research is the lack of definitive truth.

Unlike quantitative data, which is clinical, logical, and deals in absolutes, qualitative data is collected by researchers who simply want to know more about the subject at hand.

Note that qualitative data collection does not occur in a vacuum and is context-dependent.

Data are the result of various situational factors that vary from one person to the next, and it is for this reason that qualitative researchers tend to worry about whether the data are reasonably probable as opposed to factual.

This plausibility can be increased by ensuring the data collection process is as accurate as practicable.

Importance of context

Further to the point above is the importance of context. Since qualitative research is performed to better understand real-world problems, the research must consider the natural contexts in which individuals exist.

Context depends on the individual and their social, cultural, or historical background or experience.

In this way, qualitative research provides an accurate account of how people feel, what forces shape their lives, and other less tangible factors that quantitative data may fail to capture or explain.

Understanding what test subjects think and feel can make the solutions more empathic, equitable, effective, and efficient. 

Researcher centrism and skills

In qualitative research, the researcher who designs the test is also the instrument by which data are collected.

Since the researcher is close to the research participants, they can understand context and meaning in detail and better interpret study outcomes.

In some cases, however, this level of intimacy can threaten the ability of the researcher to collect data that is unbiased and objective.

To avoid this scenario, researchers are encouraged to think carefully about qualitative research design.

For example, it’s important to use the funnel approach to interview development which enables the interviewer to incorporate the issues that will play a role in reaching study objectives. 

Individuals conducting the study should also focus on building rapport with their subjects, practice active listening, avoid inconsistencies, and note any contradictions.

Areas of interest and subject matter

Qualitative research is best suited to areas of interest or subject matter that may be difficult to learn more about via more structured research designs.

A qualitative line of inquiry can be used to tackle sensitive or intricate topics like sexual dysfunction or those that involved one’s personal life history. 

Alternatively, it can be used to collate meaningful information from populations that are hard to reach or underserved, such as children from certain subcultures or groups.

In terms of issues that affect both business and society, qualitative research can provide insight into more nebulous topics such as:

  • How social media use is affecting physical social engagement among teens in cities.
  • The importance of mental health education in a high-school curriculum.
  • The benefits of immunization in poor, rural areas.
  • Understanding the factors that cause food insecurity and scarcity in a given region, and
  • The importance of establishing positive client-customer relationships.

Quantitative vs. Qualitative Research

The characteristics of quantitative research contribute to methods that use statistics as the basis for making generalizations about something. These generalizations are constructed from data that is used to find patterns and averages and test causal relationships.

Quantitative data can be extremely powerful in enhancing an organization’s business processes.

However, balancing it with qualitative understanding is critical.

Indeed, the qualitative side makes the data values in the first place.

In fact, with qualitative understanding, you can clean, curate, and validate the data which goes into the quantitative model.

While on the other hand, you can also balance out your judgment of quantitative methods, as they are skewed toward what can be measured and toward short-term optimization vs. long-term thinking.

That is why it’s critical to imbue a second-order thinking mindset to balance out the adverse effects of quantitative data, build a solid validation funnel, and make the best of both quantitative and qualitative data!

Second-order thinking is a means of assessing the implications of our decisions by considering future consequences. Second-order thinking is a mental model that considers all future possibilities. It encourages individuals to think outside of the box so that they can prepare for every and any eventuality. It also discourages individuals from defaulting to the most obvious choice.

Qualitative research and understanding, therefore, are critical for long-term decision-making, which moves beyond the short-term consequences.

In that logic, to understand the difference between:

  • Frist vs. second-order effect.
  • Short-term optimizations vs. long-term innovation bets.
  • Efficiency vs. efficacy.

Thus, qualitative understanding helps first-order balance thinking with second-order thinking and effects.

It helps to balance out short-term optimizations with long-term innovation bets.

And it helps to understand when efficiency is worth undertaking vs. when instead efficacy is needed.

Qualitative research examples and methods comprise:

Qualitative vs. Quantitative Data in Entrepreneurship

Jeff Bezos is among the people that have led the Internet Revolution.

And while many today give Amazon for granted, it’s worth highlighting that many of the things, products, and business models the company has built over the years, didn’t exist before.

In other words, Amazon has led the way in building and shaping the business playbook of the Internet.

From that standpoint, the company has been an incredible “optimizer,” thus leveraging data to make business processes (from inventory management to delivery) much more efficient.

