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Qualitative Data – Types, Methods and Examples

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Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

• Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
• Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
• Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
• Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
• Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
• Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
• Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

• Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
• Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
• Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
• Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
• Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

• Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
• Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
• Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
• Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
• Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
• Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

• Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
• Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
• Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
• Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
• Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
• Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
• Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
• Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
• I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
• Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

• Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
• Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
• Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
• Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
• Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
• Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

• Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
• Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
• Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

• Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
• Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
• Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
• Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
• Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

• Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
• Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
• Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

• Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
• Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
• Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
• Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
• Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
• Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
• Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

• Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
• Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
• Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
• Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
• New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
• Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
• Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

• Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
• Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
• Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
• Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
• Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
• Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
• Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

About the author

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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10 Examples of Qualitative Data: A Comprehensive Guide

• by Daniel Taylor
• October 10, 2023

In the world of research and data analysis, qualitative data plays a crucial role in providing insights and understanding complex phenomena. This type of data helps researchers delve into the why, how, and what of a subject, rather than simply focusing on numerical measurements . Whether you’re conducting a qualitative research project or trying to make sense of qualitative data types, it’s essential to have a clear understanding of what they are and how they can be applied.

In this blog post, we will explore 10 examples of qualitative data that can be found in various fields of study, such as sociology, psychology, anthropology, and market research. We’ll also dive into the characteristics of qualitative research, the different types of qualitative approaches, and how to obtain and analyze qualitative data effectively. So, if you’re ready to explore the diverse world of qualitative data, let’s get started!

What are 10 Examples of Qualitative Data?

Qualitative data, unlike its numerical counterpart, provides a deeper understanding of the human experience. It encompasses information that is descriptive in nature, allowing researchers to explore the nuances and intricacies of a particular phenomenon. So, what are some examples of qualitative data that can help researchers gain valuable insights? Let’s dive in!

1. In-depth Interviews:

Conducting one-on-one interviews allows researchers to gather rich qualitative data by exploring the interviewees’ thoughts, opinions, and experiences. These interviews often delve into personal narratives, providing invaluable insights into a wide range of topics.

2. Focus Groups:

Bringing people together for a group discussion can yield a treasure trove of qualitative data. Focus groups facilitate lively conversations, enabling researchers to examine social dynamics, uncover common themes, and gain a deeper understanding of participants’ perspectives.

3. Observations:

Through direct observations, researchers can gather qualitative data by carefully witnessing and documenting behaviors, interactions, and contextual details. This method is particularly useful in fields such as anthropology and sociology, providing an up-close look into human behavior.

4. Case Studies:

Examining a specific individual , group, or event in detail allows researchers to generate qualitative data that offers a comprehensive understanding of complex situations. Case studies often involve multiple data sources, such as interviews, observations, and document analysis.

5. Diaries and Journals:

By encouraging individuals to keep diaries or journals, researchers can gain access to their inner thoughts, emotions, and experiences. This form of qualitative data allows for a deep exploration of personal narratives over time, providing unique insights into subjective experiences.

6. Open-Ended Surveys:

Using open-ended survey questions allows participants to provide detailed and qualitative responses. Unlike closed-ended surveys that limit options to predefined choices, open-ended surveys capture individual perspectives and allow for a more nuanced analysis.

7. Text and Document Analysis:

Analyzing written texts, documents, or transcripts provides a rich source of qualitative data. This method is commonly used in fields such as literature, history, and linguistics, allowing researchers to delve into the meaning, context, and themes embedded within texts.

8. Visual Data:

Qualitative data is not limited to text-based sources. Visual data, such as photographs, videos, and artwork, can offer unique insights and narratives. Visual analysis allows researchers to explore the power of imagery in conveying emotions, cultural influences, and societal messages.

9. Ethnography:

Ethnography involves immersing oneself in a particular community or culture to gain a deep understanding of their beliefs, practices, and social norms. This qualitative research approach provides rich, contextual information and helps uncover hidden meanings embedded within a specific cultural context.

10. Online Forums and Social Media:

Digital platforms offer a wealth of qualitative data through online forums, social media discussions, and comment sections. These platforms enable researchers to analyze conversations, opinions, and experiences shared by individuals from diverse backgrounds, widening the scope of qualitative research.

It’s important to note that qualitative data collection methods often overlap, with multiple approaches used in combination to provide a comprehensive understanding of a research topic. By leveraging these diverse methods, researchers can delve into the complexities of human experiences, shedding light on the nuances that quantitative data often misses.

So, whether you’re an aspiring researcher or simply curious about the intricacies of qualitative data, exploring these examples will deepen your understanding and appreciation for the wealth of insights that qualitative research has to offer.

FAQ: What are 10 Examples of Qualitative Data?

What are qualitative data types.

Qualitative data types refer to the various forms of non-numerical information that is collected and analyzed in qualitative research. They provide insights into people’s experiences, opinions, behaviors, and perceptions. Qualitative data types include textual, visual, oral, and observational data. Researchers use these data types to gain a deeper understanding of complex phenomena.

What Are 3 Examples of Qualitative Research

Qualitative research is a method used to explore and understand individuals’ experiences, behaviors, and beliefs. Here are three examples of qualitative research:

In-depth Interviews: Researchers conduct open-ended interviews to obtain detailed information about participants’ experiences, perspectives, and opinions.

Focus Groups: A moderator facilitates a group discussion to explore specific topics or issues, allowing participants to share their thoughts and ideas with each other.

Ethnography: Researchers immerse themselves in a particular culture or community to observe and document social interactions, practices, and behaviors.

Is Eye Color Qualitative or Quantitative

Eye color is a qualitative data characteristic. It falls under qualitative data because it is not measured or represented numerically, but rather described categorically (e.g., blue, brown, green, etc.).

What Are the 11 Characteristics of Research

Research is characterized by various factors that contribute to its credibility and reliability. Here are 11 key characteristics of research:

Systematic: Research follows a structured and organized approach.

Empirical: It is based on observations and evidence gathered from the real world.

Objective: Research aims to remain impartial and free from personal biases.

Logical: It follows logical reasoning and analysis.

Cyclical: Research is an ongoing and iterative process.

Replicable: Findings can be duplicated or tested by other researchers.

Ethical: Research adheres to ethical guidelines and principles.

Transparent: Researchers provide detailed information and methods, making their work transparent.

Generalizable: Research findings can be applied and generalized to broader populations or contexts.

Clear Purpose: Research has a clear objective or aim.

Controlled: Variables are controlled to minimize external influences and ensure accuracy.

How Do You Find Qualitative Data

Qualitative data can be found through various methods and sources. Here are a few ways to access qualitative data:

Interviews: Conducting interviews with individuals or focus groups can provide rich qualitative data.

Observations: Immersing oneself in the research environment to observe and document behaviors and interactions.

Surveys: Open-ended questions in surveys can yield qualitative data if participants provide detailed responses.

Documents: Analyzing written records, such as diaries, letters, or organizational documents, can provide valuable insights.

What Are the 4 Types of Research Methods

Research methods refer to the specific approaches and techniques used to collect and analyze data. The four main types of research methods are:

Experimental Research: Researchers manipulate variables to determine cause-and-effect relationships.

Survey Research: Data is collected through questionnaires or interviews to understand opinions, behaviors, or attitudes.

Observational Research: Researchers observe and document behaviors and interactions in natural or controlled settings.

Qualitative Research: This approach focuses on understanding experiences, beliefs, and meanings through in-depth investigations.

What Are the 6 Types of Qualitative Research

Qualitative research encompasses various methods and approaches, including:

Phenomenology: Examines individuals’ subjective experiences and how they interpret and make meaning of those experiences.

Grounded Theory: Develops theories based on analyzing data and identifying emerging patterns or themes.

Ethnography: Immerses researchers in a culture or community to understand their practices, beliefs, and social interactions.

Case Study: In-depth analysis of a particular case or a small group to gain detailed insights.

Narrative Research: Focuses on collecting and analyzing personal stories or accounts to explore individuals’ experiences.

Action Research: Collaborative research approach where researchers work with participants to bring about practical solutions or improvements.

What Are 5 Examples of Qualitative Data

Qualitative data can take various forms. Here are five examples:

Interview transcripts: Verbatim records of interviews containing participants’ responses, emotions, and perspectives.

Field notes: Detailed written descriptions of observations, interactions, and contexts during data collection.

Photographs: Visual representations capturing specific moments, events, or environments for analysis.

Audio recordings: Recordings capturing participants’ voices, discussions, or narratives.

Textual documents: Letters, diaries, or archival documents offering insights into people’s thoughts and experiences.

What Are the 5 Qualitative Approaches

Qualitative research employs different approaches to gain insights into individuals’ experiences. Here are five common qualitative approaches:

Interpretative Phenomenological Analysis (IPA): Focuses on understanding how individuals make meaning of their experiences.

Content Analysis: Systematically analyzes textual, visual, or audio data to identify patterns, themes, or meanings.

Narrative Analysis: Explores the stories or accounts individuals construct to make sense of their experiences.

Grounded Theory: Develops theories grounded in empirical data, emerging patterns, and codes identified during analysis.

Discourse Analysis: Analyzes language use to understand how social, cultural, or political contexts shape meaning.

What Are the 10 Types of Research

Research can be classified into various types based on the objectives and methods used. Here are ten common types of research:

Basic Research: Seeks to expand knowledge without any immediate practical application.

Applied Research: Focuses on solving specific problems or addressing practical issues.

Experimental Research: Investigates cause-and-effect relationships through controlled manipulation of variables.

Survey Research: Collects data through questionnaires or interviews to understand opinions, attitudes, or behaviors.

Case Study Research: In-depth analysis of a particular case, individual, or group.

Observational Research: Systematically observes and documents behaviors, interactions, or phenomena in natural or controlled settings.

Longitudinal Research: Studies trends or changes over an extended period by collecting data at multiple points in time.

Correlational Research: Examines the relationship between variables without direct manipulation.

Qualitative Research: Explores individuals’ experiences, beliefs, and perspectives through in-depth investigations.

Quantitative Research: Focuses on collecting and analyzing numerical data to generalize and make statistical inferences.

What Is Qualitative Data

Qualitative data refers to non-numerical information that helps researchers understand complex phenomena in depth. It provides insights into subjective experiences, opinions, attitudes, and behaviors. Qualitative data is collected through methods such as interviews, observations, focus groups, and document analysis. Its analysis involves identifying patterns, themes, and meanings rather than statistical calculations.

