data analysis tools used in qualitative research

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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10 best qualitative data analysis tools

A lot of teams spend a lot of time collecting qualitative customer experience data—but how do you make sense of it, and how do you turn insights into action?

Qualitative data analysis tools help you make sense of customer feedback so you can focus on improving the user and product experience and creating customer delight.

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This chapter of Hotjar's qualitative data analysis (QDA) guide covers the ten best QDA tools that will help you make sense of your customer insights and better understand your users.

Collect qualitative customer data with Hotjar

Use Hotjar’s Surveys and Feedback widget to collect user insights and better understand your customers.

10 tools for qualitative data analysis 

Qualitative data analysis involves gathering, structuring, and interpreting contextual data to identify key patterns and themes in text, audio, and video.

Qualitative data analysis software automates this process, allowing you to focus on interpreting the results—and make informed decisions about how to improve your product—rather than wading through pages of often subjective, text-based data.

Pro tip: before you can analyze qualitative data, you need to gather it. 

One way to collect qualitative customer insights is to place Hotjar Surveys on key pages of your site . Surveys make it easy to capture voice-of-the-customer (VoC) feedback about product features, updated designs, and customer satisfaction—or to perform user and market research.

Need some ideas for your next qualitative research survey? Check out our Hotjar Survey Templates for inspiration.

Example product discovery questions from Hotjar’s bank of survey templates

1. Cauliflower

Cauliflower is a no-code qualitative data analysis tool that gives researchers, product marketers, and developers access to AI-based analytics without dealing with complex interfaces.

How Cauliflower analyzes qualitative data

Cauliflower’s AI-powered analytics help you understand the differences and similarities between different pieces of customer feedback. Ready-made visualizations help identify themes in customers’ words without reading through every review, and make it easy to:

Analyze customer survey data and answers to open-ended questions

Process and understand customer reviews

Examine your social media channels

Identify and prioritize product testing initiatives

Visualize results and share them with your team

One of Cauliflower’s customers says, “[Cauliflower is] great for visualizing the output, particularly finding relevant patterns in comparing breakouts and focussing our qualitative analysis on the big themes emerging.”

NVivo is one of the most popular qualitative data analysis tools on the market—and probably the most expensive. It’s a more technical solution than Cauliflower, and requires more training. NVivo is best for tech-savvy customer experience and product development teams at mid-sized companies and enterprises.

How NVivo analyzes qualitative data

NVivo’s Transcription tool transcribes and analyzes audio and video files from recorded calls—like sales calls, customer interviews, and product demos—and lets you automatically transfer text files into NVivo for further analysis to:

Find recurring themes in customer feedback

Analyze different types of qualitative data, like text, audio, and video

Code and visualize customer input

Identify market gaps based on qualitative and consumer-focused research

Dylan Hazlett from Adial Pharmaceuticals says, “ We needed a reliable software to perform qualitative text analysis. The complexity and features of [Nvivo] have created great value for our team.”

3. ​​Quirkos

Quirkos is a simple and affordable qualitative data analysis tool. Its text analyzer identifies common keywords within text documents to help businesses quickly and easily interpret customer reviews and interviews.

How Quirkos analyzes qualitative data

Quirkos displays side-by-side comparison views to help you understand the difference between feedback shared by different audience groups (by age group, location, gender, etc.). You can also use it to:

Identify keywords and phrases in survey responses and customer interviews

Visualize customer insights

Collaborate on projects

Color code texts effortlessly

One of Quirkos's users says, “ The interface is intuitive, easy to use, and follows quite an intuitive method of assigning codes to documents.”

4. Qualtrics

Qualtrics is a sophisticated experience management platform. The platform offers a range of tools, but we’ll focus on Qualtrics CoreXM here.  

Qualtrics CoreXM lets you collect and analyze insights to remove uncertainty from product development. It helps validate product ideas, spot gaps in the market, and identify broken product experiences, and the tool uses predictive intelligence and analytics to put your customer opinion at the heart of your decision-making.

How Qualtrics analyzes qualitative data

Qualtrics helps teams streamline multiple processes in one interface. You can gather and analyze qualitative data, then immediately share results and hypotheses with stakeholders. The platform also allows you to:

Collect customer feedback through various channels

Understand emotions and sentiment behind customers’ words

Predict what your customers will do next

Act immediately based on the results provided through various integrations

A user in project management shares, “The most useful part of Qualtrics is the depth of analytics you receive on your surveys, questionnaires, and other tools. In real-time, as you develop your surveys, you are given insights into how your data can be analyzed. It is designed to help you get the data you need without asking unnecessary questions.”

5. Dovetail

Dovetail is a customer research platform for growing businesses. It offers three core tools: Playback, Markup, and Backstage. For qualitative data analysis, you’ll need Markup.

Markup offers tools for transcription and analysis of all kinds of qualitative data, and is a great way to consolidate insights.

How Dovetail analyzes qualitative data

Dovetail’s charts help you easily quantify qualitative data. If you need to present your findings to the team, the platform makes it easy to loop in your teammates, manage access rights, and collaborate through the interface. You can:

Transcribe recordings automatically

Discover meaningful patterns in textual data

Highlight and tag customer interviews

Run sentiment analysis

Collaborate on customer research through one interface

Kathryn Rounding , Senior Product Designer at You Need A Budget, says, “Dovetail is a fantastic tool for conducting and managing qualitative research. It helps bring all your research planning, source data, analysis, and reporting together, so you can not only share the final results but all the supporting work that helped you get there.”

6. Thematic

Thematic's AI-driven text feedback analysis platform helps you understand what your customers are saying—and why they’re saying it.

How Thematic analyzes qualitative data

Thematic helps you connect feedback from different channels, uncover themes in customer experience data, and run sentiment analysis—all to make better product decisions. Thematic is helpful when you need to:

Analyze unstructured feedback data from across channels

Discover relationships and patterns in feedback

Reveal emerging trends in customer feedback

Split insights by customer segment

Use resulting data in predictive analytics

Emma Glazer , Director of Marketing at DoorDash, says, “Thematic empowers us with information to help make the right decisions, and I love seeing themes as they emerge. We get real-time signals on issues our customers are experiencing and early feedback on new features they love. I love looking at the week-over-week breakdowns and comparing segments of our audience (market, tenure, etc.) Thematic helps me understand what’s driving our metrics and what steps we need to take next.” 

Delve is cloud-based qualitative data analysis software perfect for coding large volumes of textual data, and is best for analyzing long-form customer interviews.

How Delve analyzes qualitative data

Delve helps reveal the core themes and narratives behind transcripts from sales calls and customer interviews. It also helps to:

Find, group, and refine themes in customer feedback

Analyze long-form customer interviews

Categorize your data by code, pattern, and demographic information

Perform thematic analysis, narrative analysis, and grounded theory analysis

One Delve user says, “Using Delve, it is easier to focus just on coding to start, without getting sidetracked analyzing what I am reading. Once coding is finished, the selected excerpts are already organized based on my own custom outline and I can begin analyzing right away, rather than spending time organizing my notes before I can begin the analysis and writing process.”

8. ATLAS.ti

ATLAS.ti is a qualitative data analysis tool that brings together customer and product research data. It has a range of helpful features for marketers, product analysts, UX professionals, and product designers.

How ATLAS.ti analyzes qualitative data

ATLAS.ti helps product teams collect, structure, and evaluate user feedback before realizing new product ideas. To enhance your product design process with ATLAS.ti, you can:

Generate qualitative insights from surveys

Apply any method of qualitative research

Analyze open-ended questions and standardized surveys

Perform prototype testing

Visualize research results with charts

Collaborate with your team through a single platform

One of the ATLAS.ti customers shares,“ATLAS.ti is innovating in the handling of qualitative data. It gives the user total freedom and the possibility of connecting with other software, as it has many export options.” 

MAXQDA is a data analysis software that can analyze and organize a wide range of data, from handwritten texts, to video recordings, to Tweets.

How MAXQDA analyzes qualitative data

MAWQDA organizes your customer interviews and turns the data into digestible statistics by enabling you to:

Easily transcribe audio or video interviews

Structure standardized and open-ended survey responses

Categorize survey data

Combine qualitative and quantitative methods to get deeper insights into customer data

Share your work with team members

One enterprise-level customer says MAXQDA has “lots of useful features for analyzing and reporting interview and survey data. I really appreciated how easy it was to integrate SPSS data and conduct mixed-method research. The reporting features are high-quality and I loved using Word Clouds for quick and easy data representation.”

10. MonkeyLearn

MonkeyLearn is no-code analytics software for CX and product teams.

How MonkeyLearn analyzes qualitative data

MonkeyLearn automatically sorts, visualizes, and prioritizes customer feedback with its AI-powered algorithms. Along with organizing your data into themes, the tool will split it by intent—allowing you to promptly distinguish positive reviews from issues and requests and address them immediately.

