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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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What is qualitative data? How to understand, collect, and analyze it

A comprehensive guide to qualitative data, how it differs from quantitative data, and why it's a valuable tool for solving problems.

What is qualitative research?

Importance of qualitative data.

• Differences between qualitative and quantitative data

Characteristics of qualitative data

Types of qualitative data.

• Pros and cons
• Collection methods
• Qualitative data: Examples and how to use it
• Qualitative vs. quantitative data in research: what's the difference?
• Quantitative data examples to help you understand how to take action
• What is behavioral data & why is it important?

Everything 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 gives insights into people's thoughts and feelings through detailed descriptions from interviews, observations, and visual materials.

The three main types of qualitative data are binary, nominal, and ordinal.

There are many different types of qualitative data, like data in research, work, and statistics. 

Both qualitative and quantitative research are conducted through surveys and interviews, among other methods. 

What is qualitative data?

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: 

The music had a light tone that filled the kitchen.

Every blue button had white lettering, while the red buttons had yellow. 

The little girl had red hair with a white hat.

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. 

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.

Examples of qualitative data

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. 

Qualitative data in descriptions

Analysis of qualitative data requires descriptive context in order to support its theories and hypothesis. Here are some core examples of descriptive qualitative data:

The extremely short woman has curly hair and brilliant blue eyes.

A bright white light pierced the small dark space. 

The plump fish jumped out of crystal-clear waters. 

The fluffy brown dog jumped over the tall white fence. 

A soft cloud floated by an otherwise bright blue sky.

Qualitative data in research

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. 

Some examples of qualitative data collection in research:

What country do you work in? 

What is your most recent job title? 

How do you rank in the search engines? 

How do you rate your purchase: good, bad, or exceptional?

Qualitative data at work

Professionals in various industries use qualitative observations in their work and research. Examples of this type of data in the workforce include:

A manager gives an employee constructive criticism on their skills. "Your efforts are solid and you understand the product knowledge well, just have patience."

A judge shares the verdict with the courtroom. "The man was found not guilty and is free to go."

A sales associate collects feedback from customers. "The customer said the check-out button did not work.”

A teacher gives feedback to their student. "I gave you an A on this project because of your dedication and commitment to the cause."

A digital marketer watches a session replay to get an understand of how users use their platform.

Qualitative data in statistics

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:

The age, weight, and height of a group of body types to determine clothing size charts. 

The origin, gender, and location for a census reading.

The name, title, and profession of people attending a conference to aid in follow-up emails.

Difference between qualitative and quantitative data

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. 

Here are the differences between qualitative and quantitative data :

Qualitative data is individualized, descriptive, and relating to emotions.

Quantitative data is countable, measurable and relating to numbers.

Qualitative data helps us understand why, or how something occurred behind certain behaviors .

Quantitative data helps us understand how many, how much, or how often something occurred. 

Qualitative data is subjective and personalized.

Quantitative data is fixed and ubiquitous.

Qualitative research methods are conducted through observations or in-depth interviews.

Quantitative research methods are conducted through surveys and factual measuring. 

Qualitative data is analyzed by grouping the data into classifications and topics. 

Quantitative data is analyzed using statistical analysis.

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. 

Descriptive : describing or classifying in an objective and nonjudgmental way.

Detailed : to give an account in words with full particulars.

Open-ended : having no determined limit or boundary.

Non-numerical : not containing numbers. 

Subjective : based on or influenced by personal feelings, tastes, or opinions.

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:

1. Binary data

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.

2. Nominal data

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.

3. Ordinal data

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. 

When should you use qualitative research?

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. 

Pros and cons of qualitative data

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. 

Pros of qualitative data

Qualitative research is affordable and requires a small sample size.

Qualitative data provides a predictive element and provides specific insight into development.

Qualitative research focuses on the details of personal choice and uses these individual choices as workable data.

Qualitative research works to remove bias from its collected information by using an open-ended response process.

Qualitative data research provides useful content in any thematic analysis.

Cons of qualitative data 

Qualitative data can be time-consuming to collect and can be difficult to scale out to a larger population.

Qualitative research creates subjective information points.

Qualitative research can involve significant levels of repetition and is often difficult to replicate.

Qualitative research relies on the knowledge of the researchers.

Qualitative research does not offer statistical analysis, for that, you have to turn to quantitative data.

Qualitative data collection methods

Here are the main approaches and collection methods of qualitative studies and data: 

1. Interviews

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. 

2. Focus groups

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.

3. Data records 

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. 

4. Observation

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.

Further reading: Site observations through heatmaps

5. Case studies

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. 

6. Longitudinal studies

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.

Qualitative data analysis tools

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: 

MAXQDA —A qualitative and mixed-method data analysis software 

Fullstory —A behavioral data and analysis platform

ATLAS.ti —A powerful qualitative data tool that offers AI-based functions 

Quirkos —Qualitative data analysis software for the simple learner

Dedoose —A project management and analysis tool for collaboration and teamwork

Taguette —A free, open-source, data analysis and organization platform 

MonkeyLearn —AI-powered, qualitative text analysis, and visualization tool 

Qualtrics —Experience management software

Frequently asked questions about qualitative data

Is qualitative data subjective.

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.

Who uses qualitative data?

If you’re interested in the following, you should use qualitative data:

Understand emotional connections to your brand

Identify obstacles in any funnel, for example with session replay

Uncover confusion about your messaging

Locate product feature gaps 

Improve usability of your website, app, or experience

Observe how people talk, think, and feel about your brand

Learn how an organization selects vendors and partners

What are the steps for 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.

Is qualitative data better than quantitative data?

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. 

Further reading : Qualitative vs. quantitative data — what's the difference?

Learn how to analyze qualitative data. We show examples of how to collect, organize, and analyze qualitative data to gain insights.

Here's how you can quantitatively analyze your qualitative digital experience data to unlock an entirely new workflow.

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

A comprehensive guide to product analysis and analytics platforms, how important they are, and why they’re a valuable asset for your bottom line.

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?

What is qualitative data?

Advantages and disadvantages of qualitative data, qualitative and quantitative data, qualitative data collection, ethical considerations for qualitative data.

• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods

Focus groups

• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

When you think of the word "data," you might think about numbers and tables and organized spreadsheets. Qualitative data, on the other hand, can take on so many forms and serve so many purposes that it's important to examine the topic in greater detail.

