GuidesResearch methodsQualitative data examples to ground your understanding

Qualitative data examples to ground your understanding

Last updated

14 February 2024


Dovetail Editorial Team

When it comes to research and data analysis, every decision is rooted in metrics. You trust the data to inform your processes, strategies, and outcomes. There's no gray area.

Or is there?

While numbers paint a clear picture, qualitative data adds depth and nuance, revealing a spectrum of perspectives that numbers alone can't capture. To truly harness the power of qualitative research, you must navigate this 'gray area' with precision, understanding not only what qualitative data is but also how it can unlock invaluable insights that shape strategies and outcomes in ways metrics alone cannot.

In this article, we will unravel the potential of qualitative data by delving into what it entails, effective collection methods, and its advantages and disadvantages.

Read on to explore the dynamics of qualitative research and data, with examples to ground your understanding and application.

What is qualitative data?

This type of data represents concepts, beliefs, and information not represented by numbers alone. They are insights gathered from people, usually in interviews and focus groups. 

Qualitative data can come from anywhere, including maps, photos, observations, diaries, and lab notes. However, it typically represents preferences, opinions, and beliefs from a subjective perspective, not rooted in numbers as you'd see with quantitative datasets.

Characteristics of qualitative data include:

  • Subjective: influenced by opinions, preferences, beliefs, and feelings

  • Open-ended: without limits or boundaries 

  • Descriptive: describing something in a non-judgmental manner

  • Detailed: shared accounts with details and sentiments

  • Non-numerical: not relating to numbers or currency

Importance of qualitative data

During your research endeavors, you'll collect data to help you understand the "what" and "how." Quantitative data, rooted in numbers, can help you.

Qualitative data will help you understand the "why," shedding light on the reasons and context behind actions. It will reveal why and how something occurred, pointing out behavioral or preference-based factors. These elements are essential to any research or project, especially those related to business and decision-making. Qualitative data can be extremely powerful in transforming your processes and methodologies.

Here's an example. An eCommerce store owner can see the quantitative data in sales reports that show which products are top sellers. But to understand why more customers bought a particular product, qualitative research is needed. Surveys or pop-up questions asking for product feedback can help the store owner learn the motivating factors behind the purchase. That qualitative data (the "why") helps the owner to make informed decisions about how to make other products more appealing.

Advantages of qualitative data

Researchers and teams turn to qualitative data for many reasons. It captures data you can't otherwise gather with quantitative research.

Key advantages of qualitative data include:

  • Explores behaviors beyond the numbers

  • Allows for in-depth attitude and preference analysis

  • Provides data-collection flexibility with interviews and focus groups, rather than predefined and structured variables

  • Offers a holistic understanding of unique projects and research

  • Encourages theory development and assessment

  • Provides affirmation and credibility

  • Appeals to exploratory research endeavors

  • Allows for observation in real-world scenarios

Disadvantages of qualitative data

As beneficial as qualitative data is for some projects, there are a few disadvantages to consider. Recognize the limitations of qualitative data so you can properly manage expectations and parameters.

The disadvantages of qualitative data include:

  • Sample sizes of groups or individuals can be an issue

  • Possible bias in the sample selection

  • Impartiality and data accuracy can be a challenge

  • Qualitative research is often more time-consuming

  • It's also hard to replicate datasets

  • There is potential for researcher bias

  • Qualitative data can be difficult to measure

  • Outliers can be over-emphasized

  • It lacks the structure commonly found in quantitative data

Types of qualitative data

Qualitative data typically falls into three categories:

  • Binary: organized into two categories, usually yes/no or true/false

  • Nominal: various data by category, with no meaningful association, like choosing colors or favorite movies

  • Ordinal: categories with a meaningful order but lacking a consistent interval between the categories, e.g. customer satisfaction ratings or levels of education

These data types can be collected through various research methods, including:

  • Case studies: researching a business application outcome

  • Focus groups: gathering insights from a test group of people

  • Observation: collecting data as an observer within an environment

  • Ethnography: studying people, cultures, and traditions

  • Narratives: evaluating people's stories and experiences

  • Interviews: seeking individual feedback and opinions

Methods of qualitative data analysis

There are five techniques to consider as you decide which research projects and business applications will benefit from qualitative data analysis. Based on your research objectives, explore which of these research techniques could be most effective:

  • Content analysis: examines the presence of subjects, words, and concepts

  • Narrative analysis: interprets stories, testimonials, and interviews

  • Thematic analysis: identifies, categorizes, and interprets data based on themes and patterns

  • Discourse analysis: studies the underlying meaning of qualitative data, including observations and context

  • Grounded theory analysis: uses real-world data to develop theories

Examples of qualitative data

Explore these real-life examples of qualitative data resources and methods. Discover which might apply best to your projects and business model so you can learn more about the "why" and "how" of key experiences and processes.


Imagine your company has recently undergone significant structural changes, shifting employee responsibilities, or direction changes with a core offering. Research will determine if these changes are beneficial and will improve productivity and boost the company culture. As part of that research, you could gather qualitative data from employee interviews. 