What Are the 4 Types of Quantitative Research Design

Quantitative research designs are structured approaches used to collect and analyze numerical data. The four main types are:

Descriptive Design: Describes characteristics or phenomena, often by calculating frequencies or averages.

Correlational Design: Examines the relationship between variables using statistical analyses.

Experimental Design: Manipulates variables to determine cause-and-effect relationships through controlled experiments.

Quasi-Experimental Design: Resembles experimental design but lacks complete control over variables.

Is Age an Example of Qualitative Data

No, age is an example of quantitative data. It is a measurable and numerical value that can be represented on a continuous scale. However, age can also be categorized into qualitative data if represented categorically (e.g., young, middle-aged, elderly).

Is Gender Qualitative Data

Yes, gender is an example of qualitative data. It is a categorical variable that classifies individuals into distinct groups—male, female, or non-binary—based on societal and cultural norms.

What Are 5 Examples of Quantitative Data

Quantitative data represents numerical values and can be analyzed statistically. Here are five examples:

Height in centimeters: A measurable value that can be analyzed using statistical methods.

Test scores: Numerical results obtained from exams or assessments.

Time taken to complete a task: A quantifiable measure of performance.

Income: Numerical data representing an individual’s earnings.

Number of siblings: A countable value indicating the total number of siblings a person has.

What Are the 8 Characteristics of Qualitative Research

Qualitative research is characterized by certain key traits that distinguish it from quantitative research. Here are eight characteristics of qualitative research:

Naturalistic: It aims to understand phenomena in natural settings, preserving their real-world context.

Subjective: Recognizes and emphasizes the influence of researchers’ interpretations and perspectives on data analysis.

Contextual: Considers the social, cultural, and environmental factors that shape individuals’ experiences.

Emergent Design: Allows the research design to evolve and adapt based on emerging findings and new insights.

Inductive: Builds theory or understanding based on observations and data analysis, rather than preconceived hypotheses.

Holistic: Considers the whole experience or situation, examining the interconnectedness of various factors.

Detailed and Descriptive: Seeks to provide a rich, comprehensive, and nuanced understanding of phenomena.

Non-Generalizable: Qualitative research focuses on deep insights, rather than seeking to generalize findings to larger populations.

What Are Examples of Qualitative and Quantitative Data

Examples of qualitative data include interview transcripts, field notes, and photographs, while examples of quantitative data include numerical measurements like test scores, ratings, or survey responses that can be statistically analyzed.

What Are the 10 Characteristics of Research

Research exhibits certain essential characteristics that distinguish it as a systematic and rigorous process. Here are ten key characteristics:

Methodical: Research follows a logical and structured approach.

Empirical: It relies on evidence and observations derived from real-world phenomena.

Objective: Research aims to remain unbiased, minimizing personal opinions or preferences.

Analytical: It employs logical and critical thinking to analyze data and draw meaningful conclusions.

Cyclical: Research is an iterative process that involves multiple stages and revisiting research questions.

Ethical: Research adheres to ethical principles, safeguarding participants’ rights and welfare.

Transparent: Researchers provide a clear account of their methods, data, and analysis processes.

Generalizable: Research findings can be applied or generalized to larger populations or contexts.

Empowering: Research has the potential to inform decisions, policies, or improvements in various fields.

Collaborative: Researchers often work in teams, exchanging ideas, expertise, and feedback to enhance research outcomes.

What Are the Two Main Types of Qualitative Data

Qualitative data can be broadly classified into two main types: textual and non-textual data. Textual data refers to written or verbal communication, including interview transcripts, written narratives, or recorded discussions. Non-textual data encompasses visual or observational data, such as photographs, videos, or field notes.

What Are the Examples of Qualitative Data

Examples of qualitative data include interview transcripts, field notes, photographs, audio recordings, videos, and written documents like diaries or letters. These types of data help researchers gain insight into individuals’ experiences, perspectives, and behaviors.

Which of the Following Is an Example of Quantitative Data

The number of siblings a person has is an example of quantitative data. It is a countable and numerical value that can be represented and analyzed using mathematical methods.

What Are the Four Types of Qualitative Data

Qualitative data can manifest in various forms. The four main types of qualitative data are:

Textual Data: Verbal or written records, such as interview transcripts, essay responses, or open-ended survey questions.

Visual Data: Photographs, videos, or other visual representations capturing aspects of the research context.

Auditory Data: Audio recordings of interviews, focus groups, or naturalistic settings.

Sensory Data: Data that captures sensory experiences, such as descriptions of smells, tastes, textures, or sensations.

What Are the 7 Types of Qualitative Research

Qualitative research encompasses several distinct types, each with its own focus and methods. Here are seven common types of qualitative research:

Ethnography: Involves immersing researchers in a particular culture or community to explore their practices and beliefs.

Phenomenology: Examines individuals’ subjective experiences and how they make meaning of those experiences.

Grounded Theory: Develops theories or explanations through systematic analysis of qualitative data.

Case Study: In-depth exploration of a particular case, entity, or situation to gain a comprehensive understanding.

Narrative Research: Focuses on analyzing personal stories or accounts to uncover themes and patterns within narratives.

Content Analysis: Systematically examines textual or visual data to identify common themes, codes, or meanings.

Action Research: Collaborative research approach where researchers and participants work together to bring about positive change or interventions.

What Are the Types of Quantitative Data

Quantitative data can be categorized into four types based on the scale of measurement:

Nominal Data: Categorical data without any inherent order or ranking, such as gender or eye color.

Ordinal Data: Categorical data with a natural order or ranking, such as survey ratings or educational levels.

Interval Data: Numerical data that has a defined scale with equal intervals, such as temperature measured in Celsius or Fahrenheit.

Ratio Data: Numerical data with a meaningful zero point, allowing for ratios and mathematical operations, such as height or weight.

• cultural influences
• data analysis
• focus groups
• human experience
• in-depth interviews
• observations
• participants
• qualitative data
• qualitative research project
• quantitative data
• specific individual
• visual data

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

87 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

Thanks so much for the write-up, it’s really good.

Thanks madam . It is very important .

thank you very good

Great presentation

This has been very well explained in simple language . It is useful even for a new researcher.

Great to hear that. Good luck with your qualitative data analysis, Pramod!

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

Thank you so much.

very informative sequential presentation

Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

Thank you very much, this is well explained and useful

i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Thank you, this is well explained and very useful.

Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

The session was very helpful and insightful. Thank you

This was very helpful and insightful. Easy to read and understand

As a professional academic writer, this has been so informative and educative. Keep up the good work Grad Coach you are unmatched with quality content for sure.

Keep up the good work Grad Coach you are unmatched with quality content for sure.

Its Great and help me the most. A Million Thanks you Dr.

It is a very nice work

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

This is Amazing and well explained, thanks

great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

This was incredibly helpful.

Very helpful.

very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

This was helpful. Thank you

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

The tutorial is useful. I benefited a lot.

This is an eye opener for me and very informative, I have used some of your guidance notes on my Thesis, I wonder if you can assist with your 1. name of your book, year of publication, topic etc., this is for citing in my Bibliography,

I certainly hope to hear from you

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Qualitative and Quantitative Data – Definitions and Examples

There are two types of data. Qualitative data is descriptive information about characteristics that are difficult to define or measure or cannot be expressed numerically. Quantitative data is numerical information that can be measured or counted.

Qualitative → Qualities

Quantitative → Quantities

Examples of Qualitative Data

• feelings and emotions
• color (unless it can be written as a specific wavelength of light)
• expressions of more/less, ugly/beautiful, fat/thin, healthy/sickly

Examples of Quantitative Data

Quantitative data can be expressed as numbers. If you can measure it, it can be expressed as a quantity.

• number of objects
• temperature
• percentages

Using Both Types of Data

While quantitative data is easier to analyze, qualitative data is also important.

If you are conducting an experiment using the scientific method, for example, you want to record your observations and data as thoroughly as possible.

Let’s say you want to describe a cat. You would use both types of data. Qualitative data could include its fur color and the color of its eyes, whether its fur is soft or rough, the sounds it makes, how it smells, whether it seems affectionate or aloof, its health, whether it is young or old. Quantitative data could include its length, weight, number of toes on each paw, how high it can jump, how many ounces of food it eats each data, and its body temperature. 

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• Knowledge Base
• Methodology
• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing 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 analysing 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, and history.

• 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 organisation?
• 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, 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 emphasise different aims and perspectives.

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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 analysing 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 organise 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 categorise 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 analysing qualitative data. Although these methods share similar processes, they emphasise 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.

Researchers must consider practical and theoretical limitations in analysing 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 analysing 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 generalisability

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

• Labour-intensive

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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 organisation 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 organisations.

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

• Prepare and organise 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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Pritha Bhandari

Qualitative data: Examples and how to use it

Data analysis is at the core of any solid business strategy—however, it can get pretty complex. 

As you probably know, data can be broken down into two main categories: quantitative data and qualitative data. Though very different, both types are highly valuable in understanding the digital customer experience.  

Today, we’re focusing on the importance of qualitative data —the different types, examples, and why it’s important to your bottom line.

The definition of qualitative data

Qualitative data, also called categorical data, is used to categorize something in a descriptive, expressive way, rather than through numerical values. Simply put, it’s information about an object or subject that you can see or feel. 

Generally, qualitative analysis is used by market researchers and statisticians to understand behaviors . For business, it’s commonly used by data analysts to understand and interpret user behavior . 

You can turn to qualitative data to answer the "why" or "how" behind an action. It’s often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Qualitative data includes traits such as how a customer feels about a product or service and what they are motivated by. It’s excellent in understanding the motivations behind customer experiences.

There are many ways to gather qualitative data, including:

Research and observation

Surveys or questionnaires

Focus groups, online forums, or communities

Case studies

These types of qualitative research methods are often expressed using studies, pie charts, or graphs. 

The importance of qualitative data

At a high level, qualitative data analysis provides data analysts a way to identify trends in the world around them. 

For online businesses, qualitative data can be used to identify the particular frequency of traits or characteristics in customers, users, or website visitors.

Qualitative data can also help businesses understand their consumers’ language and address problems effectively and efficiently, which is priceless when you’re trying to scale and grow a company. 