Qualitative research methods collect unstructured or unorganized data that is often difficult to define statistically or numerically. There are many uses for collecting and analyzing qualitative data, such as understanding social phenomena, gathering people's opinions on various subjects, and building evidence for recommendations. Ultimately, researchers will need to organize and categorize qualitative data in order to perform qualitative data analysis .

Why collect qualitative data?

Qualitative and quantitative data are almost always juxtaposed against each other. Data generated from quantitative research lends itself to statistical analysis, while qualitative data contextualizes a concept or phenomenon by describing its constituent elements.

For example, consider the difference between comparing the average temperatures of two different cities and comparing the innate beauty of those two cities. The former can be quantified so that researchers can reach a quick conclusion about the differences in the climate. On the other hand, the latter is rather difficult to reduce to numbers. Even if beauty can be placed on a ten-point scale, what does "7 points" or "4 points" on the beauty scale mean? How does someone determine such a score? Understanding the beauty of a particular city requires collecting qualitative data.

Where is qualitative data used?

Many different kinds of researchers, such as anthropologists, professionals engaged in health services research, and market researchers, collect and analyze qualitative data . While the method of collecting data in each area may differ, fields that commonly utilize qualitative data have research questions that an analysis of numerical data cannot easily answer.

Researchers often perceive a divide between qualitative data and quantitative data and get into debates about which form of data is "better." The more important task is to collect relevant data for your research. Let's consider the pros and cons of qualitative data.

Qualitative data helps offer in-depth analysis and a more nuanced understanding of phenomena than quantitative data can provide. For example, statistics can tell us the average test scores for each school whose students took a standardized test. Comparisons of average scores can give us information about which schools are more successful or are struggling. Further statistical analysis can indicate a correlation between school funding and test performance.

However, these statistics are less likely to point out the causes leading to these test results. A qualitative study gathers data that can supplement those test scores with further context, such as teachers' instructional practices, students' opinions about learning activities, and funding for educational resources.

An analysis of qualitative data can allow researchers to draw relationships between ideas. This is accomplished by "coding" the data for ideas. Coding qualitative data involves looking at your data and applying short, descriptive labels called codes to segments of text, images, audio, or video for later analysis. With systematic coding, you can turn your raw data into an organized, meaningful data set from which you can draw insightful conclusions.

Suppose the study above involves interviewing students about their test performance and teachers. A researcher can code all of the instances where students describe their teacher as "nice," "helpful," or "strict." Qualitative data analysis software like ATLAS.ti helps researchers with the coding process so that themes emerging from the data become easier to understand.

Researchers can then conduct qualitative data analysis by determining whether the well-performing or struggling students have a specific set of keywords that describe their teachers' personalities. If so, the researcher can propose a connection between a teacher's personality characteristics and their students' test scores.

Disadvantages

The main disadvantage of using qualitative data is that the analysis can be complex and time-consuming. On the other hand, quantitative data is relatively straightforward to collect and analyze. Because qualitative data cannot easily be reduced to numbers or statistics, researchers need to reorganize the data in more structured and meaningful ways for analysis. A qualitative researcher often has to read their data line by line to determine what information to code and how.

Another concern that critics of qualitative research point to is researchers' potential biases and subjectivities when analyzing qualitative data. The reorganization and analysis of the data must be presented clearly and transparently so that research audiences can easily understand the analysis and assess the credibility of the subsequent conclusions themselves.

The research objectives you want to pursue will dictate how you should collect data and what data you should collect.

Quantitative data

Let's say you are conducting an experimental study to determine the effectiveness of a nutritional supplement in helping people lose weight. In this case, you will likely collect quantitative data such as weight, caloric intake, and time spent exercising. Quantitative data like these can be analyzed statistically to help you understand if research participants are losing weight because of the supplement.

Qualitative data

On the other hand, you may also want to gather opinions on whether people are satisfied with the supplement. Qualitative data collection methods such as interviews or focus groups might ask research participants what they think the supplement tastes like, how they feel after taking it, and why they believe it is effective or not effective. Answers to these questions don't provide easy numbers or simple statistics.

Still, these insights are just as important to product researchers because even if the supplement is effective, people may choose not to buy it if it leads to unpleasant experiences. Qualitative data is valuable to researchers when they need to know more about an unfamiliar phenomenon and when understanding the phenomenon requires more complexity than a simple yes/no binary or a numerical scale can provide. Instead, a thematic analysis of qualitative data on the subject might explore the emotions (e.g., happy, frustrated) associated with each particular taste (e.g., sweet, sour, bitter).

Mixed methods research

You may want to consider a mixed methods approach to research and thus combine quantitative and qualitative data collection methods. Researchers can best understand a complex problem by collecting various types of data collected on the subject. In the example above, the successful launch of a nutritional supplement depends on its effectiveness and customer satisfaction. One is only particularly helpful if the other is also present. Ultimately, it is essential to consider whether you are collecting the right kinds of data for the research inquiry you want to pursue.

In-depth data analysis is right at your fingertips with ATLAS.ti!

Start your free trial today to see how we can turn your data into analytical insights.

A researcher can employ various qualitative methods to collect qualitative data . As a result, numerous types of qualitative data can be used for data analysis .

Questionnaires

Questionnaires or surveys are among the easiest methods for collecting large-scale qualitative and quantitative data. In addition to capturing quantitative data for statistical analysis, questionnaires can also be used to collect open-ended answers from respondents.

For example, researchers can ask respondents to rate their satisfaction with a particular product on a scale of 1 to 5 and then write down their reasons for their ratings. Qualitative data analysis can reveal sentiments about a product among respondents who are very satisfied with it and compare sentiments among unsatisfied respondents.

Qualitative data from in-depth interviews often involve transcripts and audio or video recordings . Transcription converts interviews into text that can be read and cited in documents and presentations when you want the audience to see what research respondents have said.

Recordings are also valuable as they allow researchers to see respondents' facial expressions and gestures or hear their non-verbal utterances. This qualitative data analysis helps researchers better understand how respondents feel (e.g., excited, upset, confused) during interviews.

Focus groups are similar to interviews except that multiple respondents talk simultaneously with the interviewer. Similarly to interviews , qualitative data from focus groups can be analyzed from transcripts or multimedia recordings. The recordings can have significant value for qualitative research because they can capture how focus group respondents interact or collaborate.