These interviews seek to understand how employees perceive and experience the company changes. The qualitative data you could draw from their interview responses includes:

  • Common patterns related to challenges or shared experiences

  • Quotes or narratives that highlight employee perspectives

  • Emotional responses to the company change

Field notes 

If you're studying the dynamics of a particular community as part of an ethnographic project, qualitative data in field notes can be insightful. Whether you're studying interactions, cultural practices, or community events, the field notes are your primary method of data collection. 

Field notes can be used as part of your qualitative data analysis to uncover:

  • Observations from the field that highlight key aspects

  • Participation rates of community members

  • Interactions that support community identity

Textual data 

Some of the most common methods for collecting qualitative data are open-ended surveys. Including in-person paper surveys and anonymous or digital questionnaires, surveys are pivotal in how today's businesses and researchers learn about their industries and subjects.

Using open-ended questions, you can collect opinions, beliefs, and sentiments in the participants' own words.

These textual data responses are essential for:

  • Sentiment analysis

  • Contextual understanding

  • Identifying patterns and themes

Visual data 

Visual data in qualitative analysis can include photos and videos as the data collection method. For researchers who study the environment, for example, the visual data collected from field studies is pivotal. These visual perspectives can help researchers document changes, curate mapping, and spot challenges when comparing today's visuals to previous ones. 

When analyzing visual datasets, you can learn a host of details, including:

  • Symbolic interpretation

  • Spatial relationships

  • Visual patterns and themes

Audio data

Any qualitative survey data collected over the phone would be an example of audio data. Researchers studying the experiences and perspectives of people with certain medical conditions might use these types of data collection methods. For instance, a researcher might record an interview with a participant, asking them to describe emotional or physical conditions.

Audio data can be great for analyzing more than just a participant's response. It can be used for:

  • Transcribing responses for reporting

  • Analyzing emotional tones and non-verbal cues

  • Narrative analysis of a person's complete journey

How to analyze qualitative data 

Once you’ve decided which quantitative data methods align best with your project or research goals, you'll need to collect and analyze the findings. To help make the most of your qualitative data responses, follow these five steps for in-depth analysis success.

Keep in mind that qualitative data analysis is an iterative process, requiring more flexibility than with numeric, quantitative data.

Step 1: Arrange your data

Gather your transcriptions, documents, notes, and interview responses. Sift through to separate the valid from the invalid, and arrange your data according to your demographics or pre-determined participant categories.

Step 2: Organize all your data

Mark the sources of your data and organize the notes and responses according to your research or project parameters. Sort the "yes" responses from the "no" responses.

Spend time reading (and rereading) the data to gain an in-depth understanding, keeping notes that may help you with the next step.

Step 3: Apply codes to the data collected

Create codes to guide the official categorization process. Make notes in the margins and use concept mapping and other approaches to help you code the various elements of your findings. Coding, or sorting themes and patterns, will help you evaluate the results in a more organized way.

Step 4: Validate your qualitative data

Using your codes, identify any underlying themes, opinions, language, or beliefs. Continued review of your codes may require some revisions but ultimately will help you funnel the data into themes and official categories of results. Researchers often leverage constant comparisons between new data and codes and existing ones. 

Step 5: Concluding the analysis process

Use your coded categories and themes to draw data-driven conclusions. You can then present your findings, along with the study's purpose and parameters, to key stakeholders.

The qualitative data analysis should tell a cohesive story that addresses pre-study questions and provides answers. Software solutions can help you develop final presentable findings.

Trust qualitative data to help your decision-making

Start tapping into the power of qualitative data to help you reach your research, business, and project goals. Knowing how to collect, analyze, and interpret these insights can be ground-breaking for your teams.

Having a deeper understanding of what qualitative data and research can offer will allow you to apply precision to your data-driven decision-making. And the insights gleaned from these datasets can revolutionize how you make those critical decisions, setting you up for success.


What are examples of good qualitative research questions?

These questions are great examples of open-ended qualitative research queries:

  • How would you describe your recent online experience?

  • Describe a time you experienced discomfort.

  • What areas of improvement would you suggest?

How can you avoid bias in qualitative research?

Several strategies can help you avoid introducing bias to your qualitative research project. These include:

  • Diversity in participant selection

  • Audit trails of decision-making

  • Triangulation of findings

  • Peer briefing before research

  • Reflexivity in acknowledging your biases and preconceptions

What are some quick-reference examples of qualitative data in action?

From software startups and scientific applications to backyard restaurant management and human resources oversight, there's a reason to explore qualitative research practically everywhere.

Here are a few more examples of qualitative data at work:

  • Location, origin, and gender collected for a census

  • Name, position, and event experience of a conference-goer for follow-up

  • Weight, height, and body types for a clothing size chart

  • User feedback about a newly launched software solution

  • Customer-experience survey responses to help improve a company's customer service policy

What are the core differences between qualitative and quantitative data?

While both quantitative and qualitative data provide value for research, there are primary differences between the two.

  • Quantitative data is fixed, countable, and related to numbers

  • Qualitative data is individualized, descriptive, and subjective

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