Qualitative Data: Definition, Types, and Examples for Understanding User Behavior

Key takeaways

• Qualitative data is non-numerical information, such as words, observations, and descriptions, that helps researchers understand meaning, opinions, and human behavior.

• Qualitative data complements quantitative data by explaining why people make decisions rather than simply measuring what happened.

• You can use quantitative data to tell you how many customers bought a product, but looking at qualitative data can tell you why they made that choice.

Qualitative data is typically divided into three categories: binary, nominal, and ordinal. This data is usually collected through means such as interviews, focus groups, observation, case studies, and longitudinal studies. Once collected, you analyze it through a multi-step process, in which you label and group data to identify themes and patterns. If you're ready to start building your data analysis skills, consider enrolling in the IBM Data Analytics with Excel and R Professional Certificate. In just three months, you'll have the opportunity to build entry-level skills like data wrangling, data mining, and data analysis using professional tools. Upon completion, you’ll earn a shareable certificate for your resume.

What is qualitative data?

Qualitative data is information captured from contextually complex sources, such as focus groups, surveys, personal diaries, photographs, interviews, observations, or even articles. Collecting qualitative data allows you to find meaning in human experiences, perspectives, and emotions. Many qualitative research projects are open-ended, and you may begin a project only to discover new questions and perspectives from your preliminary analysis. 

Key characteristics of qualitative data

Unlike quantitative data, qualitative data is non-numerical, and its defining characteristics shape how you collect and interpret it. Understanding these traits can help you determine when qualitative data is the right fit for your research question:

• Open-ended: Qualitative studies may involve follow-up visits with subjects or adjustments to your research questions to better understand individual motivations, such as why a customer stopped using a product.

• Descriptive: Qualitative data uses neutral language to describe a behavior, image, or subject. For example, you may note that a participant paused before answering instead of saying they "seemed hesitant."

• Interpretive: You can interpret the data in different ways, leading to a diverse range of subjective perspectives in the research. Two researchers may draw different conclusions from the same interview transcript.

• Non-numerical: Qualitative data does not have a numerical value, so you can only use descriptive statistics like frequency to describe certain behaviors. 

What is the difference between qualitative and quantitative data?

Qualitative data is non-numerical information, such as words and observations, used to uncover meaning and patterns of human behavior, while quantitative data is numerical information used to measure and count. If you were a market researcher tracking how many customers purchased a product each month, you're working with quantitative data. If you then conducted interviews to understand why those customers made their purchase decision, you have qualitative data. 

What are the types of qualitative data?

While qualitative data is descriptive, you still need to classify it to analyze it. Whether you choose an ordinal or nominal classification depends on the order of your data: 

Ordinal data

Ordinal qualitative data has a natural order in its representation, without containing a numeric value; for example, if you had attendees rate an event experience from very bad, bad, okay, good, to very good, or if you ranked runners' placement in a race from first, second, and third.

Nominal data

Nominal qualitative data is a categorical attribute that lacks order or numeric value. These are descriptors; for example, eye color, preferred operating system, or nationality. In a survey of IT professionals' preferred operating systems, this question would include Windows, macOS, and Linux as answer choices. You can assess the frequency of choices among participants, but the data themselves (their preferred operating systems) are not numeric.

Binary data

You may also see binary data listed as a discrete qualitative data type; however, binary data is nominal data with two possible values, such as "yes or no" or "negative or positive."

Why is qualitative data important?

Qualitative data goes beyond what happened or what choice was made to explore why that happened or why that choice was made. Its aim is to find nuances in human behavior, such as emotion, opinion, and description, that quantitative data do not capture. You use qualitative data to uncover the meaning underlying it rather than to measure an outcome.

In a research project, your choice to include qualitative data will depend on your research question. Often, you'll need qualitative data analysis if your question involves a "why," since answering that requires more nuance than quantitative reasoning. You may also want to use qualitative data when you need more context, or you're working with social influences, subjective meanings, or stigma in a population. 

How do you collect qualitative data?

Some common qualitative data collection methods include interviewing, focus groups, observation, case studies, and longitudinal studies. Since qualitative data can come from many different sources, you can use various methods to facilitate the open-ended data collection process in qualitative research. The methods you use should ensure you obtain a rich profile of experience, opinion, and meaning relevant to your research question.

Interviews

Interviews allow you to collect qualitative data from primary sources or first-hand accounts. In qualitative research, you typically use an individual in-depth interview (IDI) or a key informant interview (KII). IDIs typically focus on a personal perspective, while KIIs focus on a subject-matter expert or someone with community knowledge.