Observations

An observational research method can conduct data collection on a particular social phenomenon in a less controlled environment than where interviews or focus groups would be conducted. Collecting such naturalistic qualitative data in the field can help researchers who want to see the social world outside of a confined experiment. Researchers can collect various forms of data, such as audio or video recordings, the observers' field notes , and photographs. The type of research you want to conduct will help you determine which data collection methods to employ.

For example, if you are at a train station, you may want to record audio of train station announcements or record field notes about how easy or difficult it may be to navigate the station. Additionally, taking pictures or videos while walking around the train station may be valuable to later analyze what you see.

Document analysis

Any textual data , such as medical records, journal articles, and website pages, can be analyzed qualitatively. Collecting documents is useful to researchers looking to conduct a comparative analysis, thematic review, or user research. Researchers can analyze documents for their text, images, or other features depending on the inquiry they want to conduct.

Social media analysis

Content from Twitter, Instagram, and other similar platforms can provide abundant opportunities for qualitative analysis. ATLAS.ti allows researchers to import tweets directly into their project as well as comments from any social media post , such as Instagram, TikTok, Facebook, and so on. Researchers can easily search and incorporate any tweets or comments as qualitative data instantly.

Researchers should always be careful with collecting and handling qualitative data, especially if it contains personal information or needs to be obtained with consent. People's perspectives are simplified and aggregated into numbers when quantitative analysis is pursued, but qualitative data collection often preserves the words, circumstances, and behaviors of people, and participants may feel uncomfortable with how such data might be used. An important consideration is how the researcher should present the data to their audiences while not revealing any clues about participants' identities.

Medical records, for example, are especially sensitive as people can connect names to health conditions that patients may prefer to keep secret. In observations , people may not want their pictures taken if they don't want to be associated with being in a particular place. Respondents in interviews and focus groups may choose to withdraw from research after having said something about which they feel embarrassed or uncomfortable.

Before collecting any data, researchers should obtain informed consent from participants to ensure that participants understand their rights and how their privacy is protected. This might be a challenge in observations, especially when they occur outside a controlled environment. When collecting data in the field, researchers might consider avoiding taking pictures or videos of people's faces, recognizable clothing, or possessions. As a result, field notes might be the most appropriate form of data to collect while observing in the field.

Even if research participants give informed consent, there is always the possibility they might say something sensitive or provide information that they later wish they hadn’t. Researchers should always take precautions when collecting data from research participants to ensure that potentially damaging information isn't disseminated in research presentations or academic publications.

By thoughtfully engaging with rigorous data collection methods , researchers can collect rich data that provides novel insights and offers participants a chance to meaningfully express themselves.

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Qualitative data has no numerical values, unlike quantitative data, and instead captures information from sources such as open-ended survey responses, interview summaries, and video transcripts, to name just a few examples. This data is beneficial in social sciences and market research sectors since it allows for more in-depth knowledge of human experiences and customer sentiment. Enterprise data programs should incorporate both qualitative data and quantitative data for a more thorough and holistic picture of the information they want to understand.

Table of Contents

Importance of Qualitative Data

Qualitative data is an effective instrument for better understanding human experiences, actions, and the subtle dynamics that define our environment. Embracing qualitative data improves the thoroughness of research, informs decision-making processes, and helps to provide a more complex and insightful assessment of a business—for example, customer sentiment and brand satisfaction.

Characteristics of Qualitative Data

Qualitative data doesn’t depend on numbers, but on human experiences that reveal the answers to the questions that start with “why” and “how” behind numbers and statistics.

Non-Numerical Data

Qualitative data is differentiated by its non-numerical nature, with information represented in words, pictures, or other non-numerical formats. Unlike quantitative data , which is concerned with observable quantities, qualitative data encapsulates the essence of experiences and views.

In an interview on work satisfaction, for example, comments may include descriptive terms such as “fulfilling,” “challenging,” or “supportive” rather than numerical scores.

In-Depth Responses

Qualitative data can include a wider range of information than quantitative data by capturing human emotion or sentiment—for example, from detailed responses to a survey question, a consumer satisfaction hotline, or a focus group. Such information allows researchers to study the breadth of experiences and viewpoints.

This method invites individuals to describe their ideas, feelings, and motives in depth. For example, in a focus group examining consumer preferences for a new product, participants may offer personal anecdotes, preferences, and worries, providing more in-depth knowledge than numerical evaluations.

Harder to Organize

The diversity and open-ended nature of qualitative data can make it difficult to arrange. Researchers frequently encounter the challenge of organizing material without established classifications. Coding and thematic analysis are used to uncover patterns and themes in the data.

Qualitative Data Types

Researchers frequently use qualitative approaches to acquire a better understanding of complex events, capturing the complexity and context of the issue under investigation. It may be roughly divided into two sorts, nominal data, and ordinal data, each of which provides a different purpose in the analytical process.

Nominal Data

Nominal data includes categories or labels that lack any specific order or ranking. It divides objects into separate groups, and all data points inside each category are treated equally. Nominal data contains information on the various types or characteristics of phenomena. Examples include:

• Gender: Male, Female, Other
• Hair Color: Brown, Black, Blonde, Red, Other
• Type of Housing: House, Apartment, Trailer, Other

Ordinal Data

Ordinal data refers to categories that have a meaningful order but no consistent or observable distinction between them. It depicts a hierarchy in which objects can be rated or arranged according to their magnitude. Examples include:

• Business Ranking: 1st, 10th, 13th
• Likert Scale Rating: Strongly disagree, disagree, neutral, agree, strongly agree
• Time of Day: Dawn, morning, noon, afternoon, evening, night
• Political Orientation: Left, center, right

When To Use Qualitative Data

Qualitative data is useful for gaining a nuanced, in-depth knowledge of qualities, patterns, and contextual variables. It enhances quantitative procedures by offering a rich and holistic viewpoint, making it a necessary component of complete research methodology. It can be used in a wide range of applications—here are some key examples:

• To determine the frequency of traits or characteristics— Use methods such as interviews or observations to determine trait frequency, exposing variances and subtleties.
• To form parameters for datasets— Define important categories through qualitative research, creating the groundwork for developing quantitative investigations.
• To identify patterns— Use qualitative data to find patterns, themes, and trends within a certain setting and identify underlying patterns and relationships that would not be obvious using quantitative approaches alone.
• To explore context and depth— Qualitative data can capture complexities, motives, and cultural influences, resulting in a more thorough study.

