What is qualitative data?

Let's talk data. There is a growing need and broader adoption of qualitative data analysis in science, today's business environments, and across innovative initiatives. Qualitative data describes findings beyond clear-cut numbers from research across various methods and types. 

If you're wondering how qualitative data can help your business or innovative research efforts, keep reading. This is the ultimate guide to understanding what it is, how it works, and where to begin in collecting and analyzing data.

For those who want to leverage the power of qualitative data, it's essential to understand the basic definitions first. 

When working with qualitative data, you're collecting and reviewing metrics that characterize and approximate. You source this data through observation, including interviews, surveys, and focus groups.

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

You've likely heard of quantitative data. It's equally important to understand the distinctions between quantitative and qualitative datasets. While qualitative data measures feelings, behaviors, and properties, quantitative data are measures of values expressed as numbers.

Quantitative data answers questions like how much or how often. Qualitative data will answer questions like what type or why. Because qualitative data evaluate deeper sentiments, they're best at illustrating thought processes and behaviors behind the raw quantitative data.

Qualitative data plays a more pivotal role in research and business today, especially because it helps unravel the characteristics and behavioral motivations. This data allows researchers and managers to "qualify" the environment or ecosystem they're studying. These analytics allow you to dive deeper into understanding people’s emotional or perceptual motivations. 

These metrics provide insights about target audiences and customer decision-making preferences in business. These datasets are great for solving problems and paving the way for innovations. 

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

Since qualitative data is more of an exploratory process, it involves a more in-depth look at behaviors and concepts. And 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 pillar categories: Nominal, binary, and ordinal. But all initiatives should answer a primary research question.

Most qualitative data methods aim to:

• Gain behavioral insights

• Understand reasoning

• Explore motivations

• Identify emotional connections

It may be helpful to see qualitative data examples to better understand how to apply these collection and analysis methods to your model. Here are a few simplified samples to illustrate the value of these metrics in business and research.

Measuring characteristics

If you're studying a group of women, you might dive deeper to measure their characteristics. For example, you can collect qualitative data about the various hair colors or current jobs of the women in your study group. These qualitative metrics 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 those children choose to play with first. Studying these behaviors will help you better understand the behavior behind what attracts a child to a particular type of toy, which is great for product innovation initiatives.

Measuring motivations

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

When you're ready to explore collecting and analyzing qualitative data, there are several collection methods to consider. Surveys and focus groups are great tools, but there are other methods, too. Each method is uniquely beneficial to specific metrics and research goals.

One-on-one interviews

One of the most common data collection methods, this qualitative research effort provides a more 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 responses will contribute to your qualitative datasets.

Case studies

These methods are ideal for collecting specific in-depth data using a combination of qualitative data sources to gain contextual knowledge about a specific real-world phenomenon. 

Longitudinal studies

This research approach is where you collect data from the same participants repeatedly. It's an observational model for dynamically comparing a captured group's results over days or even decades.

Record keeping

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

Observation

This method involves a researcher immersing into a setting to watch and take notes of participants. Documentation might be in the form of note-taking, video observation, photography, or audio recording. 

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

Deductive approach

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

Inductive approach

This method involves analyzing qualitative data when you have no idea what the responses, results, or research phenomenon will be. It's more time-consuming to study data this way, but it can also be where researchers find revolutionary anomalies and lightbulb, a-ha solutions.

To help you begin analyzing your recently collected qualitative data, you can loosely follow these basic steps to initiate your analysis.

1. Arrange your data into systems or categories into a software platform or analysis tool that allows you to easily visualize what you've collected.

2. Organize your qualitative data according to your research objectives using tables, spreadsheets, or visually appealing graphics.

3. Assigning proper codes for your data will help you compress vast libraries of information. Coding really translates to categories but takes it a step further by assigning properties and patterns. Codes will help you draw conclusions later.

4. Start validating your qualitative data to identify viable collection samples and eliminate any flawed or misconstrued datasets. Verify the accuracy of the collection methods and confirm the reliability and accuracy of the data you’ve collected.

5. Conclude your data with systematic presentation in a condensed report. In this step, you'll outline the methods you used in collection, the researchers involved, and the approach. You'll share the positives, negatives, and limitations of your study. Here, you draw inferences about your findings and offer suggestions for action or future research.

When you're ready to share your qualitative analysis findings, you can choose a variety of formats to suit your presentation audience. These might include:

• Digital or physical reports

• Images or infographics

• Audio or visual materials

• Scanned historical documents

• Observation dictations

• Field notes

When explaining your analysis, it's best to share the purpose and parameters of your study. Then you can offer immediate results. Your qualitative analysis process will allow you to draw conclusions, apply judgment, and determine the next steps based on your unique scenario. 

There are inherent advantages to applying qualitative data collection and analysis methods to business and research projects. These are the three most pivotal benefits of tapping into regular qualitative data initiatives ongoing:

1. Get in-depth data beyond the numbers.

2. Understand participants or consumer behaviors more intuitively.

3. Discover rich data you can use and reuse well into the future.

A few disadvantages of qualitative data practices are worth noting. Before adopting these approaches, consider these potential setbacks:

1. Proper qualitative data collection and analysis is time-consuming.

2. The data can be hard to generalize, especially with fewer participants.

3. The researcher’s data analysis skills and the results are directly correlated.

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

Explore software solutions that streamline how you collect data, like online survey tools or customer questionnaires. Discover the design and data management software solutions out there intended to help you categorize and organize your data. 

Software packages for qualitative data analysis are your best friend when entering the analysis stage and looking for patterns and conclusions.

Start leveraging the many benefits of qualitative data analysis for your business or research project. Remember to reference this guide as you develop your studies and determine your methods.