Qualitative (categorical) vs. quantitative data

Before we get into types and examples, here’s a quick breakdown of the differences between qualitative and quantitative data. 

Qualitative data is observational and descriptive, while quantitative data is numerical and measurable. Data analysts often use qualitative and quantitative data together when conducting studies or analyses. 

When it comes to data research, you’ll need different collection, hypotheses, and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data :

Quantitative data

Numbers-based, countable, or measurable

Tells us how many, how much, or how often in calculations

Analyzed using statistical analysis

Qualitative data

Interpretation-based, descriptive, and relating to language

Understand why, how, or what happened behind certain behaviors

Analyzed by grouping the data into categories and themes

Types of qualitative data

As we narrow down the importance of qualitative data, you should understand that there are different types. Data analysts often categorize qualitative data into three types:

1. Binary data

Binary data is represented or displayed in the binary numeral system and is numerically represented by a combination of zeros and ones—much like coding on a computer. In fact, binary data is the only category of data that can be directly understood and executed by a computer.

Researchers 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.

2. Nominal data

Nominal data , also called “named” or “labeled data,” or a “nominal scale,” 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. Additionally, you might use nominal data to create multiple-choice survey responses or to profile participants.

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. 

For example, questionnaire responses on a scale from satisfied to unsatisfied are considered ordinal data results. Data analysts might use ordinal data when creating charts, while researchers might use it to classify groups such as age, gender, or class.

Qualitative data examples

Qualitative data is all around you. We’re going to cover how this type of data might appear in descriptions, research, work, and statistics. 

Qualitative data in descriptions

Describing data points can create more context in your analysis. Here are some basic examples of qualitative data in a descriptive style:

The woman has light brown hair and bright blue eyes.

The office was dark and cold with light tan wallpaper.

The purple flowers smelled like lilac and lavender.

The plump, white dog had cute whiskers.

The tall businessman wore a gray striped suit.

The tan leather couch was soft and comfortable.

Qualitative data in research

Qualitative data research methods allow analysts to describe and explain their subjects and use that information to create theories and models. This can be helpful to understand motivations and actions. 

Examples of qualitative data collection in research include open-ended and close-ended questions like:

What state do you live in?

What is your most recent degree: bachelor’s, master’s, or Ph.D.?

How do you rank against your competition? 

Which digital intelligence platform do you use the most: Fullstory , Google Analytics, or HotJar?

How do you rate the restaurant’s customer service: not good, good, great, or excellent?

Qualitative data at work

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

A professor gives a student feedback on their math skills. "Your calculations are concise, and you understand the core concepts."

A psychiatrist observes and analyzes a patient’s behavior. "The woman was angry and disinterested in her husband."

A marketer collects feedback through surveys and customer interviews. "The customer said the onboarding experience was top notch.”

A doctor evaluates the lifestyle and behavior of patients to determine health risks. "Most patients have elevated blood pressure because of high levels of stress."

A lab student transcribes what happens during an experiment: "The solution turned pink when we added salt."

Qualitative data in statistics

Qualitative data can provide companies with important statistics about customers, groups of users, and products. Here are some examples of qualitative data set collections for statistical purposes:

The origin, gender, and location of citizens so a state legislature can categorize the population in a certain county.

The name, title, and profession of people attending a webinar to help target the right content.

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

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 data by using an open-ended process.

Qualitative data research provides useful content in any data 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.

A qualitative data collection tool 

By collecting qualitative data using a Digital Experience Intelligence (DXI) solution like Fullstory , data analysts have access to customer intelligence and robust research capabilities. Data analysis is made easy with an efficient data collection tool that replays real-time sessions .

DXI combines qualitative and qualitative data to analyze the customer experience at different points along their omnichannel journey , and use robust insights to continuously improve the digital customer experience. 

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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.

15 Qualitative Data Examples

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Qualitative data refers to data that is non-numerical. It can include interview transcripts, video data, images, observation notes, and so on.

The great benefit of qualitative data is that it allows researchers to achieve rich, detailed, and unique insights into a phenomenon in a way that quantitative data cannot.

However, its downside is that it is not so easily subjected to statistical analysis, and tends not to be able to produce generalizable results that have statistical meaning beyond the sample or case study under analysis.

Key types of qualitative data, also known as categorical data , include:

• Nominal data
• Ordinal data
• Binary data

Examples of each are provided later, but first, let’s look at some common sources for qualitative data.

Qualitative Data Examples

Common sources of qualitative data include:

1. Interview transcripts Interview transcripts are documented records of what was communicated during an interview, providing a detailed account of the interviewee’s experiences, perceptions, and opinions about the research topic. They serve as a rich resource, allowing researchers to revisit the interview repeatedly and deepen their understanding.

2. Video recordings Video recordings capture visual and auditory data simultaneously, providing a comprehensive account of social and non-verbal cues, environments, and actions; they can be analyzed for different purposes in a single study. A common method for analyzing them is multimodal analysis.

3. Audio recordings Audio recordings are sound files that capture spoken language, tones, pauses, and other audible cues during interviews or discussions. These are then transcribed and analyzed to understand verbal communication patterns, emotions, and the use of language.

4. Photographs Photographs offer a visual representation of reality, serving as a tangible memory of observed phenomena, which can be analyzed to understand substances, settings, or events without an overt researcher influence. Commonly, researchers use semiotic and semantic analysis to analyze photographs.

5. Diaries or journals Diaries and journals are collections of personal entries written by research participants, revealing their thoughts, experiences, and feelings over time; they can offer intimate details about their everyday lives, internal states, or lived experiences. They’re common when conducting autoethnographic research .

6. Letters or correspondence Letters and other forms of correspondence (say, emails) are personal or professional written communication between individuals. Analysis of such can reveal perspectives, emotions, beliefs, or values individuals share or foster about certain topics.

7. Field notes Field notes are the jottings researchers make while observing or interacting with participants in their natural contexts, documenting details about the setting, events, behaviors, or conversations in a methodical way to be analyzed later. These are common in ethnographic research projects .

8. Observational notes Observational notes are records researchers create when monitoring participants’ behaviors, interactions, or environments, capturing observable data in a structured manner for analysis. These are also common in ethnographies and fly-on-the wall observational studies .

9. Focus group discussions Focus groups involve guided group conversations where participants share and debate their views on a given topic, generating a dynamic interactional data that lends to the understanding of social constructions.

10. Artifacts Artifacts like tools, clothing, or documents, embody cultural, historical, or functional significations which when analyzed, reveal practices, norms, or values of the people who use them. They’re common in anthropological research. For more on this, see my guide on cultural artifacts .

11. Drawings or sketches Drawings and sketches are visual artistic creations that can express meanings, feelings, or thoughts; analyzing these can expose creative responses, symbolic meanings, or visual cognition processes. They were commonly used when psychoanalytic psychology was dominant in the social sciences.

12. Open-ended survey responses Open-ended surveys are participants’ free-text answers to survey questions, offering unique, self-expressed insights, experiences, or perspectives, instead of pre-set responses.

13. Personal narratives or stories Personal narratives are firsthand accounts of individuals’ experiences and how they make sense of them, allowing researchers to explore subjective meaning-making, identity construction, or value negotiation. These are common in life history research.

14. Social media posts and comments Increasingly common as a subject of research, especially with the rise of digital anthropology, social media posts represent individuals’ interactive communication in a digital environment, shedding light on their online identities, digital discourses, or community formation.

15. Written communication Written communication provides insights into how people craft their messages, deal with specific issues, or interact with others in semi-formal or formal written communication. This is commonly used as a data source in history and literature studies.

Types of Qualitative Data Examples

There are three types of qualitative data: nominal, ordinal, and binary.

Nominal Data

Nominal data refers to discrete units that can be named or labeled, but lack an inherent order or ranking system.

Its simplicity is its strength. Nominal data is the easiest type of data to collect and analyze, reducing the possibility of errors.

However, there’s a downside. The nominal data lacks depth as it can’t express the degree of difference because there’s no inherent numerical or ordered value associated.

Examples of nominal data include:

• Hair Color: With categorical options such as black, blonde, or brown, hair color is a clear example of this data type.
• Religion: As it categorizes people into various religious affiliations, say Christian, Muslim, Hindu, and so forth, religion is a nominal.
• Car Brands: Categorizing vehicles by their manufacturers, such as Ford, Toyota, or Mercedes..

See More Examples of Nominal Variables Here

Ordinal Data

Ordinal data is data that consists of categories with a specific order or ranking to them. It’s called ordinal because the data can be ordered .

A primary advantage of this data type is that it allows us to compare ranked social categories, unlike nominal, which doesn’t have inherent rank. Ordinal variables allow researchers to analyze and compare categories with ranks in a meaningful way.

However, ordinal data fails to quantify the difference between the rankings in any meaningful way (which differentiates it from interval data, which is quantitative in try), making detailed statistical analysis challenging.

Examples of ordinal data include:

• Academic Grades: When students are graded as A, B, C, D, or E, we are creating data that has a clear order. These grades provide a rank order of performance.
• Customer Satisfaction Surveys: Responses like ‘very unsatisfied’, ‘unsatisfied’, ‘neutral’, ‘satisfied’, and ‘very satisfied’ are common examples of this type. These categories are used in Likert scales.
• Military Ranks: Positions such as private, corporal, sergeant, and lieutenant have an inherent hierarchy clearly defined.

See More Examples of Ordinal Variables Here

Binary Data

Binary data offers exactly two possible outcomes, generally represented as ‘0’ and ‘1’ or ‘true’ and ‘false’.

Binary data is simple and straightforward, making it easy to manage, record, and analyze due to only having two possible answers.

Nonetheless, binary data can be limiting. Its binary nature may oversimplify complex situations and cannot handle factors that require more nuanced responses or rankings.

Examples of binary data include:

• Gender (Traditional): The traditional classification of gender falls into the duality of male or female.
• Survey Responses: When survey questions prompt a ‘yes’ or ‘no’ response, they create dual possibilities.
• Light Switch: A light switch exists in one of two states: ‘on’ or ‘off’.

See More Examples of Binary Variables Here

Qualitative Data Collection Methods

To collect qualitative data, researchers usually work with a smaller cohort of research participants with the intention of achieving deep insights rather than a breadth of statistical data. The more rich the data, the better.