Advantages of Qualitative Data

Qualitative research offers a complex and exploratory approach to understanding human experiences, attitudes, and actions, allowing researchers to get valuable insights beyond quantitative data.

In-Depth Attitudes and Behaviors

Qualitative data allows researchers to investigate attitudes and behaviors in-depth, resulting in a thorough knowledge of the context, motives, and underlying variables that impact participants. This depth is typically difficult to reach using quantitative approaches.

Explains What Numbers Can’t

Qualitative data, by its definition, is non-numeric. It dives into the complexity of human experiences, motivations, and behaviors to provide a more nuanced view. It assists in determining the context and meanings of observed patterns, answering “why” and “how” questions, and developing theories in fields like sociology, psychology, anthropology, and market research.

More Flexible Approach

Qualitative research approaches provide greater flexibility in study design and data collection, letting researchers adjust methods in real-time and resulting in a more responsive and dynamic investigation of the research issue. This versatility is particularly useful when dealing with new or unexpected parts of the research.

Disadvantages of Qualitative Data

Qualitative research has some limitations and challenges to be aware of, especially when compared to quantitative data .

Limited Sample Sizes

Qualitative studies frequently use smaller sample sizes than quantitative research. While this enables for more in-depth examination, the findings may be difficult to generalize to larger groups, reducing the research’s external validity.

Potential Bias in Sample Selection

The subjective nature of qualitative data collecting might lead to bias in sample selection. Researchers may accidentally select volunteers who reflect their preconceived views or preferences, resulting in a distorted portrayal of the target population.

Question Accuracy

Effective and fair interview questions or prompts are essential in qualitative research. Poorly phrased questions might result in misinterpretations or restricted insights. Researchers need to dedicate time and skill to designing questions that are in line with the study’s aims and do not mistakenly steer participants’ responses.

7 Qualitative Data Collection Methods

Qualitative data collecting methods gather non-numerical information using a wide range of methods depending on the topic, the nature of the events being examined, and the resources available. Here are some of the most common:

• Interviews— One of the most commonly used methods; can be structured, semi-structured, or unstructured.
• Focus Groups— Allow researchers to investigate different viewpoints and interactions among participants on the specified topic.
• Observations— Capture real-time behavior and context to gain a firsthand understanding of the subject without depending on participant self-reports; can reveal subtleties other approaches may miss.
• Open-Ended Surveys and Questionnaires— Let respondents freely express themselves outside the bounds of a face-to-face interview. The best approach is to identify whether the answers are positive, neutral, or negative.
• Case Studies— The holistic approach lets researchers look at different aspects of an individual or group to create a thorough and context-rich grasp of an issue. Case studies are ideal for delving deeply into difficult and unusual circumstances, providing insights that may be used to inform more general ideas.
• Text Analysis— This involves the methodical analysis of written or textual material, such as documents, literature, or web content. Can show patterns, themes, and discourses in written material, offering useful information on language usage and communication.
• Audio and Video Recordings— Audio and video recordings allow researchers to capture verbal, tone, and context clues. These recordings enhance qualitative data by capturing the intricacies of human interaction and behavior.

7 Qualitative Data Examples

These qualitative data examples demonstrate the variety of forms qualitative data can take, emphasizing its richness and ability to capture the complexities of human experiences and behaviors, allowing researchers to gain a deeper understanding and contextual interpretation that quantitative data alone may not provide:

Frequently Asked Questions (FAQs)

What is the key difference between qualitative and quantitative data.

Qualitative data is non-numerical and descriptive, emphasizing traits and attributes, whereas quantitative data is numerical and quantifiable, allowing for statistical analysis.

What is another term for qualitative data?

Categorical data is another word for qualitative data that emphasizes the separation of things into discrete categories based on unique qualities.

What is data coding in qualitative research?

Codes or labels are used for data segments, such as text or images, to categorize and organize information. It assists researchers in identifying patterns, themes, and concepts, as well as in organizing, retrieving, and interpreting qualitative data, all of which contribute to a better understanding of the topic under investigation.

Bottom Line: Qualitative Data is Essential

Qualitative data in research and analysis allows for a more in-depth knowledge of human experiences, behaviors, and societal phenomena. It supplements quantitative data by providing depth, context, and insights that are frequently impossible to obtain using numerical measurements alone.

Qualitative data is important for investigating complicated research issues, developing hypotheses, and acquiring a comprehensive view on a variety of topics. Whether used in social sciences, market research, or other sectors, qualitative data is critical for developing knowledge and guiding relevant decision-making processes.

If you’re learning about different types of data, read about the differences between structured and unstructured data .

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods, and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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Qualitative data are data representing information and concepts that are not represented by numbers. They are often gathered from interviews and focus groups, personal diaries and lab notebooks, maps, photographs, and other printed materials or observations. Qualitative data are distinguished from quantitative data , which focus primarily on data that can be represented with numbers. 

Qualitative data can be analyzed in multiple ways. One common method is data coding, which refers to the process of transforming the raw collected data into a set of meaningful categories that describe essential concepts of the data. Qualitative data and methods may be used more frequently in humanities or social science research and may be collected in descriptive studies.

Examples of qualitative data are the transcript of an interview and data collected in free text fields in a survey. 

There are many tools available for qualitative data analysis, depending on the data type. Some popular tools include:

• NVIVO: https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home/  
• Dedoose: https://www.dedoose.com/
• Taguette: https://www.taguette.org/

Further Resources

Deakin University Library created a great video explaining the difference between qualitative and quantitative research and data:

https://www.youtube.com/watch?v=4iws9XCyTEk

This guide provides a full look at qualitative data, including how and why it’s collected and used:

https://www.fullstory.com/qualitative-data/  

Send us your feedback or suggestions for new terms

Qualitative Data: Definition and Ways to Collect It (+Use Cases)

What is qualitative data, types of qualitative data, use cases of qualitative data, pros and cons of qualitative data, how to collect qualitative data, how to analyze qualitative data, qualitative vs quantitative data: which is better, start collecting qualitative data right now, fullsession pricing plans, faqs in relation to qualitative data.

Knowing everything about customers sometimes goes beyond numbers. That’s the point of qualitative data—it captures life’s rich narratives beyond mere numbers.