One MonkeyLearn user says, “I like that MonkeyLearn helps us pull data from our tickets automatically and allows us to engage with our customers properly. As our tickets come in, the AI classifies data through keywords and high-end text analysis. It highlights specific text and categorizes it for easy sorting and processing.”

The next step in automating qualitative data analysis 

Qualitative data analysis tools help you uncover actionable insights from customer feedback, reviews, interviews, and survey responses—without getting lost in data.

But there's no one tool to rule them all: each solution has specific functionality, and your team might need to use the tools together depending on your objectives.

With the right qualitative data analysis software, you can make sense of what your customers really want and create better products for them, achieving customer delight and loyalty.

FAQs about qualitative data analysis software

What is qualitative data analysis software.

Qualitative data analysis software is technology that compiles and organizes contextual, non-quantifiable data, making it easy to interpret qualitative customer insights and information.

Which software is used for qualitative data analysis?

The best software used for qualitative data analysis is:

Cauliflower

MonkeyLearn

Is NVivo the only tool for qualitative data analysis?

NVivo isn’t the only tool for qualitative data analysis, but it’s one of the best (and most popular) software providers for qualitative and mixed-methods research.

QDA examples

Previous chapter

Guide index

The Sheridan Libraries

• Qualitative Data Analysis Software (nVivo, Atlas.TI, and more)
• Sheridan Libraries

Qualitative Data Analysis Software (QDAS) overview

Choosing qda software, core qdas functions.

• Other QDAS Software
• Qualitative Data Sources

For direct assistance

Contact us , JHU Data Services   for assistance with access to nVivo and ATLAS.ti at the Data Services offices on A level, JHU Eisenhower Library.

Visit our website for more info and our upcoming training workshops !

Qualitative research has benefited from a range of software tools facilitating most qualitative methodological techniques, particularly those involving multimedia digital data. These guides focus on two major QDAS products, nVivo and ATLAS.ti.  Both programs can be found on the workstations at the Data Services computer lab on A-level, Eisenhower Library, and nVivo is available through JHU's SAFE Desktop . This guide also lists other QDA software and linked resources.

Many university libraries have produced comprehensive guides on nVivo, ATLAS.ti, and other QDA software, to which we will provide links with our gratitude

Schmider, Christian. n.d. What Qualitative Data Analysis Software Can and Can’t Do for You – an Intro Video . MERIT Library at the School of Education: School of Education, University of Wisconsin-Madison. Accessed January 7, 2020. https://www.youtube.com/watch?v=tLKfaCiHVic .

• Supported Methods
• Decision Factors
• Compare QDA Software

Qualitative Data Analysis (QDA) Software supports a variety of qualitative techniques and methodologies

Qualitative techniques supported by  QDAS

• Coding and Classifying
• Writing: analysis, description, memos
• Relating: finding and annotating connections, relationships, patterns
• Audio/Visual analysis: marking, clipping, transcribing, annotating
• Text mining: computer-aided discovery in large amounts of unstructured text
• Visualization: diagramming, relationship and network patterns, quantitative summary 

QDAS  supported methodologies

• Ethnography
• Case studies
• Grounded theory/ phenomenology
• Discourse/narrative analysis
• Sociolinguistic analysis
• Collaborative qualitative research
• Text analysis & text mining

Overview of qualitative methods from ATLAS.ti:  https://atlasti.com/qualitative-research-methods/

Decision factors for your research

• Methods to feature facilitation (in disciplinary context): How many features directly support your methodology?
• Interface for collection, analysis, reports: Do features accommodate most phases of your research workflow?
• Visualization and outputs: Does it produce and successfully export needed visualization without extensive modification?
• Cost and access to software: Is it worth the investment cost as well as in learning to use it? Look for education discounts.
• Software Comparisons: Commercial & Free. (George Mason University) Lists of flagship software, free software, and tools for converting codebooks among QDA software.
• QDA Software Comparison Chart (NYU Libraries) Comparison chart of QDA software from NYU Library's LibGuide
• Top 14 Qualitative Data Analysis Software Guide with descriptive summaries of the main QDA software, several with business focus.
• Dueling CAQDAS using ATLAS.ti and NVivo Webinar comparing features and use of ATLAS.ti and NVIvo for qualitative data analysis. Includes live demos.

Basic functions common to most QDA programs, and to NVivo and ATLAS.ti in particular:

• Application of a maintained set of terms and short phrases linked to segments of text or audio/video that can be queried and gathered for comparative analysis. 
• Longer narrative notes attached to text or a/v segments, or to codes
• Quick access to codes and segments that can be brought together in panel views for comparison, advanced Boolean search options, and flexible interlinking of segments, codes, and annotation
• Most QDAS facilitates transcribing audio and video, ideally maintaining the links between transcript and A/V segments. 
• Gathering codes, segments, and annotations facilitates pattern discovery and further description of relationships. Some QDAS support social network analysis techniques and visualization
• A range of reports using queries and filters to assemble data and annotations facilitates analysis and writing results.
• ​ Typically includes code tables, social network graphs, and annotated A/V clips.
• Shared access to data & analysis, facilitating comments and discussion, and tracking contributor actions and changes.
• Next: NVivo >>
• Last Updated: Jul 18, 2024 10:26 AM
• URL: https://guides.library.jhu.edu/QDAS

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!

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i need a citation of your book.

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

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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.

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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 ?

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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.

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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

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Clear explanation on qualitative and how about Case study

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Wow, Thanks for making my life easy

This was helpful thanks .

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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.

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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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10 Best Qualitative Data Analysis Software of 2024

When the data presented has words and descriptions, we call it qualitative data. There are various qualitative data analysis software that allows you to do qualitative research in a complete way through various methods, be it online focus groups, surveys, polls, idea forums, interviews, discussion groups, and more.

In this blog, we will discuss the 10 best qualitative data analysis software. Through these tools, you will be able to interpret and analyze all this information effectively through reports for an easy presentation of the results of your studies.

What is qualitative data analysis software?

Qualitative data analysis software (QDAS) is a type of software that designed to assist researchers, analysts, and professionals in managing, organizing, and analyzing non-numerical or qualitative data .

Qualitative data refers to:

• Descriptive and text-based information
• Text documents
• Audio recordings

Unlike quantitative data analysis software that focuses on numerical data, qualitative data analysis tools are tailored to handle and interpret the complexities of qualitative data.

Qualitative data analysis tools provide a range of features and functionalities to help users make sense of their qualitative data. These software solutions often include text coding and categorization, data organization, search and retrieval capabilities, data visualization, collaboration tools, and more.

By using qualitative data analysis software, researchers can uncover patterns, themes, and insights within large volumes of qualitative data, facilitating more informed decision-making and in-depth exploration of research topics.

Learn more about Qualitative Data, Types, Analysis, and Examples

Why should you use qualitative data analysis software?

Using qualitative data analysis software is essential for researchers and professionals who want to analyze and derive insights from qualitative data efficiently. Here are five key reasons why you should consider utilizing such tools:

01. To simplify the process of efficient data analysis

Qualitative data analysis tools streamline the process of analyzing qualitative data, saving you time and effort. They are specifically designed to handle large volumes of textual data, making managing and processing diverse data sources easier.

02. To get detailed insights

With these tools, you can conduct in-depth qualitative text analysis to uncover hidden patterns, sentiments, and themes within your data. This enables you to gain a more comprehensive understanding of your research topic or business problem.

03. To apply the mixed methods capability

Qualitative data analysis tools often support qualitative and mixed methods data analysis, allowing you to integrate both qualitative and quantitative data . This holistic approach provides a well-rounded perspective and enriches your findings.

04. To conduct statistical analysis

Some tools offer statistical analysis features, enabling you to quantitatively analyze qualitative research data. This is particularly useful when you need to validate qualitative insights with statistical rigor, enhancing the credibility of your findings.

05. Qualitative data analysis software makes collaboration and reporting easier

These tools facilitate collaboration among research teams and streamline the sharing of insights. You can easily collaborate with colleagues, share findings, and generate comprehensive reports to communicate your results effectively.

Incorporating a qualitative data analysis tool into your research or business processes empowers you to delve deeper into your data, make informed decisions, and present findings clearly and compellingly

Best 10 qualitative data analysis software

Let’s explore the top 10 qualitative data analysis software solutions, delving into tools that decode the richness of unstructured information and unlock invaluable insights.

01. QuestionPro

QuestionPro is a powerful tool for analyzing customer feedback. It helps businesses understand their customers better by providing easy-to-use features for collecting data from surveys, reviews, and more.

QuestionPro is well-known for its strong survey tools, but its features for analyzing qualitative data also make it a great choice for businesses wanting detailed customer insights .