While there is a wide range of qualitative research methods, some common collection methods are presented below.

• In-depth interviews: These interviews offer a researcher the opportunity to gain a comprehensive understanding of an individual’s perspective. Through one-on-one, conversation-based sessions, tailored questions can probe into specific areas of interest, granting thorough insights. Such an approach is fundamentally narrative, inviting interviewees to share their experiences and viewpoints on their terms.
• Focus groups: This refers to a collection of individuals assembled to discuss a specific topic. Leveraging the dynamics of group conversations, a variety of opinions and perspectives emerge causing themes to evolve and knowledge to be co-constructed. An experienced facilitator is essential to guide conversation, manage differing views, and ensure a broad yet relevant discussion.
• Observations : Researcher observations provide the means to directly engage with the subject of study within its natural context. Whether overt or covert, participant or non-participant, this method allows you to collect authentic data on behaviors, processes, or events as they occur. It’s insightful, yet requires careful planning and alertness to capture interactions objectively and comprehensively.
• Participant diaries or journals: Journals can deliver an intimate peek into the participants’ everyday lives. In these self-report methods, participants record their own experiences, emotions, and thoughts related to the research topic over a specific duration. This method offers a unique depth since data collected comes directly from those who are experiencing the topic of research in real-time.
• Content analysis : This involves a methodological examination of various forms of communication. From examining books, websites, and social media posts, to visual content like films or paintings, common themes or messages are classified, counted, and interpreted. As a flexible method, it allows for both qualitative and quantitative analysis as the nature of research dictates.
• Open-Ended Surveys: These allow research participants to freely express their responses to the questions posed. Instead of choosing from preset answers, participants can articulate their experiences, opinions, or feelings candidly. It surfaces a breadth of valuable insights, although it demands time-consuming analysis to properly categorize, interpret, and frame a vast array of personal narratives.

How to Analyze Qualitative Data

Qualitative data analysis can be notably demanding given its unstructured nature, often involving steps that ask for mental acuity, creativity, and an intimate familiarity with your material – which is a collection of words, images, or objects.

A common method is presented below:

• Start by organizing your data. Responses from interviews, focus group discussions, observations, and other sources need to be transcribed, categorized, and stored systematically. Digital tools can assist in handling large data sets and keep your data orderly.
• Thematic analysis. Thematic analysis should then be undertaken to identify common patterns across your data set, known as themes. Themes, vital to your research question, may underpin large sections of your data or may feature infrequently but are significant in their occurrence. Careful critical reading, coding, and memo-writing help extract these latent themes.
• Contextualize themes. Consider the broader socio-cultural context, participants’ demographics, or the specific conditions during data collection when interpreting these themes. Detailing the context accurately ensures a clear, well-rounded understanding of your findings.
• Present your findings. While you narrate the story of your data using the themes you identified, ensure to substantiate your interpretations with quotes, images, or examples from the data. This strengthens the validity of your interpretations and helps readers connect more intimately with your participants’ experiences.

Remember, qualitative analysis is iterative and reflexive. Your understanding may evolve as you deep-dive into the material, hence, you may need to revisit data, reshuffle themes, or reconsider contexts multiple times. Being open to these changes is essential to produce a rigorous, nuanced understanding of your qualitative findings.

Importance of Qualitative Data

Qualitative data is crucial for generating a deep and detailed understanding of a given topic.

Some of the main benefits of qualitative data, demonstrating its importance, include:

• Delivers depth of understanding. It provides rich, detailed, and deep insights into people’s behaviors, emotions, and perceptions. It delves into their motivations, beliefs, experiences and can uncover underlying reasons for these variables.
• Contextual specificity. It offers contextual information about phenomena examined, including the ‘why’ and ‘how’ behind the patterns observed. Therefore, responses or interpretations are understood within a specific context and don’t risk being decontextualized.
• Flexibility. Qualitative data collection methods are usually flexible, allowing adjustments in research design to explore emerging phenomena, unexpected outcomes, or nuanced sub-contexts in the field.
• Insights into complexity and subjectivity. As it appreciates human subjectivity, it allows for exploring complex, multifaceted experiences which are not easily quantifiable. It acknowledges the dynamism of social reality without reducing it to a fixed set of variables.
• End-user perspective. As it prioritizes respondents’ voices, experiences, and frames of understanding, it is vital in fields like market research, UX/UI design, or policy-making where the end user’s perspective is of prime importance.

Qualitative data, therefore, serves as the backbone of a comprehensive understanding, providing not just the facts but also the intricate stories, emotions, and meanings behind the facts that make up human experiences.

Qualitative vs Quantitative Data

Qualitative and quantitative data play distinct, yet equally critical roles in research.

In essence, qualitative research, drawn from dialogues, open-ended responses, or observations, works to unveil the ‘why’ and ‘how’ of human behavior. They tell a vivid, detailed story, unpacking intricate experiences, motivations, or evolving dialogues with profound depth and contextuality.

Quantitative data, numerical in nature, gathered from close-ended responses, tests, or predefined scales summarize ‘what’, ‘where’, ‘when’, or ‘who’ components of a phenomenon. They deal with quantities, facilitating precise measurement, comparison, or statistical inquiry, presenting a clear, concise picture that is generalizable and objective.

Their combined usage, known as mixed methods, offers a well-rounded understanding of a research problem , melding the depth of qualitative data with the precision of quantitative data.

Before you Go

For a well-rounded understanding of data analysis , I recommend reading my detailed article on quantitative data examples .

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Green Flags in a Relationship
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Signs you're Burnt Out, Not Lazy
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Toxic Things Parents Say to their Children

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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 ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

Qualitative data examples to ground your understanding

Last updated

14 February 2024

Reviewed by

Miroslav Damyanov

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While numbers paint a clear picture, qualitative data adds depth and nuance, revealing a spectrum of perspectives that numbers alone can't capture. To truly harness the power of qualitative research , you must navigate this 'gray area' with precision, understanding not only what qualitative data is but also how it can unlock invaluable insights that shape strategies and outcomes in ways metrics alone cannot.

In this article, we will unravel the potential of qualitative data by delving into what it entails, effective collection methods, and its advantages and disadvantages.

Read on to explore the dynamics of qualitative research and data, with examples to ground your understanding and application.

• What is qualitative data?

This type of data represents concepts, beliefs, and information not represented by numbers alone. They are insights gathered from people, usually in interviews and focus groups . 

Qualitative data can come from anywhere, including maps, photos, observations, diaries, and lab notes. However, it typically represents preferences, opinions, and beliefs from a subjective perspective, not rooted in numbers as you'd see with quantitative datasets.

Characteristics of qualitative data include:

Subjective: influenced by opinions, preferences, beliefs, and feelings

Open-ended: without limits or boundaries 

Descriptive: describing something in a non-judgmental manner

Detailed: shared accounts with details and sentiments

Non-numerical: not relating to numbers or currency

• Importance of qualitative data

During your research endeavors, you'll collect data to help you understand the "what" and "how." Quantitative data , rooted in numbers, can help you.

Qualitative data will help you understand the "why," shedding light on the reasons and context behind actions. It will reveal why and how something occurred, pointing out behavioral or preference-based factors. These elements are essential to any research or project, especially those related to business and decision-making. Qualitative data can be extremely powerful in transforming your processes and methodologies.

Here's an example. An eCommerce store owner can see the quantitative data in sales reports that show which products are top sellers. But to understand why more customers bought a particular product, qualitative research is needed. Surveys or pop-up questions asking for product feedback can help the store owner learn the motivating factors behind the purchase. That qualitative data (the "why") helps the owner to make informed decisions about how to make other products more appealing.

• Advantages of qualitative data

Researchers and teams turn to qualitative data for many reasons. It captures data you can't otherwise gather with quantitative research .

Key advantages of qualitative data include:

Explores behaviors beyond the numbers

Allows for in-depth attitude and preference analysis

Provides data-collection flexibility with interviews and focus groups, rather than predefined and structured variables

Offers a holistic understanding of unique projects and research

Encourages theory development and assessment

Provides affirmation and credibility

Appeals to exploratory research endeavors

Allows for observation in real-world scenarios

• Disadvantages of qualitative data

As beneficial as qualitative data is for some projects, there are a few disadvantages to consider. Recognize the limitations of qualitative data so you can properly manage expectations and parameters.

The disadvantages of qualitative data include:

Sample sizes of groups or individuals can be an issue

Possible bias in the sample selection

Impartiality and data accuracy can be a challenge

Qualitative research is often more time-consuming

It's also hard to replicate datasets

There is potential for researcher bias

Qualitative data can be difficult to measure

Outliers can be over-emphasized

It lacks the structure commonly found in quantitative data

• Types of qualitative data

Qualitative data typically falls into three categories:

Binary: organized into two categories, usually yes/no or true/false

Nominal: various data by category, with no meaningful association, like choosing colors or favorite movies

Ordinal: categories with a meaningful order but lacking a consistent interval between the categories, e.g. customer satisfaction ratings or levels of education

These data types can be collected through various research methods , including:

Case studies : researching a business application outcome

Focus groups : gathering insights from a test group of people

Observation: collecting data as an observer within an environment

Ethnography : studying people, cultures, and traditions

Narratives: evaluating people's stories and experiences

Interviews: seeking individual feedback and opinions

• Methods of qualitative data analysis

There are five techniques to consider as you decide which research projects and business applications will benefit from qualitative data analysis . Based on your research objectives, explore which of these research techniques could be most effective:

Content analysis : examines the presence of subjects, words, and concepts

Narrative analysis : interprets stories, testimonials, and interviews

Thematic analysis : identifies, categorizes, and interprets data based on themes and patterns

Discourse analysis : studies the underlying meaning of qualitative data, including observations and context

Grounded theory analysis:  uses real-world data to develop theories

• Examples of qualitative data

Explore these real-life examples of qualitative data resources and methods. Discover which might apply best to your projects and business model so you can learn more about the "why" and "how" of key experiences and processes.

Interviews 

Imagine your company has recently undergone significant structural changes, shifting employee responsibilities, or direction changes with a core offering. Research will determine if these changes are beneficial and will improve productivity and boost the company culture . As part of that research, you could gather qualitative data from employee interviews. 