Dive into this read to get savvy about how these vibrant details shape research in ways spreadsheets never could.

In this article, we’ll also show you some real-world examples where this data collection method simply shines. Stay tuned to learn more!

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Qualitative data works with non-numerical information collected through methods such as interviews, observations, and open-ended questions . It understands the subject matter deeply and supports a deductive approach.

Think of it as the vibrant paint on an artist’s palette, ready to tell a story that numbers alone can’t.

Unlike its quantitative counterpart, which loves to count and measure, qualitative data thrives on capturing the full spectrum of human experiences—those rich textures and shades that bring depth to our understanding.

Why Qualitative Data Is Important?

Qualitative data helps market researchers find much more detailed feedback from their customers. Sure, knowing how many people click a button on your website gives you something measurable. But it’s through watching session recordings or analyzing heatmaps provided by tools like FullSession that you get the “juicy” details.

This type of insight is what sets businesses apart—the ability not just to collect dots but connect them, too.

You might notice patterns emerge from this silent narrative that reveals more about usage habits than direct questions ever would because let’s face it—who doesn’t tweak the truth now and then?

The thing about qualitative data? It may be trickier to quantify, yes, but it can see things beyond the line of the “visible.”

In research and data analysis, it’s often the rich details from qualitative data that bring context and color to our understanding. Let’s see the main collection methods used in qualitative research.

1. Interviews

Through interviews, the data collected is rich and nuanced and provides a deep understanding of the participants’ perspectives, experiences, and attitudes. If you want to collect product feedback , interviews allow for detailed and specific responses, thus helping the researcher to comprehend the user’s experience and opinions.

Such direct interaction ensures that the researcher collects data that is both comprehensive and specific to the study’s objectives

In business settings for example, customer interviews help companies get under their market’s skin better.

Such kind of intel is what helps brands stay ahead because they know exactly what makes their customers tick.

2. Observations

If interviews let us hear people’s stories firsthand, then observations allow us to watch these tales unfold in real-time sneakily (but ethically). User testing is one powerful tool for UX research to observe user behavior directly.

This method lets researchers play detective without so much as whispering, “I suspect foul play.”

It shines when studying how people use products naturally – revealing stumbling blocks they might not even be aware enough to articulate.

3. Textual Analysis

Last up is textual analysis – basically book clubbing your way through anything written down or typed up related to your study topic, from social media posts to academic papers.

It is all about unpacking language patterns plus underlying meanings behind words.

Beyond spotting trends across tweets or reviews, that technique digs deeper and exposes beliefs and attitudes, even cultural norms embedded within texts, making sure no page gets left unturned or any word misunderstood.

Qualitative data has many use cases, and it’s pivotal for many industries. We’ll touch base on some of them. Let’s see.

1. In Business

In business, companies often use customer interviews to get the nitty-gritty of user experience. It’s like detective work, where every opinion or facial expression can unlock secrets to improving products or services.

For instance, through customer feedback sessions , businesses might discover that users find their website harder to navigate than a maze without an exit – crucial insight for any web development team.

2. Healthcare

The healthcare sector relies heavily on patient narratives because symptoms are not just physical; they’re personal stories.

When doctors listen closely to these stories of discomfort or pain relief patterns during checkups, they gather reference material that is essential for diagnosis and treatment plans, turning patients into storybooks rather than just another number.

3. Educational Insights from Classrooms

Schools are gold mines for qualitative data collection, too. Educators may observe classroom interactions and realize some teaching methods spark excitement.

Such in-depth analysis can greatly help universities, too. Both schools and universities utilize insights from attendance patterns, engagement levels, and resource usage to inform policy and curriculum development.

4. Social Sciences

In social sciences, researchers conduct ethnographic studies by immersing themselves within communities; it’s almost like going undercover but with more note-taking and less drama.

Such a method could reveal how cultural nuances influence behavior subtly.

When it comes to understanding the intricacies of human behavior, qualitative data might be the right tool for each researcher. Still, it isn’t perfect, but we’re about to find out in the next paragraphs.

Advantages of Qualitative Data

Qualitative data gives us stories with depth. Imagine trying to understand why people love their favorite coffee shop; numbers might tell you how many customers come back, but conversations reveal the aroma’s nostalgic pull or the barista’s infectious smile.

It’s this richness that helps businesses tailor experiences to connect emotionally with customers.

Beyond anecdotes, qualitative research is flexible by nature—like an improvisational dance rather than rigid choreography.

Researchers have room to explore unexpected avenues as they emerge during interviews or focus groups.

Disadvantages of Qualitative Data

But let’s not get lost in romanticism because there are real challenges, too. For starters, analyzing reams of text from interviews or field notes isn’t for faint-hearted novices—it requires skilled interpreters who can identify patterns without injecting personal bias.

Furthermore, while quantitative results boast statistical significance and replicability, critics often view qualitative findings through skeptical lenses due to their subjective nature—as if they were trying to decipher abstract art instead of clear graphs.

There’s also time consumption; where quantitative studies sprint toward conclusions with rapid number-crunching software tools at hand, qualitative data requires some (or all) of the following:

• Pouring over transcripts
• Analyzing video recordings
• Coding textual responses manually takes patience—a luxury in fast-paced environments.

So, while qualitative research invites us into a world rich with color and texture beyond mere digits’ black-and-white clarity, we must tread carefully around its pitfalls lest our insights slip into subjectivity’s quicksand.

The process of collecting data for your research includes five steps. Let’s see which they are:

• Define Your Research Objectives : Clearly outline what you aim to understand through qualitative research. It’s paramount to pay a lot of attention because you might miss your targets if you do it incorrectly.
• Choose Your Data Collection Methods : Select the most appropriate qualitative data collection methods for your study. Each method has its strengths and caters to different types of research questions.
• Develop a Data Collection Plan : Prepare your data collection instruments, such as interview guides or observation checklists. Plan the logistics of your data collection. Select participants, schedule sessions, and address ethical considerations, like informed consent.
• Collect the Data : Implement your data collection plan. Be attentive and adaptable, as qualitative research often uncovers unexpected insights that may require you to adjust your approach on the go.
• Organize and Prepare Data for Analysis : After collecting your data, organize it for analysis. Having your data systematically organized will bring a more effective and thorough analysis process.