Best features:

• Effective data visualization and analysis
• Text analysis
• Sentiment analysis
• Word cloud generation
• Feedback analysis
• Create detailed surveys with customization options and various question formats.
• Immediate insights into customer sentiments through real-time sentiment analysis.
• Automated sentiment analysis categorizes responses as positive, negative, or neutral.
• Free license never expires. Upgrade anytime.
• Pricing for premium features and packages starts from $99/month.

MAXQDA stands out as a comprehensive qualitative data analysis software designed to handle a wide range of customer data. It excels in analyzing diverse data formats such as interviews, surveys, videos, and social media content.

• Multi-format data import (text, audio, video)
• Advanced coding and tagging
• Visual data exploration
• Mixed methods analysis support
• Easily import and analyze diverse data formats, offering flexibility in the research scope.
• Allows researchers to combine qualitative and quantitative data for more holistic insights.
• Offers various coding methods for a nuanced analysis of qualitative data.
• The free version might be limited; access to advanced features requires a license.
• Premium plan starts from $45/month.

03. Quirkos

Quirkos offers a unique approach to qualitative data analysis, emphasizing comparative analysis. This software enables side-by-side comparisons of data, facilitating the identification of trends and customer behavior patterns.

• Comparative analysis
• Drag-and-drop coding
• Real-time visualization
• Theme color customization
• Integration with SPSS, Word, Excel
• Compatible with multiple operating systems, making it accessible to a wider range of users.
• The drag-and-drop coding and color coding features enhance user experience.
• Color-coded themes enable quick identification and analysis of data segments.
• Some advanced analysis tools available in other software might be missing in Quirkos.
• Quirkos might not have advanced AI-driven features for automated sentiment analysis or data insights.
• Premium plan starts from $5/month.

04. Raven’s Eye

Raven’s Eye stands out as a qualitative data analysis software with a focus on natural language data processing. This software’s unique feature is its audio converter, which transforms audio files into text, allowing for in-depth analysis of customer interviews.

• Natural language audio and text analysis
• Real-time data processing
• Transcription of audio files
• A unique feature to convert audio to text for in-depth analysis, particularly useful for interview data.
• Supports text analysis in numerous languages, enhancing its usability for global data.
• Provides valuable insights into customer behavior through text and spoken word analysis.
• The accuracy of audio-to-text conversion might be affected by audio quality, leading to potential errors.
• The accuracy of insights heavily relies on the quality and quantity of data input.
• Premium plan starts from $35/month.

05. Square Feedback

Square Feedback emerges as a valuable free customer feedback collection tool that extends its capabilities to qualitative data analysis. This tool efficiently collects feedback from surveys, enabling businesses to analyze customer responses and gauge satisfaction levels.

• Integration with digital receipts
• Historical data comparison
• Private tracking of customer comments
• Simplifies the process of collecting customer feedback through surveys and digital receipts.
• Offers the option to track and analyze customer comments and responses privately.
• Presents customer insights in visually appealing charts and graphs for better understanding.
• It focuses primarily on qualitative customer feedback and may lack advanced analytical capabilities.
• Available upon request.

LiGRE, a versatile qualitative data analysis software, caters to a wide user base, including students, researchers, and business professionals. Offering both free and premium plans, LiGRE excels in analyzing interviews and extensive text datasets.

• Automatic transcription (audio and video)
• Survey building and data merging
• Multimedia data support
• Collaboration via LiGRE laboratory
• Streamlines the process of transcribing audio and video files, saving time and effort.
• Simplifies the creation of surveys to gather qualitative data directly from participants.
• Provides a collaborative platform for teams to work together on qualitative research projects.
• Users might require some time to learn and adapt to the features and functionalities.
• May face limitations when handling complex projects with extensive data sources.
• Requires compatible hardware and software for efficient usage.

07. QDA Miner Lite

QDA Miner Lite, a user-friendly qualitative data analysis software, offers a simplified approach to analyzing interviews, open-ended responses, journals, and more. This tool is particularly advantageous for researchers seeking to uncover intricate patterns and insights within qualitative data.

• Easy coding, retrieval, and review of data
• Visual presentation of results
• Support for various data formats
• Mixed methods analysis
• Equips users with tools to analyze and code text data efficiently.
• Supports analysis of diverse data types, enhancing research capabilities.
• Enables precise retrieval of specific text segments for analysis.
• New users may need time to learn and proficiently use the software.

08. Dedoose

Dedoose is a comprehensive qualitative data analysis software that caters to both qualitative and mixed-method research. It empowers researchers, professionals, and students to analyze various data types, including text, audio, images, videos, and surveys.

• Qualitative and mixed method analysis
• Interactive visual presentations
• Advanced analytics tools
• Suitable for researchers requiring both qualitative and quantitative insights.
• It presents data in visually engaging formats for better understanding.
• It supports coding and analysis of various types of media and data.
• New users may require time to familiarize themselves with the software’s features.
• Users might face limitations on data export formats or options.
• Premium plan starts from $13/month.

09. Glimpse

Glimpse stands out as a valuable qualitative data analysis software for customer success teams seeking to gain deep insights into consumer behaviors. This user-friendly tool incorporates qualitative sentiment analysis surveys to collect and analyze data from various sources, such as customer feedback and reviews.

• Qualitative sentiment analysis
• Real-time data collection and analysis
• Cross-platform data collection
• Enables understanding of customer emotions and sentiments.
• Collects data from diverse platforms for a holistic view.
• Presents insights visually, aiding quick understanding.
• Utilizes machine learning for more in-depth analysis.
• Handling machine learning and what-if analysis might be complex for some users.
• Machine learning and advanced features might require some technical expertise.
• Premium plan starts from $458/month.

10. HubSpot

HubSpot offers a comprehensive customer feedback tool that serves as qualitative data analysis software, enabling businesses to gather and analyze insights from surveys and customer reviews.

• Customer feedback analysis
• NPS surveys integration
• Real-time data visualization
• Integrates with NPS surveys, streamlining the collection of customer feedback.
• Provides visual representations of customer insights for better understanding.
• Collects and analyzes both quantitative and qualitative customer feedback.
• It may lack advanced analysis features compared to specialized tools.
• Primarily designed for customer feedback analysis, it might not cover other data types.
• Premium plan starts from $15/month.

Whether you’re a business owner, a student, or simply intrigued about the beauty of language, explore these text analysis tools and make your life easier.

Why is QuestionPro the best qualitative data analysis software?

QuestionPro stands out as the premier choice for qualitative data analysis software, especially for analyzing survey responses and conducting mixed-methods research. Several key factors underscore its supremacy:

• Effective data visualization and analysis: QuestionPro offers robust tools to visualize and analyze data efficiently. Researchers can transform raw data into insightful graphs, charts, and dashboards that facilitate clear comprehension of trends and patterns.
• Text analysis: With its advanced text analysis capabilities, QuestionPro allows users to delve into textual data, uncovering underlying themes and sentiments. This aids in understanding customers’ opinions, preferences, and feedback.
• Sentiment analysis: The software’s sentiment analysis feature goes beyond simple text processing. It discerns emotional tones and attitudes within responses, enabling researchers to accurately gauge customer satisfaction and sentiment.
• Word cloud: The inclusion of word clouds enhances qualitative analysis by visually representing frequently occurring words in a dataset. This feature enables quick identification of prominent themes and concepts.
• Feedback analysis: QuestionPro excels in feedback analysis, enabling researchers to extract valuable insights from open-ended responses. This is crucial for gaining a deeper understanding of customers’ thoughts and suggestions.
• Image analysis: With image analysis capabilities, QuestionPro extends its reach to include multimedia content. This is particularly beneficial when analyzing visual feedback or incorporating image-based data into research.
• Integrations: QuestionPro’s seamless integration capabilities allow researchers to connect their analysis with other tools and platforms. This feature fosters a cohesive research workflow, enhancing the overall efficiency of the qualitative data analysis process.

QuestionPro’s qualitative software offers an all-encompassing solution for survey responses and mixed methods research. Its array of features ensures effective data analysis, making it the best choice for researchers seeking comprehensive and insightful analysis tools. Contact QuestionPro to schedule a demo or learn more!

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Frequently Asked Question (FAQs)

Qualitative data analysis software helps researchers work with non-numerical data like text, audio, video, and images. It helps organize, code, and interpret this data to find patterns, themes, and insights.

To choose the right QDA software, consider factors such as your research needs, the type of data you will analyze, the software’s features and capabilities, ease of use, compatibility with other tools you use, and your budget. QuestionPro can be the best choice for you as it allows you to try out trial versions of different programs to determine which one best meets your needs.

Yes, QDA software like QuestionPro can handle mixed methods research. This platform allows researchers to work with both qualitative and quantitative data, making it easier to do a thorough analysis of their findings.

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend, and review analysis is a great place to start. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations ( conversational analytics ), and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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When two major storms wreaked havoc on Auckland and Watercare’s infrastructurem the utility went through a CX crisis. With a massive influx of calls to their support center, Thematic helped them get inisghts from this data to forge a new approach to restore services and satisfaction levels.