These interviews seek to understand how employees perceive and experience the company changes. The qualitative data you could draw from their interview responses includes:

Common patterns related to challenges or shared experiences

Quotes or narratives that highlight employee perspectives

Emotional responses to the company change

Field notes 

If you're studying the dynamics of a particular community as part of an ethnographic project, qualitative data in field notes can be insightful. Whether you're studying interactions, cultural practices, or community events, the field notes are your primary method of data collection. 

Field notes can be used as part of your qualitative data analysis to uncover:

Observations from the field that highlight key aspects

Participation rates of community members

Interactions that support community identity

Textual data 

Some of the most common methods for collecting qualitative data are open-ended surveys. Including in-person paper surveys and anonymous or digital questionnaires , surveys are pivotal in how today's businesses and researchers learn about their industries and subjects.

Using open-ended questions , you can collect opinions, beliefs, and sentiments in the participants' own words.

These textual data responses are essential for:

Sentiment analysis

Contextual understanding

Identifying patterns and themes

Visual data 

Visual data in qualitative analysis can include photos and videos as the data collection method. For researchers who study the environment, for example, the visual data collected from field studies is pivotal. These visual perspectives can help researchers document changes, curate mapping, and spot challenges when comparing today's visuals to previous ones. 

When analyzing visual datasets, you can learn a host of details, including:

Symbolic interpretation

Spatial relationships

Visual patterns and themes

Any qualitative survey data collected over the phone would be an example of audio data. Researchers studying the experiences and perspectives of people with certain medical conditions might use these types of data collection methods. For instance, a researcher might record an interview with a participant, asking them to describe emotional or physical conditions.

Audio data can be great for analyzing more than just a participant's response. It can be used for:

Transcribing responses for reporting

Analyzing emotional tones and non-verbal cues

Narrative analysis of a person's complete journey

• How to analyze qualitative data 

Once you’ve decided which quantitative data methods align best with your project or research goals, you'll need to collect and analyze the findings. To help make the most of your qualitative data responses, follow these five steps for in-depth analysis success.

Keep in mind that qualitative data analysis is an iterative process, requiring more flexibility than with numeric, quantitative data.

Step 1: Arrange your data

Gather your transcriptions, documents, notes, and interview responses. Sift through to separate the valid from the invalid, and arrange your data according to your demographics or pre-determined participant categories.

Step 2: Organize all your data

Mark the sources of your data and organize the notes and responses according to your research or project parameters. Sort the "yes" responses from the "no" responses.

Spend time reading (and rereading) the data to gain an in-depth understanding, keeping notes that may help you with the next step.

Step 3: Apply codes to the data collected

Create codes to guide the official categorization process. Make notes in the margins and use concept mapping and other approaches to help you code the various elements of your findings. Coding, or sorting themes and patterns, will help you evaluate the results in a more organized way.

Step 4: Validate your qualitative data

Using your codes, identify any underlying themes, opinions, language, or beliefs. Continued review of your codes may require some revisions but ultimately will help you funnel the data into themes and official categories of results. Researchers often leverage constant comparisons between new data and codes and existing ones. 

Step 5: Concluding the analysis process

Use your coded categories and themes to draw data-driven conclusions. You can then present your findings, along with the study's purpose and parameters, to key stakeholders .

The qualitative data analysis should tell a cohesive story that addresses pre-study questions and provides answers. Software solutions can help you develop final presentable findings.

• Trust qualitative data to help your decision-making

Start tapping into the power of qualitative data to help you reach your research, business, and project goals. Knowing how to collect, analyze, and interpret these insights can be ground-breaking for your teams.

Having a deeper understanding of what qualitative data and research can offer will allow you to apply precision to your data-driven decision-making . And the insights gleaned from these datasets can revolutionize  how  you make those critical decisions, setting you up for success.

What are examples of good qualitative research questions?

These questions are great examples of open-ended qualitative research queries:

How would you describe your recent online experience?

Describe a time you experienced discomfort.

What areas of improvement would you suggest?

How can you avoid bias in qualitative research?

Several strategies can help you avoid introducing bias to your qualitative research project. These include:

Diversity in participant selection

Audit trails of decision-making

Triangulation of findings

Peer briefing before research

Reflexivity in acknowledging your biases and preconceptions

What are some quick-reference examples of qualitative data in action?

From software startups and scientific applications to backyard restaurant management and human resources oversight, there's a reason to explore qualitative research practically everywhere.

Here are a few more examples of qualitative data at work:

Location, origin, and gender collected for a census

Name, position, and event experience of a conference-goer for follow-up

Weight, height, and body types for a clothing size chart

User feedback about a newly launched software solution

Customer-experience survey responses to help improve a company's customer service policy

What are the core differences between qualitative and quantitative data?

While both quantitative and qualitative data provide value for research, there are primary differences between the two.

Quantitative data is fixed, countable, and related to numbers

Qualitative data is individualized, descriptive, and subjective

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative research examples

UserTesting

Qualitative research is a powerful tool that helps you unlock insights into the user experience—quintessential to building effective products and services. It provides a deeper understanding of complex behaviors, needs, and motivations. But what is qualitative research, and when is it ideal to use it? Let’s explore its methodologies and implementation with a few qualitative research examples.

What is qualitative research?

Qualitative research is a behavioral research method that seeks to understand the undertones, motivations, and subjective interpretations inherent in human behavior. It involves gathering nonnumerical data, such as text, audio, and video, allowing you to explore nuances and patterns that quantitative data can’t capture.

Instead of focusing on how many or how much, qualitative research questions delve into the why and how. This approach is instrumental in gaining a comprehensive understanding of a particular context, issue, or phenomenon from the perspective of those experiencing it. Examples of qualitative research questions include “How did you feel when you first used our product?” and “Could you describe your experience when you purchased a product from our website?”

Qualitative research methodology

Qualitative research design employs a variety of methodologies to collect and analyze data. The primary objective is to gather detailed and nuanced insights rather than generalizable findings. Steps include the following:

• Formulating research questions:  Qualitative research begins by identifying specific research questions to guide the study. These questions should align with the research objectives and provide a clear focus for data collection and analysis.  
• Selection of participants:  Participant selection is a critical step in qualitative research. You must recruit participants who provide relevant and diverse perspectives on the research topic. It involves purposive sampling, where participants are chosen based on their knowledge or experiences related to the research questions. ​​​​​​
• Data collection:  Qualitative research uses various methods to collect data, such as interviews, focus groups, observation, and document analysis. You often employ multiple methods to comprehensively understand the research topic.
• Data analysis:  Once the data is collected, it’s analyzed to identify recurring themes, patterns, and meanings. This analysis uses coding, thematic analysis, and constant comparison. The goal is to uncover the underlying perspectives of the participant.
• Interpretation and reporting:  This is the final step in which findings are synthesized and interpreted, revealing their significance to the research questions. You can present your findings through descriptive narratives, quotes, and illustrative examples to provide a rich understanding of the research topic. 

Types of qualitative research methods

The best qualitative research method primarily depends on your research questions and objectives. Different methods uncover different discernments.

One-on-one interviews

You often use one-on-one interviews to delve deep into a topic or understand individual experiences or perspectives. An interviewer asks a participant open-ended questions to understand their perspective, thoughts, feelings, and experiences regarding a specific topic, product, or service. Read about open ended vs closed ended questions to learn which questions will be most effective in an interview.

Say you’re developing a new electric vehicle mode. You can conduct one-on-one interviews to understand user experiences, probing into aspects such as comfort, design, driving experience, and more.

Focus groups

In-person or remote focus groups involve a small group of people (usually 6–10) discussing a given topic or question under the guidance of a moderator. This method is beneficial when you want to understand group dynamics or collective views. The interaction among group members can disclose awarenesses that may not arise in one-on-one interviews.

In the gaming industry, for example, you can use focus groups to explore player reactions to a new game design. You can encourage group interaction to spark discussions about usability, game mechanics, graphics, storyline, and other aspects.

Case study research

Case study research provides an in-depth analysis of a particular case (an individual, group, organization, event, etc.) within its real-life context. It’s a valuable method for exploring something in-depth and in its natural setting.

For instance, a healthcare case study could explore implementing a new electronic health record system in a hospital, focusing on challenges, successes, and lessons learned.

Ethnographic research

Ethnographic research (or an ethnographic stud y) involves an immersive investigation into a group’s behaviors, culture, and practices. It requires you to engage directly with the participants over a prolonged period in their natural environment. It can help uncover how people interact with products or services in natural settings.

A gaming organization may choose to study players in their natural gaming environments (such as home, game cafes, or e-sport tournaments) to understand their gaming habits, social interactions, and responses to specific features. These insights can inform the development of more engaging and user-friendly games.

Process of observation

The process of observation typically doesn’t involve the same level of immersion as ethnographic research. You observe and record behavior related to a specific context or activity. It can be in natural settings (naturalistic observation) or a controlled environment. It’s more about observing and recording specific behaviors or situations rather than cultural norms or dynamics.

For example, a consumer technology organization could observe how users interact with a new software interface, noting challenges, efficiencies, and overall user experience.

Record keeping

Record keeping refers to collecting and analyzing documents, records, and artifacts that provide an understanding of the study area. Record keeping allows you to access historical and contextual data that can be examined and reexamined. It’s a nonobtrusive method, meaning it doesn’t involve direct contact with the participants, nor does it affect or alter the situation you’re studying.

An online retailer might examine shopping cart abandonment records to identify at what point in the buying process customers tend to drop off. This information can help streamline the checkout process and improve conversion rates.

Qualitative research: Data collection and analysis

Data collection and analysis in qualitative research are closely linked processes that help generate meaningful and useful results.

Data collection

Data collection involves gathering rich, detailed materials to explain and understand the subject. These include interview transcripts, meeting notes, personal diaries, and photographs. 

There are various qualitative data collection methods to consider depending on your research questions and the context of your study. For example, you could use one-on-one interviews to understand personal user experiences with a financial services app. A moderated focus group may be more appropriate to discuss user preferences in a new media and entertainment platform.

Data analysis

Once data are collected, the analysis process begins. It’s where you extract patterns, themes, and insights from the collected data. It’s one of the most critical aspects of qualitative research, turning raw, unstructured data into valuable insights.