If you want to analyze qualitative data, you need to use a systematic examination to find out patterns and trends from the collected data.

1. Thematic Analysis

Imagine thematic analysis as your research’s highlight reel. It’s about picking out recurring themes across your dataset—whether they’re glowing reviews or gripes about user experience.

You sift through responses from interviews or focus groups and tag them with codes—a fancy term for labels—to track common threads.

Coding is less about ones and zeroes here; it’s more akin to sorting laundry by color and fabric type. Each piece of data gets sorted into categories you’ve created based on their significance to your study’s goals.

But beware—the wrong coding strategy can leave you tangled in data without any useful insight. And you need someone with technical knowledge.

3. Leverage Software Tools

Gone are the days when researchers had to comb through stacks of paper with nothing but sticky notes and sheer willpower. Modern problems require modern solutions, so enter stage left software tools designed specifically for qualitative analysis.

To make sense of complex user behavior patterns online, FullSession offers session recording features that turn abstract clicks into concrete stories worth reading—and learning from.

Remember, peeling back layers of qualitative data gives context colors—it paints pictures quantitative stats can’t capture alone because life isn’t just black-and-white (or red-and-green bar graphs).

Both qualitative and quantitative data add a lot of value for your business or organization. We can’t disregard each. That’s why we’ll try to see which one suits your next research better.

1. Distinguishing Qualities

The essence of qualitative data lies in its ability to capture the colorful intricacies of human experience, which often elude nominal data. It’s about focusing on behaviors and emotions that tick behind our decisions to understand why people do what they do. Meanwhile, quantitative data zeroes in on hard facts—the who, what, when, and where—with precision but may miss out on context.

To grasp these differences more clearly:

• Qualitative : Imagine interviewing someone about their favorite book—it’s all about feelings and opinions.
• Quantitative : Now consider counting how many books they read last year—a straightforward tally.

2. Critical Applications

In business or research settings, context is king when making informed decisions, which makes qualitative insights priceless. For instance:

• A focus group discussing a new product gives life to customer sentiments beyond mere satisfaction scores.
• User testing sessions reveal not just if an app feature is used but how it feels intuitively to interact with it—crucial for UX designers crafting memorable digital experiences.
• In healthcare, patient stories can illuminate subtleties in care quality missed by statistics alone.

It takes less than 5 minutes to set up your first website or app feedback form, with FullSession , and it’s completely free!

After that, you will be able to collect high-quality feedback and avoid the guesswork.

The FullSession platform offers a 14-day free trial. It provides two paid plans—Basic and Business. Here are more details on each plan.

• The Basic plan costs $39/month and allows you to monitor up to 5,000 monthly sessions.
• The Business plan costs $149/month and helps you to track and analyze up to 25,000 monthly sessions.
• The Enterprise plan starts from 100,000 monthly sessions and has custom pricing.

If you need more information, you can get a demo.

Qualitative data paints the full picture. It digs deep where numbers can’t reach, unveiling the human stories behind statistics.

Bear in mind these pointers. While qualitative insights offer depth, watch out for biases. Approach analysis with a mix of creativity and rigor to get it right.

Harness its power wisely: knowing when to use qualitative over quantitative data can make or break your research outcomes—so choose based on what story needs telling.

What is the main benefit of using qualitative data in research?

Qualitative data provides depth and detail and provides good insights into the ‘why’ and ‘how’ behind human behaviors and decisions, which numbers alone can’t reveal.

What is qualitative vs quantitative data?

Qualitative research explores the ‘why’ through words; quantitative research measures the ‘how much’ with numbers.

Why might someone choose qualitative research over quantitative?

If the research goal is to explore concepts or phenomena in-depth rather than to quantify them, qualitative research is the appropriate choice.

Can qualitative data be quantified for analysis?

While inherently non-numerical, qualitative data can be categorized and indirectly quantified through coding for thematic analysis and pattern identification.

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Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”.

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.”

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection:

Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.

Criterion sampling-selection based on pre-identified factors.

Convenience sampling- selection based on availability.

Snowball sampling- the selection is by referral from other participants or people who know potential participants.

Extreme case sampling- targeted selection of rare cases.

Typical case sampling-selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo.

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research.

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others.

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• v.68(3); May-Jun 2015

Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
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Qualitative Research in General

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Group Dynamics in Focus Groups

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Social Constructivism

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Collecting Qualitative Data

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Constructivist Grounded Theory

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

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Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Qualitative VS Quantitative Definition – Research Methods and Data

When you’re conducting research, your data will fall into two categories: qualitative or quantitative. So what’s the difference between these two data types?

Well, here’s a quick and easy way to remember at least the basic difference: quantitative data deals with quan tities of things – numbers and measurable information, like how many people visit a website each day. That’s all about quantity (sounds like quantitative, right?).

On the other hand, qualitative data gives you more insight into what people think, feel, and believe – the qual ity of a thing, person, or situation. Alright that one’s a bit more of a stretch, but it works.

Now let’s get more into the details of qualitative and quantitative research so you know how to conduct each.

What is Qualitative research?

Qualitative research focuses on the human perspective, and usually answers the question “why?” If you want to learn how people perceive their environment, why they hold certain beliefs, or how they understand their problems, you’ll conduct qualitative research.

It’s also all about context. When you’re researching a group, you want to study them in their natural environment. This gives you insights into their behavior, beliefs, opinions, and so on.

How do you conduct qualitative research?

You can conduct qualitative research in a few different ways. Doing interviews, setting up focus groups, giving people open-ended questionnaires, studying photo collections, and observing people in their daily routines are all forms of qualitative data collection.

When you engage with people in these ways, you’re giving the opportunity to give more in-depth, elaborate responses. They’re not just responding “yes” or “no” – they’re telling you what they think.

You can also make observations from photographs or from watching people – things like the way people are looking at each other lovingly, or how two old people might hold hands while they watch TV.

From these observations, you can theorize that those people love each other, are close to each other, know each other well and are comfortable around each other, and so on. Things that are hard to quantify with numbers or measure with figures.

What is Quantitative research?

Quantitative research, on the other hand, involves collecting facts and figures and often results in numerical, structured data. Think data you can put in a spreadsheet and analyze.

Instead of talking to people and getting their opinions, you’re asking them yes or no questions. Instead of asking someone why they do something, you’re finding out what they do, or how many people do that thing, or how often – and so on.