• (Top 8) Best Qualitative Data Analysis Software

Explore top qualitative data analysis tools to enhance your research with powerful features and user-friendly interfaces.

Corporations, large and small, amass a great deal of qualitative data. Customer feedback, interviews, support tickets – how do they analyze it all ? 

They use Qualitative Data Analysis Software.

Qualitative researchers used to perform their work manually . Conducting research today without technology is the equivalent of bringing a knife to a lightsaber fight.

Let’s get down to brass tacks . Qualitative Data Analysis tools enhance the UX workflow. They expedite cumbersome manual processes, allowing researchers and designers to focus more on analysis.

Want to learn more? Marvin’s here to help on your journey!

Here’s our no-frills guide to qualitative data analysis software. [We promise to try using fewer Star Wars references.]

TL;DR – Best Qualitative Data Analysis Software

There’s no shortage of programs for qualitative data analysis on the market. Companies spoiled for choice have a tough time whittling their options down. So how do they decide? 

Let’s understand more about how to choose the right tools and what to look out for. 

Highlighting their pros and cons, we’ll examine the following tools closely:

• Cauliflower

Our recommendation? Glad you asked!

Marvin is fast gaining popularity in the UX community. With a simple and eye-catching UI, it’s easy to navigate through the application.

We’re constantly rolling out new features that delight users. We believe listening to our customers eventually enhances their user experience.

Read on to learn why researchers and designers call Marvin a ‘game changer .’

What is Qualitative Data Analysis Software?

We won’t wax lyrical about why Qualitative Research matters . Suffice to say that non-numerical, qualitative data is the lifeblood of good research and design.

Qualitative Data Analysis Software facilitates the gathering, structuring, and interpreting of qualitative data. Researchers identify trends or patterns in contextual data from various formats. They seek to understand users’ thought processes – the “why” behind their actions. 

This software allows researchers to automate aspects of the research process, such as:

• Transcription. Tools capture user interactions from audio and video, automatically producing a verbatim transcript with multi-language support.
• Coding and Annotations. Tag your qualitative data and cluster textual data into themes. Query data and connect pieces of evidence across projects.
• Statistical. Uncover trends, patterns, and correlations between variables. 
• Sentiment. Interprets and classifies data to understand people’s attitudes.
• Textual. Text mining extracts high-quality information from your research.
• Media analytics. Analyze data from various media types or sources such as images, text, audio, and video. Link meaningful insights back to source data.
• Data reporting and visualization. Summarize and present findings. Spot trends and patterns and discover links across projects. Map and track the customer journey.

Use qualitative research data analysis software to unearth insights from unstructured data. Insights inform decision-making, allowing UX professionals to create experiences that delight users. Isn’t that the whole point, anyway?

8 Best Qualitative Data Analysis Software

All qualitative data platforms aren’t created equal.

As the software does, we’ve performed the heavy lifting for you. We scoured the web for the best software for analyzing qualitative data. 

Put these 8 tools to the test and forget about the rest (just kidding):

A centralized UX research repository that houses both qualitative and quantitative data. Marvin integrates with apps that designers and UX researchers love. Connect data from Zendesk, Qualtrics, Asana, Miro, and Notion (among others). 

Connect to popular video conferencing tools – Marvin offers transcription support in over 40 languages. It supports seamless workflows. AskAI is a search engine within Marvin that allows you to ask questions about your data across the entire repository. Search for key insights effortlessly.

Supercharge your analysis. Marvin automatically surfaces key trends from data. It summarizes lengthy interviews and highlights key takeaways, so you don’t have to pore over transcripts. Examine your data within the powerful Analyze section with a global view of all tags. 

Create stunning visualizations and reports. Stitch together highlight reels from audio and video files. Share files in various formats with stakeholders who’d benefit from insights. Consumers of research love hearing directly from users.

All your research in one place. That’s Marvin.

• Industry-leading AI features
• Multiple language support
• Wide-ranging integrations
• Highly intuitive UI
• User-friendly
• Collaborative – share insights with cross-functional teams & stakeholders
• Highly responsive customer support
• Limited quantitative statistical analysis

Best for: One-size-fits-all. Teams of 2 to 20,000 use Marvin! Set up your free demo with Marvin today.

2. ATLAS.ti

A powerful tool that assimilates customer and product data under one roof. Collect, structure, and evaluate customer feedback within the application. Atlas caters to a varying set of users who all collaborate on a single platform.

User surveys to collect feedback directly from users. Perform prototype testing and refine wireframes. Represent data with charts and visualizations. Atlas has incorporated AI features, including autocoding. Import data from Twitter or popular note-taking apps such as Evernote (among others).

• AI features – auto coding, summarization, and chatbots
• Strong integrations
• Expensive for small teams
• Coding features aren’t intuitive

Best for: Large Research teams and Academic Institutions

3. Cauliflower

A no-code qualitative data analysis tool. Conduct and analyze open ended survey data with Cauliflower . Examine social media content to understand customer reviews. Cauliflower has ready-made visualizations to help identify themes. 

Cauliflower’s AI-powered analytics help correlate pieces of customer feedback. Features include no-code text analysis using an integrated chatbot. Automate conversations with customers to generate leads and provide 24/7 support. Collect feedback without human intervention. 

• AI-powered chatbots
• Automates data and lead collection
• High level of customization
• High quality training data required
• Quality of AI results vary widely
• Complex tool to learn

Best for: Companies who want to automate customer conversations

This software can handle both quantitative and qualitative data but with limitations. Import data from various file formats including audio, visual, text and spreadsheets. Its robust tools help during data coding, charting, and visualization. 

Learn when to use Qualitative Research vs. Quantitative Research .

A major downside is the lack of transcription. They make it easy to integrate with a transcription app of your choice though. Deedose ’ cloud-based platform makes remote access easy. A team-oriented application, collaborate effortlessly with your peers. 

• Easy collaboration through the cloud
• Mobile compatible
• 100% Web-based
• Limited multimedia analysis
• No AI features
• No transcription

Best for: Students, Academic Researchers (UCLA academics created Deedose)

Delve is a no-nonsense qualitative data analysis tool solely focused on data coding. It’s especially beneficial for analyzing long-form customer interviews. Create and group data into themes and use its AI Assistant to suggest new subcodes. 

Slice and view data from different dimensions – such as demographics or codes. Delve specializes in thematic analysis but is helpful for all kinds of qualitative analysis. The company curates helpful material (and even an online course!) to get you started with qualitative coding.

• Easy-to-learn and use
• Flexible pricing
• Limited AI features
• Limited collaborative features
• Limited functionality – a purely thematic analysis tool

Best for: Students or Researchers beginning their journey

A versatile mixed methods data analysis tool that’s been around since 1989. Export and analyze a wide range of data sources. These vary from interviews, reports, tables, surveys, focus groups, and handwritten text to tweets. Get deeper insights using both quantitative and qualitative data. 

MAXQDA allows you to easily search and retrieve information. Structure surveys both open and close-ended. Organize and categorize unstructured data. Visualize data with word clouds and more. Create lit reviews and work with bibliographical information. It offers transcription as well. 

• Easy-to-use
• Multimedia functionality
• Advanced Visualization 
• Inexpensive
• Supports multiple file formats and languages 
• Collaboration isn’t easy
• High IT resources required
• No real-time syncing
• Limited free version

Best for: Education, Nonprofits and Small Organizations

A popular tool for qualitative and mixed method research. Organize and structure qualitative data before analysis. Import audio and video files, and NVivo will automatically transcribe them. Use this to transcribe sales calls, customer interviews, product demos, and social media content. 

Analyze these files in-house to find recurring themes. NVivo offers autocoding (which does exactly what it says). A caveat – researchers must perform analysis after running autocoding. Powerful visualizations using word clouds and concept trees. 

• Relationship coding
• Query data feature
• Difficult for beginners
• No transcription in-house
• Limited language support

Best for: Tech-savvy Researchers or Academics, Product teams at mid to large companies.

8. Qualtrics

An all-in-one customer experience management platform. Qualtrics XM helps remove uncertainty from product development. Gather data from various customer touchpoints and analyze it in one place. 

Qualtrics offers a great variety of analytics. Gauge user sentiment from textual data. Visual tools such as session replays create a comprehensive view of the user experience. XM uses powerful predictive analytics to ensure customer opinion is at the heart of decision-making.

Validate product ideas, identify market opportunities, and fix broken customer journeys. Real-time data collection and analysis facilitates agile decision-making. Bonus: Qualtrics integrates seamlessly with Marvin. 