Interviews can vary from structured to informal, though many take the form of a semi-structured interview that creates a conversational back-and-forth with some questions prepared beforehand. You can work on follow-ups as you receive additional responses and begin analyzing your data.

Focus groups

Focus groups, sometimes referred to as group key informant interviews, take the form of a semi-structured interview with a group that shares specific demographics, such as age or similar purchasing behavior. A trained moderator guides the group through the conversation. The group setting promotes open discussion, allowing you to gain insight into community experiences while ensuring participants remain engaged.

While the group dynamics of a focus group can give deep insight into a problem or research question, they can limit more personal perspectives, as some individuals may not be comfortable sharing certain experiences with a group. Some qualitative studies will work in IDIs to overcome this limitation. 

Observation

In an observation, you study subjects in their environment, making notes throughout the period on how they behave. It is also common to take audio and video recordings for later study. By using direct observation techniques, you can see how your subject behaves in situations without needing an interview. The goal is to watch and take field notes without interfering in the participant's activities.

Case studies

A case study combines interviews and observations into an in-depth examination of a person, group, organization, or community. You’ll want to use a case study when you need data from many places, such as interviews, observations, and document reviews, to put together the full picture of an event or person. The goal is to understand how a phenomenon works from every facet you can put together. 

Longitudinal studies

A longitudinal study follows a group or individual over an extended period to see how they're affected. This is a common practice for medical researchers studying how a pharmaceutical works in a cohort of patients.

Records analysis

In a records analysis, you study existing databases, books, or reports to uncover patterns in those documents. You may use a library or data-mining tools to collect the information you want to study and explore how and why a particular subject acted as they did. A common example of record analysis is historical analysis. For example, if you wanted to study past printmaking trends, you'd need to examine archival documents related to early books and printing presses.

Read more: What Are Common Data Collection Methods?

How many participants do I need for qualitative research?

The number of participants you need for qualitative research will depend on your research question, as the open-ended nature of qualitative research may demand flexibility in your data collection. Research from a 2025 analysis of sample size in qualitative research suggests these sample sizes for a few common qualitative data collection methods [1]:

• Thematic Analysis: 12–20 participants

• Focus Groups: 4–12 focus groups

• Case Study: 4–10 cases or single-case in-depth analysis

What is an example of qualitative data usage?

An example of using qualitative data would be to find out customers' perceptions of your company's brand. This goes beyond sales statistics to really uncover what customers think about your company. To do this, you could collect data through written surveys, focus groups, or data-mining approaches, such as sentiment analysis, to uncover opinions about your brand online. A customer response like "I trust this brand because it always feels personal" is qualitative data that captures emotion and meaning that a sales figure cannot.

How do you analyze qualitative data?

Since qualitative data is non-numerical, the methods for analyzing it differ from those used in conventional statistical data analysis. The following are common steps within the qualitative data analysis process [2]:

1. Data collection

You start by collecting data related to your research question, or by forming a research question based on the data you collect.

2. Coding

When coding, you can assign a deductive code frame based on your research question. If you're researching price, quality, and service, you may use those terms as codes. Sometimes you might notice the theme of trust emerging unexpectedly from the data, known as an inductive code frame. Many times, you may start with a deductive analysis and create inductive codes as needed. Common coding methods include:

• Descriptive: Summarizing the fundamental sentiment of a passage

• In vivo: Using the participant's words verbatim to summarize

• Process: Coding actions, like labeling a moment where a participant "Argues with a suggestion" during a focus group

• Thematic: Defining patterns in the language, such as noticing multiple participants use words related to "feeling unheard" across different interviews

3. Analysis

In conjunction with coding, you perform the data analysis by grouping passages by theme, finding patterns, and determining relationships. You may choose to focus your analysis on the data's actual content by exploring discourse or uncovering themes and meaning within it.

4. Reporting and sharing

You can report and share your findings using three different methods [3]:

• Narrative: You directly include quotes from your research to make an argument. For example, you could directly quote customers in a report on how they feel about your brand.

• Thematic: You generate your report structured around the themes you've uncovered in your research. For example, you structure your stakeholder branding report based on themes from customer sentiment. 

• Visualization: You use visualizations like word clouds, concept maps, and bar graphs to show frequency. For example, you utilize graphics like word clouds to show the frequency of particular words customers used to describe your brand. 

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