A comprehensive guide to qualitative data, how it differs from quantitative data, and why it's a valuable tool for solving problems.
Table of ContentsEverything that’s done digitally—from surfing the web to conducting a transaction—creates a data trail. And data analysts are constantly exploring and examining that trail, trying to find out ways to use data to make better decisions.
Different types of data define more and more of our interactions online—one of the most common and well-known being qualitative data or data that can be expressed in descriptions and feelings.
This guide takes a deep look at what qualitative data is, what it can be used for, how it’s collected, and how it’s important to you.
Key takeaways:
Qualitative data is descriptive information that captures observable qualities and characteristics not quantifiable by numbers. It is collected from interviews, focus groups, observations, and documents offering insights into experiences, perceptions, and behaviors.
Qualitative data analysis cannot be counted or measured because it describes the data. It refers to the words or labels used to describe certain characteristics or traits.
This type of data answers the "why" or "how" behind the analysis. It’s often used to conduct open-ended studies, allowing those partaking to show their true feelings and actions without direction.
Think of qualitative data as the type of data you’d get if you were to ask someone why they did something—what was their reasoning?
Qualitative research not only helps to collect data, it also gives the researcher a chance to understand the trends and meanings of natural actions.
This type of data research focuses on the qualities of users—the actions behind the numbers. Qualitative research is the descriptive and subjective research that helps bring context to quantitative data.
It’s flexible and iterative. For example:
Qualitative data is important in determining the frequency of traits or characteristics.
Understanding your data can help you understand your customers, users, or visitors better. And, when you understand your audience better, you can make them happier. First-party data, which is collected directly from your own audience, is especially valuable as it provides the most accurate and relevant insights for your specific needs.
Qualitative data helps the market researcher answer questions like what issues or problems they are facing, what motivates them, and what improvements can be made.
You’ve most likely used qualitative data today. This type of data is found in your everyday work and in statistics all over the web. Here are some examples of qualitative data in descriptions, research, work, and statistics.
Analysis of qualitative data requires descriptive context in order to support its theories and hypothesis. Here are some core examples of descriptive qualitative data:
Qualitative data research methods allow analysts to use contextual information to create theories and models. These open- and closed-ended questions can be helpful to understand the reasoning behind motivations, frustrations, and actions—in any type of case.
Professionals in various industries use qualitative observations in their work and research. Examples of this type of data in the workforce include:
Qualitative data can provide important statistics about any industry, any group of users, and any products. Here are some examples of qualitative data set collections in statistics:
Qualitative and quantitative data are much different, but bring equal value to any data analysis. When it comes to understanding data research, there are different analysis methods, collection types and uses.
Both provide a ton of value for any data collection and are key to truly understanding trending use cases and patterns in behavior. Dig deeper into quantitative data examples.
The characteristics of qualitative data are vast. There are a few traits that stand out amongst other data that should be understood for successful data analysis.
With qualitative data samples, these traits can help you understand the meaning behind the equation—or for lack of a better term, what’s behind the results.
As we narrow down the importance of qualitative data, you should understand that there are different data types. Data analysts often categorize qualitative data into three types:
Binary data is numerically represented by a combination of zeros and ones. Binary data is the only category of data that can be directly understood and executed by a computer.
Data analysts use binary data to create statistical models that predict how often the study subject is likely to be positive or negative, up or down, right or wrong—based on a zero scale.
Nominal data, also referred to as “named, labeled data” or “nominal scaled data,” is any type of data used to label something without giving it a numerical value.
Data analysts use nominal data to determine statistically significant differences between sets of qualitative data.
For example, a multiple-choice test to profile participants’ skills in a study.
Ordinal data is qualitative data categorized in a particular order or on a ranging scale. When researchers use ordinal data, the order of the qualitative information matters more than the difference between each category. Data analysts might use ordinal data when creating charts, while researchers might use it to classify groups, such as age, gender, or class.
For example, a Net Promoter Score (NPS) survey has results that are on a 0-10 satisfaction scale.
One of the important things to learn about qualitative data is when to use it.