• Real-time data 
• Predictive analytics
• One-stop shop for UX
• Numerous integrations
• Limited data coding capabilities

Best for: Large Enterprises and Research teams dealing with multiple data sources

How to Choose the Best Qualitative Data Analysis Software

Every company is different. Each has different resources, capabilities, and needs when choosing qualitative data analysis software. Be mindful of these as you evaluate tools and how they fit into your organization. 

Follow these steps:

• Understand Requirements. What problem are you trying to solve? How will the application fit into the company’s workflow? What does a successful implementation look like? If you can’t measure it, you can’t improve it…
• Establish Criteria. What features are you looking for? What functionality is essential, and what’s ‘nice-to-have’? How helpful is their customer support when you get stuck?
• Identify Users. Which teams or employees will use the application? What are their needs? Are there different types of users – power or view-only? 
• Explore Integrations. Does the application fit in with your existing tool stack? What applications must integrate with the new software? Is this process easy or cumbersome?
• Test with Free Trials. After evaluating tools capabilities and business needs, it’s testing time. Pilot a small project and ask users to evaluate the software in action. What did they find easy or difficult?
• Review and Decide. Involve primary users and stakeholders in the decision process. What did users like about each software? Which one would they vote for? Come to a consensus.

Keep these questions in mind to pick the tool that suits your business needs best.

Best Practices to Organize and Code Data in Qualitative Data Analysis Tools

Coding or tagging data makes it easier to understand and categorize user feedback. Tagging makes data analyzable, discoverable, and shareable . 

Each research project is unique – there isn’t one specific way to tackle coding data. Having said that, there are some tricks to add efficiency to the coding process. 

Here we go:

• Start with Output. What questions do you want to answer? For some questions, you may have an idea of the output (close-ended responses). Create tags for these to save time. 
• Draft a Discussion / Question Guide. An effective discussion guide includes questions that help you navigate an interview. Tag your questions! It provides a starting point on how to group your codes. 
• Data Preparation. Organize and tag data before analysis. Create foundational labels for overarching topics. Create codes for structured questions in advance (Like scale/radio buttons). Code personas to distinguish between user groups. 
• Go Granular. Mihaela Vorvoreanu, or ‘Mickey,’ is the Director of UX Research Education for Aether at Microsoft. She suggests always coding with more granularity. That way, it’s easier to collapse codes than to go back and recode data.
• Refine Tags. Use a global view of your codes to clean them up. Collapse codes and merge overlapping ones. Educate other researchers on how to tag data correctly.
• Visualize Data. Use graphs and charts to visualize data. Viewing data in this form helps crystallize your thinking and build narratives. Anyone can look at a graph and make conclusions about your study. 
• Stay Up-to-Date. Companies roll out new coding features at a rate of knots. Stay learning!

For more information, head over to our guide on tagging user research insights .

Frequently Asked Questions (FAQs)

Here, we tackle some frequently asked questions about qualitative data analysis software:

Is HeyMarvin Suitable for Large Research Projects?

Marvin gobbles up research data from any source and helps churn out rich insights, no matter the data volume.

Don’t take it from us. We’ll let Marvin users demonstrate how it handles large datasets:

At Microsoft, Mickey and team (from above) created a Responsible AI Maturity Model . They interviewed 47 AI practitioners and amassed over 80 hours of interview and focus group footage.

To dissect the data, they used Marvin to create a constantly changing codebook. Aether synthesized over 2000 notes to create the framework.

“If you can do this kind of complex project and this kind of complex data analysis with Marvin, you could probably do it with a lot easier projects, things that are not quite as hairy,” Mihaela said.

Entertainment Partners handles all things finance in the entertainment industry. Head of Research Beth Seaman handles terabytes of data every day. 

“What HeyMarvin does for me is it allows me to very quickly see patterns in large amounts of data and then communicate those out,” she said. 

In the pre-Marvin era, she had to sift through individual surveys manually. 

“I don’t think I ever knew how bad it was until I figured out what Marvin can do for me,” she said. “It has changed my existence .” 

What is the Best Data Analysis Method for Qualitative Research?

We’ll go with a researcher’s favorite answer — it depends.  

Qualitative research involves several methods of analyzing data. The most common methods are listed below: 

• Content. Transforms verbal and behavioral data into quantitative output. Helps classify data to give reliable conclusions about users and their opinions.
• Thematic. Identifies, interprets, analyzes, and categorizes patterns in qualitative data. 
• Narrative. Examines different user stories and reformulates them based on the context of each case. The revision of primary qualitative data by a researcher. 
• Discourse. Studies relationships between information and the social context.
• Grounded theory. Formulates a theory from a single case. Further examination of real-world case studies is needed to see if they contribute. 

Each has its own applications and merits. To determine the method, identify what questions you want answers to. Qualitative data analysis software doesn’t favor one particular data analysis method. Due to the overlapping nature of qualitative data, software will work for most research methodologies. 

What AI Apps are Best for Large Qualitative Data Sets?

AI brings efficiency and scale to qualitative research. It frees up a researcher’s time to dive deeper into analysis. Learn how AI makes qualitative research powerful . 

Some AI applications handle large volumes of qualitative data better than others.

To deal with large qualitative datasets, we recommend using the following:

Let AI handle the drudgery of research and spend more time on analysis with these apps. 

You have learned well, padawan (we couldn’t resist!).

Qualitative data analysis software helps you make sense of mountains of data. Turn raw, unstructured data into actionable insights.

It’s essential to consider your business needs before settling on a tool. Test each one and ask researchers which tool they think works best. Since AI is the talk of the town, future-proof your choice of tool with AI features. Implement these best practices for coding data to expedite the process. We’d love to show you Marvin’s capabilities. Sign up for a free demo today. Use Marvin to understand qualitative data with ease.

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

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MAXQDA is the most user-friendly choice for your qualitative data analysis needs and is considered as one of the best qualitative data analysis software . It is designed to work with a wide range of data types, including text, audio, and video, and offers a variety of powerful tools for qualitative data analysis. Whether you’re looking to code and classify data , visualize patterns and themes, or perform mixed-methods , or quantitative content analysis, MAXQDA makes it easy to get the insights you need for your qualitative data analysis.

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All-in-one Qualitative Analysis Software developed by and for researchers

MAXQDA is the go-to software for managing and analyzing your qualitative research. Developed by and for researchers, it offers a wide range of powerful tools for managing your research project, analyzing data and collaborating with team members. With its user-friendly interface, step-by-step free tutorials and comprehensive documentation, it’s the perfect choice for both experienced and novice qualitative researchers. MAXQDA streamlines the qualitative data analysis process , giving you more time to focus on interpreting and understanding your data, rather than struggling with complicated software.

Developed by and for researchers – since 1989

Having used several qualitative data analysis software programs, there is no doubt in my mind that MAXQDA has advantages over all the others. In addition to its remarkable analytical features for harnessing data, MAXQDA’s stellar customer service, online tutorials, and global learning community make it a user friendly and top-notch product.

Sally S. Cohen – NYU Rory Meyers College of Nursing

Qualitative Data Analysis is Faster and Smarter with MAXQDA

MAXQDA makes qualitative data analysis faster and easier than ever before. It offers a wide range of analysis methods, including Grounded Theory , qualitative content analysis , group discussions , discourse analysis , Mixed Methods , and case and field studies . Its user-friendly 4-Window Interface provides quick access to powerful tools and functions, streamlining the data analysis process. Additionally, MAXQDA is the only leading Qualitative Data Analysis software that is 100% identical on Windows and Mac , providing a consistent and seamless analysis experience.

MAXQDA is the ultimate qualitative data analysis software, with its ability to seamlessly import all types of qualitative data making it the perfect tool for managing and analyzing your research project. With MAXQDA, you can easily import a wide range of data types such as text, interviews, focus groups, PDFs, web pages, spreadsheets, articles, e-books, bibliographic data, videos, audio files, and even social media data. Organize your data into groups, link relevant quotes to each other, use MAXQDA’s powerful tools to facilitate your qualitative analysis, and share and compare work with your team members. The project file stays flexible allowing you to expand and refine your category system as you go, ensuring your research is tailored to your needs.

User-Friendly Tools for Qualitative Coding

Utilize a variety of tools such as codes, colors, symbols, and emoticons to mark important information in your data. With MAXQDA, you can create codes with just one click and apply them quickly via drag & drop. The software’s Text Search tools allow you to explore your material without coding or reading them first. You can also search for keywords and automatically code them with just a few clicks. Organize your thoughts and theories in memos that can be linked to any element of your project. Retrieve your coded segments with one click or use MAXQDA’s powerful summary tools to test and develop new theories. Make the most of your time with MAXQDA, the #1 qualitative analysis software

Organize Your Qualitative Data with MAXQDA’s Memo Tools

As you perform your qualitative analysis, MAXQDA allows you to capture ideas and insights by creating memos to store research questions, objectives, and paraphrasing passages into your own words. The software’s memo feature also allows for easy creation of audit trails by attaching memos like post-it notes to text passages, texts, document groups, images, audio/video clips, and codes. With the unique MAXQDA memo manager and lexical search function, you have immediate access to every single memo at any time, making it easy to stay organized and on top of your research. With MAXQDA, the leading qualitative analysis software, your data organization is made simple.