It’s also the same company that has obsessed over customers with its customer obsession approach, which is a combination of iterative loops.

But also discovery, through data and intuition, to launch and build products that people might not know they want yet.

In the Amazon Shareholders’ Letter for 2018, Jeff Bezos analyzed the Amazon business model, and it also focused on a few key lessons that Amazon as a company has learned over the years. These lessons are fundamental for any entrepreneur, of small or large organization to understand the pitfalls to avoid to run a successful company!

Another example of that is Apple’s playbook.

Indeed, while also here, Apple uses data to drive many of its short-term decisions.

The company has also made bold bets based on intuition and building something for which there is no market yet.

What we know as a Blue Ocean.

A blue ocean is a strategy where the boundaries of existing markets are redefined, and new uncontested markets are created. At its core, there is value innovation, for which uncontested markets are created, where competition is made irrelevant. And the cost-value trade-off is broken. Thus, companies following a blue ocean strategy offer much more value at a lower cost for the end customers.

Now, with a quantitative approach alone, would you be able to build a product that creates a new market or a blue ocean?

Chances are, you won’t. Why?

Well, because there is no data or reliable data to look at when it comes to new markets.

There, what matters is human intuition and the ability to have a vision that can imagine a future that does not exist yet!

In all these cases, you might want to move away from quantitative research and focus on sharpening your intuition.

Of course, quantitative data might help in assessing the development of a trend via market research.

Take the story of how Jeff Bezos, before starting Amazon, was doing market research about the Internet, only to discover its staggering pace of growth.

Thus, he made the decision to start an online business.

As he would have regretted missing it (he recalls making the decision through a regret minimization framework).

Yet, also there, he didn’t know what it would have made sense to build on top of the nascent internet, as there was no mature market yet!

So, he decided to test the market by building a bookstore, which he used as a transitional business model to test the new developing Internet while building a business that would be viable at a small scale first, then at a larger and larger scale!

That is the essence of how entrepreneurs understand when to use data, and quantitative research, when to use intuition, and when to combine both!

Qualitative research examples

Here are some examples of companies that rely on qualitative research. 


Starbucks is the most successful coffeehouse chain in the world. While its superior customer service and vertically integrated supply chain are important, Starbucks also relies on the qualitative data it collects from customers.

In 2008 the company launched, a website where customers could submit their innovative ideas, read the ideas of others, or see those that were implemented by the company. 

The site offered an open and collaborative environment where consumers could share their views and take an active part in shaping the famous Starbucks experience.

This afforded Starbucks a free and vast source of qualitative information that reflected current social, cultural, and consumer trends.

Five years later, in 2013, some 277 ideas had been implemented from a total list of 150,000. Implemented ideas included:

  • Splash sticks that are inserted into coffee cup lids to protect employee and customer clothes from spills.
  • New coffee flavors such as Mocha Coconut Frappuccino, Hazelnut Macchiato, and Pumpkin Spice. 
  • Mobile payment option for drive-thru services.


While now defunct, the Apple Customer Pulse website was launched in 2011 to collect qualitative feedback from twice-monthly customer surveys. Similar to, Apple Customer Pulse served as an online community where product users could offer input on subjects and issues concerning the company.

Today, Apple continues to collect this data but in different ways. The company emails questionnaires to customers immediately after purchase and asks questions about their overall experience.

Consumers are also asked in-store to rate their experience at the point of sale. In both cases, Apple collects quick, accurate feedback whilst still fresh in the consumer’s mind.

One metric the company has relied on for some time is NPS. For example, Apple measured customer satisfaction of the new Apple Watch to determine that it was a hit with customers just three months after launch. 

In one earnings report, CEO Tim Cook noted that the market research “measured a 97 percent customer satisfaction rate for Apple Watch and we hear from people every day about the impact it’s having on their health, their daily routines, and how they communicate.


In the mid-2000s, LEGO was in financial dire straits and hired external consultants to audit its business.

They argued that the company’s brick-based products were obsolete and that it should expand its product range to survive and compete with main rival Mattel.

LEGO products were traditionally intended for boys, and with only 9% of LEGO users female, the company wondered whether it should offer more products that appealed to young girls. 

In 2007, the company embarked on a four-year study involving 3,500 girls and their mothers.

Researchers observed the playing habits of participants and conducted focus groups and interviews to determine what would make Lego toys more interesting to girls. 