Qualitative data is used when you need to determine the particular trends of traits or characteristics or to form parameters for larger data sets to be observed. Qualitative data provides the means by which analysts can quantify the world around them.
You would use qualitative data to help answer questions like who your customers are, what issues or problems they’re facing, and where they need to focus their attention, so you can better solve those issues.
Qualitative data is widely used to understand language consumers speak—so apply it where necessary.
Qualitative data is a detailed, deep understanding of a topic through observing and interviewing a sample of people. There are both benefits and drawbacks to this type of data.
Here are the main approaches and collection methods of qualitative studies and data:
Personal interviews are one of the most commonly used deductive data collection methods for qualitative research, because of its personal approach.
The interview may be informal and unstructured and is often conversational in nature. The interviewer or the researcher collects data directly from the interviewee one-to-one. Mostly the open-ended questions are asked spontaneously, with the interviewer allowing the flow of the interview to dictate the questions and answers.
The point of the interview is to obtain how the interviewee feels about the subject.
Focus groups are held in a discussion-style setting with 6 to 10 people. The moderator is assigned to monitor and dictate the discussion based on focus questions.
Depending on the qualitative data that is needed, the members of the group may have something in common. For example, a researcher conducting a study on dog sled runners understands dogs, sleds, and snow and would have sufficient knowledge of the subject matter.
Data doesn’t start with your collection, it has most likely been obtained in the past.
Using already existing reliable data and similar sources of information as the data source is a surefire way to obtain qualitative research. Much like going to a library, you can review books and other reference material to collect relevant data that can be used in the research.
For example, if you were to study the trends of dictionaries, you would want to know the past history of every dictionary made, starting with the very first one.
Observation is a longstanding qualitative data collection method, where the researcher simply observes behaviors in a participant's natural setting. They keep a keen eye on the participants and take down transcript notes to find out innate responses and reactions without prompting.
Typically observation is an inductive approach, which is used when a researcher has very little or no idea of the research phenomenon.
Other documentation methods, such as video recordings, audio recordings, and photo imagery, may be used to obtain qualitative data.
Case studies are an intensive analysis of an individual person or community with a stress on developmental factors in relation to the environment.
In this method, data is gathered by an in-depth analysis and is used to understand both simple and complex subjects. The goal of a case study is to see how using a product or service has positively impacted the subject, showcasing a solution to a problem or the like.
A longitudinal study is where people who share a single characteristic are studied over a period of time.
This data collection method is performed on the same subject repeatedly over an extended period. It is an observational research method that goes on for a few years and, in some cases, decades. The goal is to find correlations of subjects with common traits.
For example, medical researchers conduct longitudinal studies to ascertain the effects of a drug or the symptoms related.
And, as with anything—you aren’t able to be successful without the right tools. Here are a few qualitative data analysis tools to have in your toolbox:
Yes, categorical data or qualitative data is information that cannot generally be proven. For instance, the statement “the chair is too small” depends on what it is used for and by whom it is being used.
If you’re interested in the following, you should use qualitative data:
1. Transcribe your data: Once you’ve collected all the data, you need to transcribe it. The first step in analyzing your data is arranging it systematically. Arranging data means converting all the data into a text format.
2. Organize your data: Go back to your research objectives and organize the data based on the questions asked. Arrange your research objective in a table, so it appears visually clear. Avoid working with unorganized data, there will be no conclusive results obtained.
3. Categorize and assign the data: The coding process of qualitative data means categorizing and assigning variables, properties, and patterns. Coding is an important step in qualitative data analysis, as you can derive theories from relevant research findings. You can then begin to gain in-depth insight into the data that help make informed decisions.
4. Validate your data: Data validation is a recurring step that should be followed throughout the research process. There are two sides to validating data: the accuracy and reliability of your research methods, which is the extent to which the methods produce accurate data consistently.
5. Conclude the data analysis: Present your data in a report that shares the method used to conduct the research studies, the outcomes, and the projected hypothesis of your findings in any related areas.
One is not better than the other, rather they work cohesively to create a better overall data analysis experience. Understanding the importance of both qualitative and quantitative data is going to produce the best possible data content analysis outcome for any study.