Text Search and Autocoding Tools

When analyzing large amounts of text for a qualitative study, MAXQDA’s Text Search tools can be of great help. These tools allow you to explore your documents without having to manually read or code them first, by searching for keywords or concepts that are important to your analysis. With just a few clicks, you can automatically code these keywords, creating document variables that can be used for searching and retrieving specific segments. Additionally, MAXQDA’s powerful Coding Query feature allows for in-depth analysis of the combination of activated codes in various ways, making it a valuable tool in the qualitative data analysis software toolkit.

Visual Text Exploration

When it comes to conducting a qualitative data analysis, the software you choose can make a big difference in the ease and efficiency of your research. MAXQDA is a powerful and versatile qualitative data analysis software that is well-suited for analyzing both small and large sets of text. The software’s Interactive Wordtree feature is a particularly powerful tool, visualizing all the combinations that lead to or from any word of your choice and providing a detailed display of frequencies. This feature can provide new and fascinating perspectives even on texts you know well and allows for a comprehensive overview of those you don’t. Furthermore, MAXQDA’s Text Search tools and Coding Query feature allow you to search for keywords, concepts or certain segments and analyze the combination of activated codes in different ways.

A lot more than just a Qualitative Data Analysis Software

Quantitative aspects can also be relevant in qualitative data analysis, and MAXQDA as a leading qualitative data analysis software, offers a wide range of tools specifically designed to facilitate quantitative content analyses. These include tools for word frequency analysis, visual text exploration, content analysis, vocabulary analysis, and dictionary-based analysis that help researchers analyze terms and their semantic contexts in a quantitative way. With MAXQDA, you can easily display frequencies of individual words or word combinations in tables, visualize them in the Interactive Wordtree, or use the Keyword-in-Context function to transfer the textual contexts of selected words into a clear table, among other features.

Visualize your qualitative data

MAXQDA’s wide range of visual tools for qualitative data analysis allows you to create stunning visualizations to analyze your material and gain insights from your data. From codelines to code clouds and concept maps, these tools provide a range of options to help you explore and understand your data in new ways. With the interactive connection between your visualizations and your MAXQDA data, you can easily switch between different perspectives and never lose sight of the big picture. Additionally, you can export your visualizations in various formats to enrich your final report and share your findings with others. These visual tools in MAXQDA make it a powerful software for qualitative data analysis and mixed methods.

AI Assist: Qualitative data analysis software meets AI

AI Assist – your virtual research assistant – supports your work with various tools. Besides automatic transcription of audio and video recordings in different languages, AI Assist simplifies your work by automatically analyzing and summarizing elements of your research project and by generating suggestions for subcodes. No matter which AI tool you use – you can customize your results to suit your needs.

Free tutorials and guides on qualitative data analysis software

MAXQDA offers a variety of free learning resources for qualitative data analysis, making it easy for both beginners and advanced users to learn how to use the software. From free video tutorials and webinars to step-by-step guides and sample projects, these resources provide a wealth of information to help you understand the features and functionality of MAXQDA. For beginners, the software’s user-friendly interface and comprehensive help center make it easy to get started with your data analysis, while advanced users will appreciate the detailed guides and tutorials that cover more complex features and techniques. Whether you’re just starting out or are an experienced researcher, MAXQDA’s free learning resources will help you get the most out of your qualitative data analysis.

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Get your maxqda license, compare the features of maxqda and maxqda analytics pro, faq: qualitative data analysis software.

MAXQDA is widely considered to be one of the best qualitative data analysis software on the market. It’s developed by researchers and offers a wide range of features and tools that allow researchers to easily organize, analyze and interpret qualitative data. With MAXQDA, users can import and work with a variety of file formats including text, audio, and video files. The software also provides advanced coding and categorizing capabilities, as well as visualization options for data analysis.

Additionally, MAXQDA has a user-friendly interface and offers a variety of tutorials and support resources to help users get the most out of the software. Overall, MAXQDA’s comprehensive set of features and ease of use make it the best choice for qualitative data analysis.

MAXQDA offers a wide range of tools for analyzing qualitative data, some of the most commonly used include:

• Coding : MAXQDA allows users to assign codes to segments of text, audio, or video files, and then use these codes to analyze the data.
• Memo : This tool allows users to create notes and reflections on the data, which can be used to help develop and refine coding schemes.
• Retrieval : MAXQDA provides several options for searching and retrieving data, such as keyword search, Boolean search, and proximity search.
• Visualization : The software offers a variety of visualization options, including word clouds, concordance lines, and code co-occurrence matrices, which allow users to quickly and easily identify patterns and connections in the data.
• Statistics : MAXQDA provides several statistical tools to analyze data, including frequency lists, crosstabs, and chi-square tests, which allow users to explore patterns and relationships in the data.
• Collaboration : MAXQDA also allows multiple users to work on the same project simultaneously and share the work.
• Importing and Exporting : MAXQDA can import and work with a variety of file formats including text, audio, and video files, and also export data to different formats such as Excel, SPSS, R, etc.

These are some of the most commonly used tools in MAXQDA, but the software offers many more options for analyzing qualitative data, depending on the research design and the researcher’s needs.

Analyzing qualitative data involves several steps. The first step is to import your data into MAXQDA. This can include text, audio, and video files, as well as images and other types of multimedia. Once the data is imported, you will need to organize it into manageable chunks. This might involve breaking up text files into smaller segments, transcribing audio or video files, and so on.

The next step is to begin coding the data. This involves assigning codes to segments of the data that relate to specific themes or topics. Codes can be assigned manually or automatically, and you can use multiple codes to analyze the data. Once you have coded your data, you can then use retrieval tools to search for specific segments of text, audio, or video that relate to a particular code or theme.

MAXQDA provides several visualization options, such as word clouds, concordance lines, and code co-occurrence matrices, which allow users to quickly and easily identify patterns and connections in the data. With the data organized, coded, and retrieved, you can begin to analyze it. This might involve identifying patterns and themes, comparing different segments of data, and so on.

The software allows users to create notes and reflections on the data, which can be used to help develop and refine coding schemes. MAXQDA allows multiple users to work on the same project simultaneously and share the work. Finally, when your analysis is complete, you can export your results in a variety of formats, including Excel, SPSS, R, etc. It’s important to note that the process of analyzing qualitative data with MAXQDA can vary depending on the research question, design, and methodologies you are using.

There are many different qualitative data analysis methods that researchers can use to analyze their data, such as:

Grounded theory : This method involves developing a theory that emerges from the data itself. Researchers begin by collecting and analyzing data, and then use this information to develop a theory that explains the data. This method is often used in the social sciences to study complex phenomena such as social interactions, organizational processes, and so on. With Grounded theory, the researcher does not begin with a preconceived theory but rather allows the theory to emerge from the data. MAXQDA offers tools such as the memo function and the ability to create codes and subcodes, which can help researchers to identify patterns and themes in the data, and develop a theory that explains these patterns and themes.

Content analysis : This method involves analyzing the content of the data, such as the words or themes that appear in a text. Researchers can use content analysis to identify patterns and themes in the data, such as the frequency of certain words or themes. MAXQDA provides tools such as the word frequency and word cloud functions, which can help researchers to quickly and easily identify patterns and themes in their data. Additionally, MAXQDA allows the user to code and categorize the data, which makes it easy to identify and analyze recurrent themes or patterns.

Discourse analysis : This method involves analyzing how language is used in the data to construct meaning. Researchers can use discourse analysis to study how language is used in different contexts, such as in political speeches, media reports, or online forums. MAXQDA offers tools such as the Word Tree, which can help researchers to study the use of language in their data, and the memo function, which can be used to reflect on the data and its meaning.

Narrative analysis : This method involves analyzing stories or narratives in the data to understand how they are constructed and how they convey meaning. Researchers can use narrative analysis to study how individuals construct and understand their own experiences, or how groups or communities construct and understand their collective experiences. MAXQDA provides tools such as the memo function, which can be used to reflect on the data and its meaning, and the ability to create codes and subcodes, which can help researchers to identify patterns and themes in the data.

Ethnography : This method involves studying a culture or community by immersing oneself in the culture or community and observing and participating in their daily activities. Researchers can use ethnography to study how culture or community shapes the experiences of individuals and groups. MAXQDA provides tools such as the memo function, which can be used to reflect on the data and its meaning, and the ability to create codes and subcodes, which can help researchers to identify patterns and themes in the data.

Case study : This method involves studying an individual or group in depth, often to understand a specific phenomenon or problem. Researchers can use case study to study how individuals or groups experience a particular phenomenon or problem. MAXQDA provides tools such as the memo function, which can be used to reflect on the data and its meaning, and the ability to create codes and subcodes, which can help researchers to identify patterns and themes in the data.