Real quick - what is structured data?

Let's say you're looking at a recipe on your favorite online cooking blog. The structured data are things like the ingredients, the oven temperature, how many calories a serving has, and how long you cook the food. These are all quantifiable (and measurable with numbers/facts) things.

Unstructured data, on the other hand, would include the food blogger's little story about how they discovered or created the recipe, what people have said about how delicious it is, and how much they love the texture of those soft, gooey cookies. You can't measure these data – they're opinion and experience-based.

How do you conduct quantitative research?

You can conduct quantitative research by looking at statistical data (how many people did x), giving people multiple choice or true/false tests, asking them yes/no questions on a survey, and so on.

All in all, you’re trying to answer the question “what” or “how” – what something is, what’s the number of people who order from Amazon every day, how many cars are in that parking lot.

Because of the nature of the data and collection methods, context isn’t a factor in this type of research.

With quantitative research, you’re interested in gathering data that support and prove or disprove a hypothesis or theory you already have.

So instead of observing and talking to people and then forming a theory about what’s going on, you collect your data, and then make conclusions about the validity of your hypothesis based on that data.

Is Qualitative or Quantitative research better?

Alright, so you have these two methods of research – which is better?

Well, most people would argue that they’re better when used together. They’re complementary. Each has its pros and cons (which we’ll discuss), but each method definitely brings important information to the table.

Before we discuss just how they can work together, let’s look at the good and the bad of each.

Pros and Cons of Qualitative research

Let's start with the good. Qualitative research lets you dig deeper into a problem, situation, or context and see why things are happening. You get personal insights from your subjects that can't necessarily come from numbers and figures.

You also have the benefit of context, which can shed light on why a person said certain things or was feeling a certain way (for example if they live in a war zone or in a small village in the middle of nowhere or in the largest city in the world).

On the other hand, qualitative research is more time-consuming and therefore expensive. It takes a lot more time to interview people or set up focus groups than it does to send someone a simple yes/no survey.

It can also be harder to get people to participate in qualitative research. They might not have the time or energy (or desire) to share extensively.

Finally, qualitative research is never really definitive. People are always changing, as are their perceptions of the world around them. So while qualitative data can help inform your hypothesis and fill in gaps in your research, it should usually be supported by quantitative data.

Pros and Cons of Quantitative research

Quantitative research produces hard facts, numbers, and other measurable things. Which can be very useful when you're trying to prove a theory or understand what you're dealing with.

It's also independent of changeable things, like researcher bias or people's current opinions or moods. So quantitative research is repeatable and can be tested and re-tested again and again.

And, practically speaking, quantitative data analysis can be performed much more quickly than qualitative research. You can simply send someone a survey, collect the response data, and dump that data into a spreadsheet or database. From there, running various queries and analyses is easy (assuming you know what you want to ask).

Still, quantitative research is limiting in certain ways. People can't explain their answers to a multiple choice test or yes/no survey (again, lack of context). This means you can't take human factors into account.

So while you have the facts and numbers, you have to decide how to interpret them and use them in your research. (This can be both good and bad.)

How to use Qualitative and Quantitative research together

Sometimes it’s best to start with qualitative research – gather information, talk to people, try to understand their problems/perceptions/opinions, then form a hypothesis.

Then, once you have your hypothesis, use quantitative methods to confirm (or disprove) it with data analysis. This will show you whether the issue/problem/situation exists in general, or was just part of someone’s perception.

But qualitative research/insights can also help round out your structured data/conclusions – if you’ve learned that x people use your site every day, quotes from people about why they use it (as opposed to another company) can teach you more about what’s working (or not) and why.

Examples of Qualitative and Quantitative research

First example.

Say you want to learn more about people who visit Paris on vacation. You could look at flight data, museum admission numbers, tourist info to figure out how many people visit Paris each year. But that won’t tell you why they’re visiting.

To learn why, you have to ask people why they wanted to visit Paris, what was their favorite part of the city, what was their experience like as a tourist in Paris, and so on. This will give you insights into what motivates people to travel there in the first place.

Another example

Let’s say you run an e-commerce site that helps people resell their gently used clothing.

You can gather information about how many people sell clothes on your site, how many items the average person has sold, how many people visit the site to buy those clothes, and so on. All that’s right there in the analytics.

But if you want to know why people choose to use your site – either to sell or buy clothes – you’d want to start by conducting an open-ended questionnaire or ask for feedback on a survey.

Also, if you want to know what they like about your site, and how that influences their decision to use it, you could ask them to describe their experience using the site, and so on.

Ultimately, you’ll want to use both qualitative and quantitative research to get the whole picture. And you won’t just use one, and then just use the other. You can go back and forth between the two methods as your research evolves and you gather more information.

This will help you get a more complete picture, form a stronger and deeper hypothesis, and establish both facts about and insights into the situation.

Former archaeologist, current editor and podcaster, life-long world traveler and learner.

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Qualitative Data Collection: 6 Things Researchers Need to Know to Get it Right

April 1, 2021.

• In more than 4 million homes across America, children are living and playing while being exposed to the damaging effects of lead.
• Approximately 3,500 infants die from sleep-related causes every year, and thousands of babies and mothers miss out on the benefits of continued exclusive breastfeeding.
• Black, American Indian, and Alaska Native (AI/AN) women are two to three times more likely to die from pregnancy-related causes than white women.

These statistics are more than just numbers – they represent millions of children throughout the country, each with their own stories and individual lived experiences. But at first glance, it is difficult to contextualize this data and fully understand the root causes of these alarming rates. Public health researchers can help a wider audience feel a connection to the data at hand.

What is Qualitative Data?

Qualitative data is the descriptive and conceptual findings collected through questionnaires, interviews, or observation. Analyzing qualitative data allows us to explore ideas and further explain quantitative results. While quantitative data collection retrieves numerical data (what, where, when), qualitative data, often presented as a narrative, collect the stories and experiences of individual patients and families (why, how):

• Quantitative Data: 87 percent of adults with sickle cell disease reported missing a preventative care appointment.
• Qualitative Data: During a virtual interview, a patient with sickle cell disease shares that she sometimes misses appointments due to not having reliable access to transportation.
• Quantitative Data: About 4 million infants are born each year in the United States, and most of them receive newborn screening for conditions that can cause serious health problems.
• Qualitative Data: During a focus group discussion, health care workers express that there is no time allocated to discuss the process of newborn screening with new parents.  