Qualitative data analysis usually takes place with several steps, such as:

• Organizing and preparing the data for analysis
• Reading through the data
• Coding the data
• Generating themes or categories
• Interpreting the findings and 
• Representing the data

Your choice of qualitative data analysis method depends on your research questions and the data type you collected. Common analysis methods include thematic, content, discourse, and narrative analysis. Some research platforms provide AI features that can do much of this analysis for researchers to speed up insight gathering.

When to use qualitative research

Qualitative techniques are ideal for understanding human experiences and perspectives. Here are common situations where qualitative research is invaluable:

• Exploring customer motivations, needs, behaviors, and pain points
• Gathering in-depth user feedback on products and services
• Understanding decision-making and buyer journeys
• Discovering barriers to adoption and satisfaction
• Developing hypotheses for future quantitative research
• Testing concepts , interfaces, or designs
• Identifying problems and improvement opportunities
• Learning about group norms, cultures, and social interactions
• Collecting evidence to develop theories and models
• Capturing complex, nuanced insights beyond numbers

Qualitative research methods vs. quantitative research methods

Qualitative and quantitative research  differ in their approach to data collection, analysis, and the nature of the findings. Here are some key differences:

• Data collection:  Qualitative research uses in-depth interviews , focus groups, observations, and analysis of documents to gather data. In contrast, quantitative research relies on structured surveys, experiments, and standard measurements.
• Analysis:  Qualitative research involves analyzing textual or visual data through coding, categorization, and theme identification techniques. Quantitative research uses statistical analysis to examine numerical data for patterns, correlations, and trends.
• Sample size:  Qualitative research typically involves smaller sample sizes, often selected through purposive sampling to ensure diversity and relevance. Quantitative research uses larger sample sizes to ensure statistical power and generalizability.
• Generalizability:  Qualitative research seeks in-depth insight into specific contexts or groups and does not prioritize generalizability. On the other hand, quantitative research seeks to draw conclusions that apply to a broader context.
• Findings:  Qualitative research generates descriptive and explanatory results that provide a deeper understanding of phenomena. Quantitative research produces numerical data that allows for statistical inferences and comparisons.
• Theory development:  Qualitative research often contributes to theory development by generating new concepts, theories, or frameworks based on the rich and context-specific data collected. However, quantitative research tests preexisting theories and hypotheses using statistical models.

Advantages and strengths of qualitative research

Qualitative research enriches your research process and outcomes, making it an invaluable tool in many fields, including UX research, marketing, and digital product development. 

In-depth understanding

Qualitative research provides a rich, detailed, in-depth understanding of the research subject.  Proactive qualitative research  takes this further with ongoing data collection, allowing organizations to continuously capture insights and adapt strategies based on evolving user needs.

Contextual data

Qualitative research collects contextually relevant data. It captures nuances that might be missed in numerically-based quantitative data, allowing you to understand the contexts in which behaviors and interactions occur.

Flexibility

The methods used in qualitative research, like interviews and focus groups, enable you to explore different topics in depth and adapt your approach based on the participants’ responses.

Human perspective

Qualitative research lets you capture human experiences and thoughts. It’s advantageous in fields such as UX research, where the human perspective is critical. 

Hypothesis generation

The exploratory nature of qualitative research helps you identify new areas for exploration or generate hypotheses you can test using quantitative methods.

Trendspotting

Qualitative research reveals trends in thought and opinions, diving deeper into the problem. This is helpful when trying to understand behaviors, culture, and user interactions.

Disadvantages and limitations of qualitative research

While qualitative research offers many advantages, it’s essential to acknowledge its limitations. 

Time-consuming

Collecting and analyzing qualitative data, particularly from in-depth interviews or focus groups, requires significant time investment.

Qualitative research relies on the skills and judgment of the researcher, introducing potential bias into the research process. The researcher may actively shape the research by posing questions, interpreting data, and influencing the findings.

Requires skilled researchers

The quality of qualitative research heavily depends on the researcher’s skills, experience, and perspective. A less experienced researcher may overlook important nuances, potentially affecting the depth and accuracy of the findings.

Lacks generalizability

Qualitative research often involves a smaller, nonrepresentative sample size than quantitative research. Therefore, the findings may not be generalizable to a larger context.

Limited numeric representation

Qualitative research usually focuses on words, observations, or experiences, so it doesn’t provide the numeric estimates often desired in research studies.

Challenging to replicate and standardize

Qualitative research’s inherent flexibility and context dependence make it challenging to repeat the study under the same conditions. This flexibility can often make it hard to standardize. Researchers approach and conduct the study in various ways, leading to inconsistent results and interpretations.

Difficult to measure reliability and validity

Assessing reliability and validity is more difficult with qualitative research since it relies on subjective human interpretation and has few established metrics and statistical tools compared to quantitative research. Triangulation and member checking add credibility but lack the discreteness of quantitative measures. However, there have been advancement s in the measurement of qualitative research that help to quantify its impact. 

Qualitative research gives you the opportunity to dive deep into human behavior, experiences, and perceptions. It offers a prolific, intricate perspective that quantifiable data alone can’t provide. Combine qualitative research methodologies with techniques like  A/B testing  to gain a more holistic understanding of user experiences and preferences. 

Despite its limitations, the depth and richness of data procured through qualitative research are undeniable assets. By understanding and utilizing its diverse methods, you will uncover detailed insights from your target audience and enhance your products or services to meet their needs. 

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Why is qualitative research important?

Qualitative research delves into subjective experiences and social contexts, providing in-depth insights and understanding. It provides a deep understanding of individuals’ needs, motivations, and preferences, allowing organizations to develop products and services that meet customer expectations.

What’s the difference between quantitative and qualitative methods?

Quantitative methods focus on numerical data and statistical analysis, aiming for generalizability and objectivity. Qualitative methods explore meanings, experiences, and behaviors, seeking in-depth understanding and detailed descriptions.

What are the main qualitative research approaches?

The main qualitative research approaches include one-on-one interviews, focus groups, case study research, ethnographic research, observation, and record-keeping. Each approach offers unique benefits and applications.

What is data collection?

Data collection in qualitative research involves gathering information through various methods such as interviews, focus groups, observations, and document analysis. It’s a critical step in generating meaningful insights and understanding human experiences.

How do you analyze qualitative data?

What are the ethical considerations in qualitative research.

Ethical considerations refer to the protection of participants’ rights, privacy, and confidentiality. You must obtain informed consent, maintain anonymity, and handle sensitive information responsibly. Additionally, maintaining transparency, addressing power imbalances, and conducting research unbiased and respectfully are vital ethical considerations in qualitative research.

How can I incorporate qualitative research into my study or project?

To incorporate qualitative research into your study, you must first define your research objectives to guide the choice of methodology. Next, choose a suitable qualitative method, such as interviews or focus groups. Then, collect and analyze the data using appropriate techniques and, finally, interpret and present the findings clearly and meaningfully. Remember to be mindful of the ethical considerations throughout the process.

How do you effectively communicate and present qualitative research findings to stakeholders?

For a quality presentation, create engaging visual representations, such as infographics or data visualizations, and use storytelling techniques to highlight key insights. Also, prepare concise and informative reports and organize interactive presentations or workshops to facilitate discussion and understanding.

How do you translate qualitative research findings into actionable insights?

Identify key themes linked to research goals and propose strategic solutions to address core needs and barriers. These solutions should be tailored to specific needs.

How can I ensure the validity and reliability of qualitative research findings?

About the author(s).

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10 Examples of Qualitative Data

Anything that involves feedback which has no direct quantitative measure against a numeric scale is qualitative data. Qualitative data is categorical. It can be used to analyze language, and through this, to develop an understanding of subjective perception. Qualitative data describes characteristics. It is usually gathered from interviews, observations, surveys, or focus groups. It is used to categorize information based on specific attributes or properties which can be verified.

Qualitative or categorical data can be ordinal (ordered on degrees of, or ranked scales), or Nominal (for example gender or demographic information). The important thing is that the categories should be mutually exclusive and should not overlap.

Here are ten examples of qualitative data:

Observation Notes

Observation is an important method of qualitative data collection. Observation may be guided by a semi-structured assessment tool, guiding what is being observed, by asking key questions around what is taking place. It is important however, not to lead the observer, by defining what to look for.

Semi-structured interviews

Semi-structured interviews are qualitative data collection tools which allow researchers to ask informants questions around a series of predetermined themes but allowing for open-ended responses. These are useful tools in qualitative research, as they open the space for both inductive and deductive reasoning in evaluation. The structure of the questions allows the researcher to explore key themes pertaining to the underlying theory; The Theory of Change, or the Conceptual Framework of the project, while the open-ended nature of the questions created the space for explanations as to why the hypothesis might have been disproved, and what occurred instead.

Open-ended survey

Surveys are useful tools for measuring how preferences or perceptions change over time, or to gather information on key experiences of informants. Surveys may make use of ranked scales such as ‘always, sometimes, never’, or a ranked scale of from strongly agree to strongly disagree. These are useful tools for not only measuring perception, but behavioral change. Creating space for comments, or thoughts creates space for deeper reflection, and as in semi-structured interviews explored above, can allow for the research to uncover pathways for change which were not considered in the design of the programme logic, but which better explain what took place and why.

Participant diaries or journals

Using diaries or journals, one could either use concept maps (explored below) to map out themes arising consistently, or if the researcher already has a theory around relevant themes, one could conduct a thematic analysis in order to evaluate whether the described causation, or the flow from thoughts, through motivations to actions is as hypothesized. Particularly in small programmes, where the research is involved in the subjective experience of participants, asking them to keep a journal of their thoughts and experienced can help to build an understanding of what is taking place, and hoe informants can be supported through a process of changing negative outcomes.

Portfolios of evidence

Portfolios of evidence are a little more robust than journals and can be used to evaluate pieces of technical work, rather than just thoughts and feelings. It should be noted that where work is marked and assessed (there are right and wrong answers, and participants receive a score), this becomes quantitative data. However, when assessing creative projects, such as lesson plans in projects to improve pedagogy, or seasonal agricultural plans, or school improvement plans, these do not have clear write and wrong answers, but can be of carrying quality. These can be assessed as to whether they meet a set of key criteria, or whether they are workable in a given circumstance. These are useful records of personal-professional development.