Phenomenology : This method involves studying the lived experiences of individuals or groups. Researchers can use phenomenology to study how individuals or groups experience the world around them. MAXQDA provides tools such as the memo function, which can be used to reflect on the data and its meaning, and the ability to create codes and subcodes, which can help researchers to identify patterns and themes in the data.

In summary, MAXQDA is a qualitative data analysis software that is compatible with all of these methods , and it provides a wide range of tools that can help researchers to analyze and understand their data.

Qualitative data analysis is the process of examining and interpreting non-numerical data, such as text, images, and audio recordings. The goal of qualitative data analysis is to identify patterns, themes, and insights within the data. This process typically involves coding and categorizing the data, and then using these codes to identify patterns and themes.

MAXQDA is widely considered as the best software for qualitative data analysis , it offers a wide range of tools to analyze and understand qualitative data, including the ability to code and categorize the data, identify patterns and themes, and create visual representations of the data. Additionally, MAXQDA is user-friendly and offers a wide range of free learning resources such as tutorials and webinars to help researchers of all levels to understand and use the software effectively. The software is developed by and for researchers, it is compatible with different research methodologies and offers a wide range of free resources to learn qualitative data analysis.

MAXQDA is considered as the best qualitative data analysis software for Mac. One of the key features of MAXQDA for Mac is that it is 100% identical and compatible with the Windows version, which makes it perfect for teamwork . This allows different people with different computers to work on the same project seamlessly, regardless of their operating system . This also provides researchers with more flexibility and allows them to work on their projects from different locations and platforms. Additionally, MAXQDA is user-friendly and offers a wide range of learning materials and tutorials to help researchers of all levels understand and use the software effectively.

MAXQDA is considered as one of the best qualitative data analysis software for students . MAXQDA offers deeply discounted prices for students, which makes it a cost-effective option for students. Additionally, MAXQDA also offers a scholarship for young researchers that provides free access to the software and training. Furthermore, MAXQDA organizes an annual international user conference, providing students with an opportunity to learn from experienced qualitative researchers , network with others in their field and stay up-to-date on the latest developments in qualitative data analysis.

MAXQDA is designed with ease of use in mind, making it a qualitative data analysis software that is accessible for students of all levels . It offers a comprehensive range of free learning resources in various formats, including videos and written guides, which are tailored to help students understand and effectively utilize the software’s various features and functionalities.

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10 Best Tools for Qualitative Data Analysis

Explore these top 10 tools for qualitative data analysis.

In the realm of research and data analysis , qualitative data provides a rich tapestry of information that goes beyond numbers, offering a nuanced understanding of human experiences, opinions, and behaviors. Effectively analyzing this type of data requires specialized tools that can handle the complexities of narratives, texts, and visual information. In this article, we unveil the 10 best tools for qualitative data analysis , empowering researchers, social scientists, and analysts to extract meaningful insights from qualitative datasets.

Overview: NVivo is a powerful qualitative data analysis (QDA) software that allows researchers to organize, analyze, and visualize unstructured information.

Key Features:

Supports various data formats, including text, audio, video, and images.

Facilitates coding, categorization, and theme identification.

Offers advanced search and query functions for in-depth analysis.

Provides robust collaboration features for team projects.

Overview: MAXQDA is a comprehensive QDA software designed for both beginners and experienced researchers, offering a user-friendly interface and advanced functionalities.

Enables the analysis of diverse data types, including text, audio, video, and images.

Facilitates coding, content analysis, and the creation of visualizations.

Supports teamwork through cloud-based collaboration features.

Integrates with transcription services for seamless analysis of audio and video data.

3. ATLAS.ti:

Overview: ATLAS.ti is a versatile qualitative data analysis tool that aids researchers in uncovering patterns, trends, and relationships within their data.

Supports text, image, audio, and video analysis.

Facilitates coding, memoing, and linking data segments.

Offers advanced visualization options, including network and geospatial analysis.

Provides robust export options for sharing results with others.

4. QDA Miner:

Overview: QDA Miner is a user-friendly qualitative data analysis tool that combines powerful analysis features with an intuitive interface.

Supports the analysis of text, images, PDFs, and Excel spreadsheets.

Facilitates coding, content analysis, and sentiment analysis.

Offers statistical tools for quantitative analysis when needed.

Provides visualization options such as word clouds and concept maps.

5. Dedoose:

Overview: Dedoose is a web-based qualitative and mixed-methods data analysis tool that focuses on collaboration and accessibility.

Supports a wide range of data types, including text, audio, video, and images.

Enables collaborative coding and analysis in real-time.

Provides advanced tools for exploring patterns and relationships.

Allows for easy integration of quantitative data for mixed-methods research.

6. Quirkos:

Overview: Quirkos is a qualitative analysis tool known for its simplicity and visual approach to coding and analyzing textual data.

Intuitive interface for easy coding and analysis.

Supports text-based data and offers compatibility with various file formats.

Facilitates collaboration through shared project files.

Provides colourful visualizations for quick insights.

7. HyperRESEARCH:

Overview: HyperRESEARCH is a straightforward qualitative data analysis tool that focuses on simplicity without compromising analytical depth.

Supports text-based data and various multimedia formats.

Facilitates coding, annotation, and memoing.

Provides a straightforward and user-friendly interface.

Allows for easy export of results for reporting and sharing.

8. Transana:

Overview: Transana is a specialized tool designed for the analysis of video and audio data, making it ideal for researchers working with qualitative multimedia content.

Supports transcription and analysis of video and audio data.

Enables coding and annotation of visual and auditory content.

Offers features for exploring patterns and themes within multimedia datasets.

9. Weft QDA:

Overview: Weft QDA is an open-source qualitative data analysis tool that provides essential features for coding and analyzing textual data.

Supports the analysis of text-based data.

Enables coding, categorization, and exploration of themes.

Simple and easy-to-use interface.

Open-source nature allows for community contributions and customization.

Overview: RQDA is an open-source R package that provides a platform for qualitative data analysis, offering flexibility and customization options.

Integrates with the R statistical computing environment.

Supports coding, categorization, and content analysis.

Allows for the creation of custom scripts and functions for advanced analysis.

Benefits from the extensive R community and ecosystem.

Conclusion:

The field of qualitative data analysis is evolving, and researchers now have access to a diverse range of tools that cater to different needs and preferences. Whether analyzing text, multimedia, or a combination of data types, these 10 tools empower researchers to uncover meaningful insights, enriching the understanding of complex human phenomena.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Qualitative Data Analysis

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Resources for Comparing QDA Tools

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There are a variety of Qualtiative Research/ QDA resources and comparison tutorials available online for free. Below are a few that have been selected for their relevance to the tools available to the NYU community.

• QDA Sample Data Google Folder with Qualitative Data
• Digital Tools for QDA A beginners guide tutorial to NYU Data Services supported qualitative data analysis software.
• How-To Guide: Digital Tools for Qualitative Data Analysis A textual explainer on how to choose and use NYU Data Services' supported qualitative data analysis software packages.
• Choosing CAQDAS -University of Surrey University of Surrey maintains a detailed comparison of most QDA software packages in PDF form. These are useful for in-depth comparison.
• Content Analysis - Chinese Media
• Fielding N. (1995). 'Choosing the Right Software Package',  ESRC Data Archive Bulletin January SHORT
• Lewis, B. (2004). 'NVivo 2.0 and Atlas.ti 5.0: A Comparative Review of Two Popular Qualitative Data-Analysis Programs',  Field Methods 16(4) 439-469
• Bird, K. (1995). Computer-Aided Qualitative Data Analysis: Theory, Methods and Practice. United Kingdom: Sage Publications (CA).​

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• Last Updated: Aug 27, 2024 5:40 PM
• URL: https://guides.nyu.edu/QDA

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If you're considering ATLAS.ti for qualitative data analysis, you're making a wise choice. Our top-rated QDA software is perfect for students, researchers, academic institutions, and commercial enterprises – offering a wide array of AI-driven analysis tools to help you succeed. Here are just a few reasons to choose ATLAS.ti:

An intuitive interface made for qualitative research

ATLAS.ti caters to both research professionals and beginners. It covers everything from qualitative text analysis and evaluation of customer interviews to web content analysis and specific business intelligence tasks.

Users can collect and analyze data on the major operating systems (Windows and macOS), even with our Web version for browsers. Our user-friendly qualitative data analysis software makes it easy to upload files and analyze data quickly and efficiently so that you can make the most out of your research.

Quick and easy AI-powered coding

Transforming text-based data into valuable insights can be time-consuming. With ATLAS.ti, you can import data from any source and gain deeper insights using AI. In addition, our qualitative data analysis software offers tools to automatically create coded segments in your data and quickly identify themes.