Qualitative analysis is important because the rich detail shared by individuals is extremely powerful in thinking through complex systems and can illustrate how the implementation of our programs and policies are working in real life and ultimately lead to change.

Here are six tips for gathering qualitative data and making the most out of your analysis.

1. Define your research question.

What data are you looking to collect? A qualitative research question is a definite or clear statement about your project’s area of concern, a condition to be improved, a difficulty to be eliminated, or a troubling question that exists. It not only defines who your participants will be, but guides the data collection methods needed to achieve the most detailed responses. Your question should be clear and concise with a focus on one health issue.

Example research problem: What are the barriers and facilitators among Black mothers to exclusively breastfeeding for the recommended six months?

When developing your research question, consider the following:

• What do you most want to know? What question(s) will you address?
• What is known about this issue and what theoretical approaches have been applied to this problem? What do you want to learn more about?
• What led you to this question?
• Why is this problem or question important to you, policymakers, academics, health care professionals? Or why is it important to the population/community you are aiming to improve?
• How will this question make a new contribution to the field of public health?

2. Determine the best data collection(s) method for your research question.

The two most common qualitative data collection methods within health care and quality improvement research are in-depth interviews and focus groups with patients, family members, community members, or key informants. Focus groups allow researchers to collect data rather quickly from multiple participants and can result in a robust conversation between the participants, providing very rich and genuine responses. On the other hand, in-depth individual interviews allow for a more personal interaction.This method may be more appropriate for collecting long, detailed responses from one participant or if the conversation is about more private information. Understanding the resources available and constraints of your project will help you determine which method is best for answering your research question.

Also, consider if your interview or focus group will be conducted virtually, over the phone, or in person. Each approach comes with its own set of benefits and challenges. For example, conducting a focus group virtually allows you to utilize chat features and visually display your questions, but your group may lack the same energy or connectedness from being in-person.

3. Develop a cohesive interview guide.

An interview guide is a list of open-ended questions that provide a framework for your interview or focus group. The goal is to collect as much information as possible, so your interview questions should encourage your participants to openly share their experiences. These three types of questions with help you get started:

• Broadest research questions: "Tell me what it’s like…"
• Follow-up questions: "Can you elaborate on …"
• Targeted questions: "Have you ever experienced…"

4. Stay neutral – Let participants share their stories.

Preventing bias is critical for collecting genuine, honest responses from your participants. This can be achieved by conducting an interview with the mindset that you are there to learn about the participants' experiences. Conducting an interview with assumptions of what your participant will share may lead to leading questions that force specific responses. That’s why it’s important to inspect your interview guide ahead of time for leading questions to reduce the risk of bias. See examples below.

Leading question : "Do you have difficulty breastfeeding your child because of time constraints?"

This question assumes that the mother is experiencing challenges with breastfeeding due to a specific cause that could be an assumption made by the interviewer.

Neutral Question: "What has your experience been with breastfeeding?"

This question allows the participant to say themselves if they are or are not breastfeeding and whether it’s been difficult. A follow-up question could be:

• "Has breastfeeding ever been difficult for you? If so, tell me about a time that it was."  The first question may result in a short response from your participant, so a follow-up question will continue to allow the participant to answer more freely about their experiences.

Remaining neutral also requires you to be cautious of your body language and natural but unscripted reactions to your participant’s responses. Practice staying neutral by conducting an interview with a partner using a very basic research question (e.g. "What is your favorite color?"). Experiment using broad, follow-up, and target questions while your partner takes notes of your behaviors. This exercise is helpful for sticking to your interview guide while speaking in a comfortable, welcoming manner.

5. Double Up!

If possible, work with at least one additional team member when conducting and analyzing qualitative research. Having a research partner is especially useful when conducting a focus group. One team member will moderate the group while the other takes notes. The moderator can then focus on guiding the conversation and navigating the interview guide and while the note-taker captures participants' responses and can begin establishing themes in real-time. Be sure to set clear expectations of your roles prior to the interview, preferably in writing. You do not want to finish conducting a focus group and realize that neither you nor your partner recorded the session!

6. Analyze your findings.

Once the data is collected, it is time to think about the story you will tell. Listen or read through your interviews to identify answers to your research question, repeated words and phrases, and experiences that have not been researched prior. Combining all your data from separate interviews and connecting themes will unveil answers to your research question and outline the efforts needed to address the health issue that your project is aiming to improve.

Data Collection is a necessary part of quality improvement. Learn more  about how collecting data can make a huge difference in the results of a public health initiative. 

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 Our main story this episode highlights he links between maternal and infant health, and the impacts of preterm birth on health outcomes for mothers, birthing people, and babies. Hear from NICHQ VP of Equity and Innovation Dr. Stacy Scott, Ph.D. D, MPA, who shares some equity considerations regarding disparate rates of preterm birth, infant mortality, and maternal mortality. Dr. Zsakeba Henderson, MD, FACOG, NICHQ’s senior health advisor, also connects the impact of maternal health on infant health outcomes while shining a light on the U.S. maternal mortality crisis and current policy initiatives that can help reverse maternal mortality trends.  

NICHQ Employee Spotlight: Callie Rowland

Each month, we’re shining a spotlight on a NICHQ employee, asking them to share their memories, advice, and goals. This month, NICHQ Project Manager Callie Rowland, MPH, shares her passion for working to affect change in the system, serving all mothers and children, and working for an organization that aims to help create more equitable systems.

NICHQ Employee Spotlight: Madeline D'Onfro

Each month, we’re shining a spotlight on a NICHQ employee, asking them to share their memories, advice, and goals. This month, NICHQ Project Manager Madeline D'Onfro, MPH, shares her passion for creating systems that protect members of society who are marginalized the most and improving experiences for those with the highest needs.

4 Strategies for Transitioning from Pediatric to Adult Care for People Living with Sickle Cell Disease

Whether transitioning to college or a full-time job, it's a time when young adults are going to be establishing their independence from their nuclear family and taking responsibility for their own needs. For a young person with special healthcare needs such as SCD, the responsibilities are compounded by the additional need to begin transitioning from pediatric to adult care. Read more for four helpful strategies for transitioning from pediatric to adult care for people living with sickle cell disease and other special health conditions.