Concept Maps

Concept Mapping provides a visual representation of how ideas held by informants link together. This is information formed from an analysis of data on these ideas as reported by informants. This is an example where deductive reasoning is used, where information gathered in analyzed for patterns, from which a tentative hypothesis is derived and tested, and from which theories can then be built. Concept Maps are frequently used in areas of action research, or participatory research and decision-making.

Case Studies

Case studies involve detailed deep dive information, usually focuses on a single case, or a small number of cases. These are particularly relevant where the researcher aims to gather rich and detailed information. These are intensive studies which will focus on context and the depth of change. Case studies, if done systematically, can generate bodies of data which can, in turn, be used for qualitative comparative analysis to analyze causation using Boolean Algebra . Case studies can combine a range of data collection methods, and points to draw a really comprehensive picture

Focus Groups

Focus groups are usually conducted with 6-10 people, and involve group discussions which are moderated. Discussion around particularly themes in a discussion context can assist with gathering information which confirms, or disproves theories around a specific theme. Focus groups can also be used effectively in analyses of process or implementation to advise on implementation changes required to improve programme efficacy.

Video and recordings

Qualitative data, particularly that used for narrative or discourse analysis can be found in film records of events, recording of interview, or video diaries. Times when this form of data is most useful is either when the generation of it forms part of the programme activities by participants, or when it is easier to be present in the moment, than to be working with pen, paper, or screen. This allows researchers to revisit key themes, and in the case of video, to make other observations about surroundings, noise levels, environments, or body language.

Qualitative Audits

These are especially useful in scoping prior to project commencement, such as in conducting needs or gap analyses. By way of example, a researcher has conducted research into what an ideal learning environment contains, and aims to conduct an audit of treatment schools to ascertain what improvements should be built into the outcomes targets by school. Categorizing cleanliness, library access rules, or the size of a room could all be included in such an audit.

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Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

Qualitative UX research made easy

Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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.

• 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 numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

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

• Mixed methods research
• 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

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Understanding Qualitative and Quantitative Data

• 7 minute read
• August 22, 2024

Written by:

Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science

Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights.

Introduction

In the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts.

This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis.

Read More:   Exploring 5 Statistical Data Analysis Techniques with Real-World Examples

Defining Qualitative Data

Qualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject.

This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups.

Characteristics of Qualitative Data

• Descriptive : Qualitative data provides rich, detailed descriptions of phenomena, allowing researchers to capture the complexity of human experiences.
• Subjective : The interpretation of qualitative data can vary based on the researcher’s perspective, making it inherently subjective.
• Contextual : This type of data is often context-dependent, meaning that the insights gained can be influenced by the environment or situation in which the data was collected.
• Exploratory : Qualitative data is typically used in exploratory research to generate hypotheses or to understand phenomena that are not well understood.

Examples of Qualitative Data

• Interview transcripts that capture participants’ thoughts and feelings.
• Observational notes from field studies.
• Responses to open-ended questions in surveys.
• Personal narratives or case studies that illustrate individual experiences.

Defining Quantitative Data

Quantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data.

Characteristics of Quantitative Data

• Objective : Quantitative data is generally considered more objective than qualitative data, as it relies on measurable values that can be statistically analysed.
• Structured : This type of data is often collected using structured methods such as surveys with closed-ended questions, experiments, or observational checklists.
• Generalizable : Because quantitative data is based on numerical values, findings can often be generalised to larger populations if the sample is representative.
• Statistical Analysis : Quantitative data lends itself to various statistical analyses , allowing researchers to draw conclusions based on numerical evidence.

Examples of Quantitative Data

• Age, height, and weight measurements.
• Survey results with numerical ratings (e.g., satisfaction scores).
• Test scores or academic performance metrics.
• Financial data such as income, expenses, and profit margins.

Key Differences Between Qualitative and Quantitative Data

Understanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions:

When to Use Qualitative Data

Qualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice:

Exploratory Research

When investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses.

Understanding Context

Qualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis.

Gaining Insights into Attitudes and Behaviours

When the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights.

Developing Theories

Qualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook.

When to Use Quantitative Data

Quantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice:

Testing Hypotheses

When researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses.

Measuring Variables

Quantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys.

Generalising Findings

When the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions.

Identifying Patterns and Trends

Quantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development.

Integrating Qualitative and Quantitative Data

While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights.

Benefits of Integration

Integrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.

• Enhanced Validity: By using both qualitative and quantitative data, researchers can validate their findings through multiple sources of evidence.
• Rich Insights : Qualitative data can provide context and depth to quantitative findings, helping to explain the “why” behind numerical trends.
• Comprehensive Understanding: Integrating both types of data allows for a more holistic understanding of complex phenomena, leading to more informed conclusions and recommendations.

Examples of Integration

• Surveys with Open-Ended Questions: Combining closed-ended questions (quantitative) with open-ended questions (qualitative) in surveys can provide both measurable data and rich descriptive insights.
• Case Studies with Statistical Analysis: Researchers can conduct case studies (qualitative) while also collecting quantitative data to support their findings, offering a more robust analysis.
• Focus Groups with Follow-Up Surveys: After conducting focus groups (qualitative), researchers can administer surveys (quantitative) to a larger population to validate the insights gained.

Challenges and Considerations

While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type:

Challenges of Qualitative Data

The challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data:

Subjectivity and Bias

One of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data.

Data Overload

Qualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked.

Lack of Structure

Qualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data.

Time-Consuming Nature

Qualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers.

Challenges of Quantitative Data

Quantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations.

Limits in Capturing Complexity

Quantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours.

Chances for Misinterpretation

Numbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data.

Influence of Measurement Errors

Due to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis.

Lack of Context

Quantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied.

Sample Size Limitations

Small sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations.

Confirmation Bias

Researchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights.

In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives.

By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making.

As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike.

Frequently Asked Questions

What is the primary difference between qualitative and quantitative data.

The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses.

When Should I Use Qualitative Data in My Research?

Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important.

Can Qualitative and Quantitative Data Be Used Together?

Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights.

Reviewed by:

Post written by: Smith Alex

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Qualitative Research Questionnaire – Types & Examples

Published by Alvin Nicolas at August 19th, 2024 , Revised On August 20, 2024

Before you start your research, the first thing you need to identify is the research method . Depending on different factors, you will either choose a quantitative or qualitative study.

Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences. Unlike quantitative research, which focuses on numerical data , qualitative research allows exploring and interpreting the experiences of the subject. Questionnaires, although mostly associated with quantitative research, can also be a valuable instrument in qualitative studies. Let’s explore what qualitative research questionnaires are and how you can create one.

What Is A Qualitative Research Questionnaire

Qualitative research questionnaires are a structured or semi-structured set of questions designed to gather detailed, open-ended participant responses. It allows you to uncover underlying reasons and opinions and provides insights into a particular phenomenon.

While quantitative questionnaires often have closed-ended questions and numerical responses, a qualitative questionnaire encourages participants to express themselves freely. Before you design your questionnaire, you should know exactly what you need so you can keep your questions specific enough for the participants to understand.

For example:

• Describe your experience using our product.
• How has technology impacted your work-life balance?

Types of Qualitative Research Questions With Examples

Now that you are familiar with what qualitative research questions are, let’s look at the different types of questions you can use in your survey .

Descriptive Questions

These are used to explore and describe a phenomenon in detail. It helps answer the “what” part of the research, and the questions are mostly foundational.

Example: How do students experience online learning?

Comparative Questions

This type allows you to compare and contrast different groups or situations. You can explore the differences and similarities to highlight the impact of specific variables.

Example: How do the study habits of first-year and fourth-year university students differ?

Interpretive Questions

These questions help you understand the meanings people attach to experiences or phenomena by answering the “how” and “why”.

Example: What does “success” mean to entrepreneurs?

Evaluative Questions

You can use these to assess the quality or value of something. These allow you to understand the outcomes of various situations.

Example: How effective is the new customer service training program?

Process-Oriented Questions

To understand how something happens or develops over time, researchers often use process-oriented questions.

Example: How do individuals develop their career goals?

Exploratory Questions

These allow you to discover new perspectives on a topic. However, you have to be careful that there must be no preconceived notions or research biases to it.

Example: What are the emerging trends in the mobile gaming industry?

How To Write Qualitative Research Questions?

For your study to be successful, it is important to consider designing a questionnaire for qualitative research critically, as it will shape your research and data collection. Here is an easy guide to writing your qualitative research questions perfectly.

Tip 1: Understand Your Research Goals

Many students start their research without clear goals, and they have to make substantial changes to their study in the middle of the research. This wastes time and resources.

Before you start crafting your questions, it is important to know your research objectives. You should know what you aim to discover through your research, or what specific knowledge gaps you are going to fill. With the help of a well-defined research focus, you can develop relevant and meaningful information.

Tip 2: Choose The Structure For Research Questions

There are mostly open-ended questionnaires in qualitative research. They begin with words like “how,” “what,” and “why.” However, the structure of your research questions depends on your research design . You have to consider using broad, overarching questions to explore the main research focus, and then add some specific probes to further research the particular aspects of the topic.

Tip 3: Use Clear Language

The more clear and concise your research questions are, the more effective and free from ambiguity they will be. Do not use complex terminology that might confuse participants. Try using simple and direct language that accurately conveys your intended meaning.

Here is a table to explain the wrong and right ways of writing your qualitative research questions.

Tip 4: Check Relevance With Research Goals

Once you have developed some questions, check if they align with your research objectives. You must ensure that each question contributes to your overall research questions. After this, you can eliminate any questions that do not serve a clear purpose in your study.

Tip 5: Concentrate On A Single Theme

While it is tempting to cover multiple aspects of a topic in one question, it is best to focus on a single theme per question. This helps to elicit focused responses from participants. Moreover, you have to avoid combining unrelated concepts into a single question.

If your main research question is complicated, you can create sub-questions with a “ladder structure”. These allow you to understand the attributes, consequences, and core values of your research. For example, let’s say your main broad research question is:

• How do you feel about your overall experience with our company?