Among other things, the participants noted that they preferred more vibrant, feminine-colored bricks and instead of fire trucks and mines, they wanted construction sets for houses and bakeries.

The Lego Friends line of construction toys for girls was released in 2012 which featured eight main characters represented by detailed and realistic Lego minifigures.

Lego Friends remains one of the company’s most successful launches to this day. Upon release, it doubled the company’s sales expectations and significantly widened its customer base within the girls’ market segment.

Key takeaways

  • Qualitative research is performed by businesses that acknowledge the human condition and want to learn more about it.
  • One of the most obvious characteristics of qualitative research is the lack of definitive truth. Researchers perform this type of research to learn more about a topic that is often nebulous and hard to define.
  • Other characteristics of qualitative research include researcher centrism, researcher skills, and the importance of context in determining how participants think and feel while they interact with their environment. 

Read Next: Characteristics of Quantitative Research

Read Also: Quantitative vs. Qualitative Research.

Connected Analysis Frameworks

Cynefin Framework

The Cynefin Framework gives context to decision making and problem-solving by providing context and guiding an appropriate response. The five domains of the Cynefin Framework comprise obvious, complicated, complex, chaotic domains and disorder if a domain has not been determined at all.

SWOT Analysis

A SWOT Analysis is a framework used for evaluating the business’s Strengths, Weaknesses, Opportunities, and Threats. It can aid in identifying the problematic areas of your business so that you can maximize your opportunities. It will also alert you to the challenges your organization might face in the future.

Personal SWOT Analysis

The SWOT analysis is commonly used as a strategic planning tool in business. However, it is also well suited for personal use in addressing a specific goal or problem. A personal SWOT analysis helps individuals identify their strengths, weaknesses, opportunities, and threats.

Pareto Analysis

The Pareto Analysis is a statistical analysis used in business decision making that identifies a certain number of input factors that have the greatest impact on income. It is based on the similarly named Pareto Principle, which states that 80% of the effect of something can be attributed to just 20% of the drivers.

Failure Mode And Effects Analysis

A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage.

Blindspot Analysis

A Blindspot Analysis is a means of unearthing incorrect or outdated assumptions that can harm decision making in an organization. The term “blindspot analysis” was first coined by American economist Michael Porter. Porter argued that in business, outdated ideas or strategies had the potential to stifle modern ideas and prevent them from succeeding. Furthermore, decisions a business thought were made with care caused projects to fail because major factors had not been duly considered.

Comparable Company Analysis

A comparable company analysis is a process that enables the identification of similar organizations to be used as a comparison to understand the business and financial performance of the target company. To find comparables you can look at two key profiles: the business and financial profile. From the comparable company analysis it is possible to understand the competitive landscape of the target organization.

Cost-Benefit Analysis

A cost-benefit analysis is a process a business can use to analyze decisions according to the costs associated with making that decision. For a cost analysis to be effective it’s important to articulate the project in the simplest terms possible, identify the costs, determine the benefits of project implementation, assess the alternatives.

Agile Business Analysis

Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.

SOAR Analysis

A SOAR analysis is a technique that helps businesses at a strategic planning level to: Focus on what they are doing right. Determine which skills could be enhanced. Understand the desires and motivations of their stakeholders.

STEEPLE Analysis

The STEEPLE analysis is a variation of the STEEP analysis. Where the step analysis comprises socio-cultural, technological, economic, environmental/ecological, and political factors as the base of the analysis. The STEEPLE analysis adds other two factors such as Legal and Ethical.

Pestel Analysis

The PESTEL analysis is a framework that can help marketers assess whether macro-economic factors are affecting an organization. This is a critical step that helps organizations identify potential threats and weaknesses that can be used in other frameworks such as SWOT or to gain a broader and better understanding of the overall marketing environment.

DESTEP Analysis

A DESTEP analysis is a framework used by businesses to understand their external environment and the issues which may impact them. The DESTEP analysis is an extension of the popular PEST analysis created by Harvard Business School professor Francis J. Aguilar. The DESTEP analysis groups external factors into six categories: demographic, economic, socio-cultural, technological, ecological, and political.

Paired Comparison Analysis

A paired comparison analysis is used to rate or rank options where evaluation criteria are subjective by nature. The analysis is particularly useful when there is a lack of clear priorities or objective data to base decisions on. A paired comparison analysis evaluates a range of options by comparing them against each other.

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