What is qualitative data?

Qualitative data is descriptive, non-numerical information—the findings you collect through interviews, questionnaires, focus groups, and observation. Where quantitative data tells you how much or how often, qualitative data describes qualities: feelings, behaviors, and motivations.

It plays a growing role in science, business, and product research because it explains the “why” behind the numbers. This guide covers what qualitative data is, how it works, and where to begin collecting and analyzing it.

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What is qualitative data?

When working with qualitative data, you’re collecting and reviewing information that characterizes and approximates rather than measures precisely. You source this data through observation, including interviews, surveys, and focus groups.

With responses and observations in hand, you can categorize your findings in terms of feelings, attributes, or properties.

Qualitative vs. quantitative data

It’s important to understand the distinction between qualitative and quantitative datasets. While qualitative data captures feelings, behaviors, and properties, quantitative data measures values expressed as numbers.

Quantitative data answers questions like how much or how often. Qualitative data answers questions like what type or why. Because qualitative data evaluates deeper sentiments, it’s best at illustrating the thought processes and behaviors behind the raw quantitative data.

Importance of qualitative data

Qualitative data helps unravel characteristics and behavioral motivations. It allows researchers and managers to “qualify” the environment or ecosystem they’re studying and dig deeper into people’s emotional or perceptual motivations.

In business, these findings provide insights about target audiences and customer decision-making preferences. They’re great for solving problems and paving the way for innovation.

Properly collecting and analyzing qualitative data gives you the insights you need to prioritize your focus, address challenges, and resolve issues.

Types of qualitative data

Qualitative research is an exploratory process, involving an in-depth look at behaviors and concepts. There are various types of qualitative data to explore.

How you collect qualitative data, and the channels you use, will help you organize your results. Most data fits into one of three categories: nominal, binary, and ordinal. But every initiative should answer a primary research question.

Most qualitative data methods aim to:

• Gain behavioral insights
• Understand reasoning
• Explore motivations
• Identify emotional connections

Qualitative data examples

Examples make it easier to see how these collection and analysis methods apply in practice. Here are a few simplified samples from business and research.

Measuring characteristics

If you’re studying a group of women, you might collect qualitative data about their characteristics—the various hair colors or current jobs of the women in your study group, for example. These qualitative attributes are great for developing marketing personas in business applications.

Measuring behaviors

Imagine you’re studying a group of children in a room full of different toys. Measuring qualitative data might include observing which toys the children choose to play with first. Studying these behaviors helps you understand what attracts a child to a particular type of toy, which is useful for product innovation initiatives.

Measuring motivations

Consumers make purchasing decisions according to personal motivations. Qualitative surveys can help you study particular groups to evaluate why those consumers decided to buy. Knowing whether your target audience is more motivated by price point, free shipping, or customer service will help you change how you engage them.

Qualitative data collection methods

When you’re ready to collect qualitative data, there are several methods to consider. Surveys and focus groups are great tools, but they’re not the only ones. Each method suits specific kinds of findings and research goals.

One-on-one interviews

One of the most common data collection methods, interviews provide a personal approach to determining sentiments and behaviors. Interviews with open-ended questions net the most in-depth responses and results.

Focus groups

Usually limited to ten or fewer participants, this method assigns a moderator to initiate a group discussion of ideas and sentiments. Members may all have something in common, but their individual responses contribute to your qualitative datasets.

Case studies

Case studies are ideal for collecting specific, in-depth data. They combine multiple qualitative data sources to build contextual knowledge about a real-world phenomenon.

Longitudinal studies

In a longitudinal study, you collect data from the same participants repeatedly. It’s an observational model for comparing a group’s results over days or even decades.

Record keeping

With this method, you use existing sources of information to inspire new research. Much like visiting a library, you study reference material to discover new qualitative data worth investigating.

Observation

Observation involves a researcher immersing themselves in a setting to watch and document participants. Documentation might take the form of note-taking, video observation, photography, or audio recording.

What is qualitative data analysis?

Once you’ve collected qualitative data, it’s time to evaluate and analyze it to produce inferences and actionable applications. There are no hard and fast rules for interpreting the data, but there are two primary approaches to understanding the qualitative data you’ve assembled.

Deductive approach

If you outlined a structure as part of your data collection process, you’re using a deductive approach. You use this analysis method when you already have an idea about the responses in the dataset. It’s often easier to execute since much of the groundwork is established during data collection.

Inductive approach

The inductive approach means analyzing qualitative data with no preconceived idea of what the responses, results, or research phenomenon will be. It’s more time-consuming, but it’s also where researchers find unexpected anomalies and breakthrough findings.

How do you begin analyzing qualitative data?

To start analyzing your qualitative data, loosely follow these steps:

1. Arrange your data into systems or categories in a software platform or analysis tool that lets you easily visualize what you’ve collected.
2. Organize your qualitative data according to your research objectives using tables, spreadsheets, or visually appealing graphics.
3. Assign codes to your data to compress vast libraries of information. Coding is essentially categorization taken a step further—assigning properties and patterns that help you draw conclusions later.
4. Validate your qualitative data to identify viable samples and eliminate flawed or misconstrued datasets. Verify the accuracy of your collection methods and confirm the reliability of the data you’ve collected.
5. Conclude with a systematic presentation in a condensed report. Outline the methods you used, the researchers involved, and the approach. Share the positives, negatives, and limitations of your study, then draw inferences and offer suggestions for action or future research.

Sharing qualitative analysis

When you’re ready to share your findings, you can choose from a variety of formats to suit your audience, including:

• Digital or physical reports
• Images or infographics
• Audio or visual materials
• Scanned historical documents
• Observation dictations
• Field notes

When explaining your analysis, share the purpose and parameters of your study first, then offer the immediate results. Your analysis process will allow you to draw conclusions, apply judgment, and determine next steps based on your unique scenario.

Advantages of qualitative data

There are clear advantages to applying qualitative data collection and analysis to business and research projects:

1. Get in-depth data beyond the numbers.
2. Understand participant or consumer behaviors more intuitively.
3. Discover rich data you can use and reuse well into the future.

Disadvantages of qualitative data

A few disadvantages are worth noting before adopting these approaches:

1. Proper qualitative data collection and analysis is time-consuming.
2. The data can be hard to generalize, especially with fewer participants.
3. The quality of the results depends directly on the researcher’s analysis skills.

Qualitative analysis tools

As complex as qualitative data analysis can be, you don’t have to go it alone or reinvent the wheel. Many tools and resources simplify each step of the research process.

Look for software that streamlines how you collect data, like online survey tools or customer questionnaires, and data management software designed to help you categorize and organize what you gather.

Dedicated qualitative analysis software earns its keep at the analysis stage, when you’re looking for patterns and conclusions. With the right tools in place, you can put qualitative data to work for your business or research project—and reference this guide as you develop your studies and choose your methods.