The intermediate questions may be:

• What aspects of your experience were positive?
• What aspects of your experience were negative?
• How likely are you to recommend our company to a friend or colleague?

Types Of Survey Questionnaires In Qualitative Research

It is important to consider your research objectives, target population, resources and needed depth of research when selecting a survey method. The main types of qualitative surveys are discussed below.

Face To Face Surveys

Face-to-face surveys involve direct interaction between the researcher and the participant. This method allows observers to capture non-verbal cues, body language, and facial expressions, and helps adapt questions based on participant responses. They also let you clarify any misunderstandings. Moreover, there is a higher response rate because of personal interaction.

Example: A researcher conducting a study on consumer experiences with a new product might visit participants’ homes to conduct a detailed interview.

Telephone Surveys

These type of qualitative research survey questionnaires provide a less intrusive method for collecting qualitative data. The benefits of telephone surveys include, that it allows you to collect data from a wider population. Moreover, it is generally less expensive than face-to-face interviews and interviews can be conducted efficiently.

Example: A market research firm might conduct telephone surveys to understand customer satisfaction with a telecommunication service.

Online Surveys

Online survey questionnaires are a convenient and cost-effective way to gather qualitative data. You can reach a wide audience quickly, and participants may feel more comfortable sharing sensitive information because of anonymity. Additionally, there are no travel or printing expenses.

Example: A university might use online surveys to explore students’ perceptions of online learning experiences.

Strengths & Limitations Of Questionnaires In Qualitative Research

Questionnaires are undoubtedly a great data collection tool. However, it comes with its fair share of advantages and disadvantages. Let’s discuss the benefits of questionnaires in qualitative research and their cons as well.

Qualitative Research Questionnaire Example

Here is a concise qualitative research questionnaire sample for research papers to give you a better idea of its format and how it is presented.

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Are questionnaires quantitative or qualitative research?

A survey research questionnaire can have both qualitative and quantitative questions. The qualitative questions are mostly open-ended, and quantitative questions take the form of yes/no, or Likert scale rating. 

Can we use questionnaires in qualitative research?

Yes, survey questionnaires can be used in qualitative research for data collection. However, instead of a Likert scale or rating, you can post open-ended questions to your respondents. The participants can provide detailed responses to the questions asked.

Why are questionnaires good for qualitative research?

In qualitative research, questionnaires allow you to collect qualitative data. The open-ended and unstructured questions help respondents present their ideas freely and provide insights. 

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Research implications | Definition, Examples & Tips

Introduction

What are research implications, why discuss research implications, types of implications in research, how do you present research implications.

Every scientific inquiry is built on previous studies and lays the groundwork for future research. The latter is where discussion of research implications lies. Researchers are expected not only to present what their findings suggest about the phenomenon being studied but also what the findings mean in a broader context.

In this article, we'll explore the nature of research implications as a means for contextualizing the findings of qualitative research and the foundation it sets for further research.

Research implications include any kind of discussion of what a particular study means for its research field and in general terms. Researchers write implications to lay out future research studies, make research recommendations based on proposed theoretical developments, and discuss practical and technological implications that can be applied in the real world.

To put it another way, research implications are intended to answer the question "what does this research mean?". Research implications look forward and out. Once findings are presented and discussed, the researcher lays out what the findings mean in a broader context and how they could guide subsequent research.

An aspect of academic writing that's related to implications is the discussion of the study's limitations. These limitations differ from implications in that they explore already acknowledged shortcomings in a study (e.g., a small sample size, an inherent weakness in a chosen methodological approach), but these limitations can also suggest how future research could address these shortcomings. Both the implications and recommendations are often coupled with limitations in a discussion section to explain the significance of the study's contributions to scientific knowledge.

Strictly speaking, there is a fine line between limitations and implications, one that a traditional approach to the scientific method may not adequately explore. Under the scientific method, the product of any research study addresses its research questions or confirms or challenges its expected outcomes. Fulfilling just this task, however, may overlook a more important step in the research process in terms of demonstrating significance.

One of the more famous research examples can provide useful insight. Galileo's experiments with falling objects allowed him to answer questions raised by Aristotle's understanding about gravity affecting objects of different weights. Galileo had something of a hypothesis - objects should fall at the same speed regardless of weight - based on a critique of then-current scientific knowledge - Aristotle's assertion about gravity - that he wanted to test in research. By conducting different experiments using inclines and pendulums (and supposedly one involving falling objects from the Tower of Pisa), he established a new understanding about gravity and its relationship (or lack thereof) to the weight of objects.

Discussion of that experiment focused on how the findings challenged Aristotle's understanding of physics. It did not, however, pose the next logical question: Why would an object like a feather fall at a much slower rate of descent than an object like a hammer if weight was not a factor?

Galileo's experiment and other similar experiments laid the groundwork for experiments on air resistance, most famously the Apollo 15 experiment on the moon where a feather and hammer fell at the same rate in a vacuum, absent any air resistance. The limitation Galileo had at the time was the inability to create a vacuum to test any theories about gravity and air resistance. The implications of his experiments testing Aristotle's claims include the call to further research that could eventually confirm or challenge his understanding of falling objects.

In formal scientific research, particularly in academic settings where peer review is an essential component, contemporary researchers are supposed to do more than simply report their findings. They are expected to engage in critical reflection in placing their research findings in a broader context. The peer review process in research publication often assesses the quality of a research paper by its ability to detail the significance of a given research study. Without an explicit description of the implications in research, readers may not necessarily know what importance the study and its findings holds for them.

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Breaking down the kinds of implications that your research findings might have will be useful in crafting a clearer and more persuasive presentation. More important than saying that the findings are compelling is arguing in what aspects the findings should prove useful.

There are different types of implications, and the type you should emphasize depends on your target audience.

Theoretical implications

When research findings present novel scientific knowledge, it should have an influence on existing theories by affirming, contradicting, or contextualizing them. This can mean the proposal of a brand new theoretical framework or developments to a existing one.

Keep in mind that, in qualitative research , researchers will often contextualize a theory rather than confirm or refute it. This means that a theory or conceptual framework that is applied to an unfamiliar context (e.g., a theory about adolescent development in a study involving graduate students) will undergo some sort of transformation due to the new analysis.

New understandings will likely develop more complex descriptions of theories as they are interpreted and re-interpreted in new contexts. The discussion of theoretical implications here requires researchers to consider how new theoretical developments might be applied to new data in future research.

Practical implications

More applied forums are interested in how a study's findings can be used in the real world. New developments in psychology could yield discussion of applications in psychiatry, while research in physics can lead to technological innovations in engineering and architecture. While some researchers focus on developing theory, others conduct research to generate actionable insights and tangible results for stakeholders.

Education research, for example, may present pathways to a new teaching method or assessment of learining outcomes. Theories about how students passively and actively develop expertise in subject-matter knowledge could eventually prompt scholars and practitioners to change existing pedagogies and materials that account for more novel understandings of teaching and learning.

Exploring the practical dimensions of research findings may touch on political implications such as policy recommendations, marketable technologies, or novel approaches to existing methods or processes. Discussion of implications along these lines is meant to promote further research and activity in the field to support these practical developments.

Methodological implications

Qualitative research methods are always under constant development and innovation. Moreover, applying research methods in new contexts or for novel research inquiries can lead to unanticipated results that might cause a researcher to reflect on and iterate on their methods of data collection and analysis .

Critical reflections on research methods are not meant to assert that the study was conducted without the necessary rigor . However, rigorous and transparent researchers are expected to argue that further iterations of the research that address any methodological gaps can only bolster the persuasiveness of the findings or generate richer insights.

There are many possible avenues for implications in terms of innovating on methodology. Does the nature of your interview questions change when interviewing certain populations? Should you change certain practices when collecting data in an ethnography to establish rapport with research participants ? How does the use of technology influence the collection and analysis of data?

All of these questions are worth discussing, with the answers providing useful guidance to those who want to base their own study design on yours. As a result, it's important to devote some space in your paper or presentation to how you conducted your study and what you would do in future iterations of your study to bolster its research rigor.

Presenting research implications or writing research implications in a research paper is a matter of answering the following question: Why should scholars read or pay attention to your research? Especially in the social sciences, the potential impact of a study is not always a foregone conclusion. In other words, to make the findings as insightful and persuasive to your audience as they are to you, you need to persuade them beyond the presentation of the analysis and the insights generated.

Here are a few main principles to achieve this task. In broad terms, they focus on what the findings mean to you, what it should mean to others, and what those impacts might mean in context.

Establish importance

Academic research writing tends to follow a structure that narrates a study from the researcher's motivation to conduct the research to why the research's findings matter. While there's seldom a strict requirement for sections in a paper or presentation, understanding commonly used patterns in academic writing will point out where the research implications are discussed.

If you look at a typical research paper abstract in a peer-reviewed journal , for example, you might find that the last sentence or two explicitly establishes why the research is useful to motivate readers to look at the paper more deeply. In the body of the paper, this is further explained in detail towards the end of the introduction and discussion sections and in the conclusion section. These areas are where you should focus on detailing the research implications and explaining how you perceive the impact of your study.

It's essential that you use these spaces to highlight why the findings matter to you. As mentioned earlier, this impact should never be assumed to be understood. Rather, you should explain in detail how your initial motivation to conduct the research has been satisfied and how you might use what you have learned from the research in theoretical and practical terms.

Tailor to your audience

Research is partly about sharing expertise and partly about understanding your audience. Scientific knowledge is generated through consensus, and the more that the researcher ensures their implications are understood by their audience, the more it will resonate in the field.

A good strategy for tailoring your research paper to a particular journal is to read its articles for the implications that are explored in the research. Applied journals will focus on more practical implications while more theoretical publications will emphasize theoretical or conceptual frameworks for other scholars to rely on. As a result, there's no need to detail every single possible implication from your study; simply describing those implications that are most relevant to your audience is often sufficient.

Provide useful examples

One of the easier ways to persuade readers of the potential implications of your research is to provide concrete examples that are simple to understand.

Think about a study that interviews children, for example, where the methodological implications dwell on establishing an emotional connection before collecting data. This might include practical considerations such as bringing toys or conducting the interview in a setting familiar to them like their classroom so they are comfortable during data collection. Explicitly detailing this example can guide scholars in useful takeaways for their research design.

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