ATLAS.ti's qualitative data analysis tools enable you to organize all your text data (i.e., from customer interviews or focus groups) in one place. This way, you can analyze qualitative data faster than ever. Plus, you can utilize a code hierarchy with a tree structure for better code management.

Our automatic AI Coding feature uses OpenAI's GPT model, which can understand natural language on a human-like level. More than text mining: This groundbreaking analysis tool empowers users from all fields of work by drastically reducing the overall coding and analysis time.

Cover all your qualitative data analysis needs

Whether you rely on transcripts from focus groups , observation notes, survey responses , or even audio and video files – you can analyze it all with ATLAS.ti. Unlike quantitative data tools, our software supports all major forms of data so that you can conduct qualitative data analysis on any research project, even customer feedback, textual data, pictures, and video recordings.

Whatever it is, you can import data into one central location in ATLAS.ti – enabling you to leverage qualitative and mixed methods for your research projects.

Powerful data analysis on autopilot

Our artificial intelligence and machine learning tools make finding insights in your research project easy. Qualitative data analysis tools such as Sentiment Analysis and Opinion Mining can perform text analysis across multiple documents to analyze large projects faster and more insightful.

Whether you want to analyze customer data or identify keywords from research materials, our AI tools can help you finish the job quickly. Regardless of what you want to achieve: pure qualitative data analysis or mixed methods research , ATLAS.ti offers the leading solution trusted by academics and businesses.

Deeper insights through qualitative analysis

We understand that any qualitative data analysis tool is only as powerful as the insights they provide to you and your audience. ATLAS.ti is more than a text analyzer – we develop our QDA software to visualize your data analysis in multiple formats:

Bar charts, Sankey diagrams, word clouds, and network visualizations help you identify data themes and patterns for robust and accurate insights.

Seamless collaboration across teams

Qualitative research and mixed methods research often relies on collaboration between team members. That's why multi-user licenses for ATLAS.ti allow you to share our qualitative data analysis software with your colleagues.

Now all your team members can work together on the same project to analyze qualitative data . Unlike other QDA software, ATLAS.ti allows you to capture customer insights with the collective power of your colleagues.

Expert support and training

Customer satisfaction is our top priority at ATLAS.ti. We offer technical and methodological support for all users, whether conducting mixed methods research, qualitative research , statistical analysis, thematic analysis , market research, or academic research.

ATLAS.ti experts worldwide are always just a click away from supporting the users of our #1 software for qualitative data analysis.

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NVivo 15: Grow Your Research Team with Qualitative Data Analysis Software (QDA Software)

The Most Trusted QDA Software is Even Better

We're thrilled to announce the release of NVivo 15, the latest version of Lumivero's most trusted qualitative data analysis (QDA) software*. This release is designed to revolutionize the way researchers analyze qualitative data, incorporating advanced Lumivero AI Assistant features and new tools that enhance efficiency across the board.

Driven by customer feedback, NVivo 15 addresses the growing need for more streamlined workflows and efficient qualitative research methods, ensuring researchers can achieve insightful and accurate results with greater ease and speed. With NVivo 15, researchers can organize and explore data from any source, visualize connections that are difficult to find manually, and use our Lumivero AI Assistant to start to dive into the details.

Continue reading to learn more about NVivo 15 or request a demo to see for yourself!

SEE NVIVO IN ACTION

Contact us to learn how you can provide NVivo to all researchers at your college or university and be assured that they are using the most widely cited QDA software in the world.

Revolutionizing Qualitative Data Analysis with AI

The standout feature of NVivo 15 is its groundbreaking Lumivero AI Assistant. This powerful tool is designed to enhance data analysis efficiency and accuracy, giving researchers more time to focus on data analysis for their research project.

New Features with NVivo 15’s Lumivero AI Assistant:

• Unpack Jargon or Technical Language: Leverage AI text summarization to explain unfamiliar terms or local idioms, saving you time from searching for answers. Quickly select the text and have NVivo 15 summarize the selection which is then added to the document as an annotation.
• Dive into Reflective Thinking: Enjoy diving into the depths of rich data but need a break from reading endless pages? NVivo’s AI document summarization brings memoing to the forefront of analysis. Seamlessly select any type of document or document(s) within NVivo 15 and summarize each of them in seconds allowing you to get a sense of each document immediately. The summaries are added in a memo linked to its document where you can reflect further, iteratively returning to each document for further insights and/or start to develop a coding framework.
• Fine tune your coding: Ever lose the rhythm when coding for a while? Dig deeper into codes you already created by using NVivo’s AI coding suggestions to suggest finer level codes. The Lumivero AI Assistant shows the evidence for the proposed codes, giving the researcher total control over what to accept. With NVivo, nothing is cast in stone, giving you the freedom to explore your data dynamically and intuitively.

All your data stays your data when using NVivo 15’s Lumivero AI Assistant. Once the AI completes its task, the information is sent to your NVivo project and deleted from the local servers.

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Working with qualitative data can be messy, but NVivo makes organizing your data a breeze. With NVivo, you can keep all your data organized and accessible, allowing you to pull out insights as needed for effective qualitative analysis. This organizational capability helps to maintain a clear mind and a focused research process, making it easier to manage complex data sets and ensure nothing is overlooked.

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NVivo excels in providing deeper insights into your data. It gives a voice to every data point, enabling intricate comparisons not only among respondents but also their organizations or any unit under analysis. Cases, classifications, and attributes highlight differences and unexpected similarities, providing a richer understanding of your data.

For those not satisfied with one-dimensional analysis, NVivo’s crosstabs feature uncovers hidden patterns by contrasting differences among respondents. This tool for qualitative data analysis offers profound insights and clarity, and for those needing more statistical analysis, NVivo allows for exporting data to Lumivero’s XLSTAT . Additionally, NVivo offers the most comprehensive set of text analysis tools and coding queries, enabling you to cut your data in myriad ways to reveal hidden patterns and fresh insights.

Prioritizing Data Privacy and Security in Qualitative Data Analysis Software

As with all Lumivero solutions, NVivo 15 prioritizes data privacy and security. The Lumivero AI Assistant ensures that all your data remains secure throughout the analysis process. Once the AI completes its task, the information is securely sent to your NVivo project and deleted from local servers, preventing any data from residing on outside servers. This ensures that your data stays confidential and protected.

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With NVivo 15, users can not only open work on projects across both Mac and Windows with ease but also all Mac teams can use NVivo Collaboration Cloud . This ensures greater flexibility and productivity in their qualitative data analysis.

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*(Scopus Database, NVivo citations compared with competitors 2010-2023)

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Unquestionably, data analysis is the most complex and mysterious of all of the phases of a qualitative project, and the one that receives the least thoughtful discussion in the literature. For neophyte nurse researchers, many of the data collection strategies involved in a qualitative project may feel familiar and comfortable. After all, nurses have always based their clinical practice on learning as much as possible about the people they work with, and detecting commonalities and variations among and between them in order to provide individualised care. However, creating a database is not sufficient to conduct a qualitative study. In order to generate findings that transform raw data into new knowledge, a qualitative researcher must engage in active and demanding analytic processes throughout all phases of the research. Understanding these processes is therefore an important aspect not only of doing qualitative research, but also of reading, understanding, and interpreting it.

For readers of qualitative studies, the language of analysis can be confusing. It is sometimes difficult to know what the researchers actually did during this phase and to understand how their findings evolved out of the data that were collected or constructed. Furthermore, in describing their processes, some authors use language that accentuates this sense of mystery and magic. For example, they may claim that their conceptual categories “emerged” from the data 1 —almost as if they left the raw data out overnight and awoke to find that the data analysis fairies had organised the data into a coherent new structure that explained everything! In this EBN notebook, I will try to help readers make sense of some of the assertions that are made about qualitative data analysis so that they can develop a critical eye for when an analytical claim is convincing and when it is not.

Qualitative data

Qualitative data come …

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Home » Best free online qualitative data analysis tools

Qualitative data analysis (QDA) is essential for researchers looking to extract meaningful insights from complex data. As the demand for efficient tools grows, many free options have emerged, allowing researchers to navigate qualitative data without financial barriers. These tools can help streamline your analytical process, whether you’re analyzing interviews, focus groups, or open-ended survey responses.

In this guide, we will explore the top free QDA tools available online. Each tool offers distinct features that cater to different research needs, from data organization to comprehensive analysis. Understanding these options can significantly enhance your research outcomes, making it easier to derive actionable insights from qualitative data.

Exploring the Need for Qualitative Data Analysis Tools

Qualitative data analysis tools play a crucial role in uncovering insights from complex datasets. These tools transform raw data into meaningful information, making them invaluable for researchers and organizations. Without the right tools, it becomes challenging to analyze themes, sentiments, and trends effectively. This is where top free QDA tools become particularly significant, offering functionalities that cater to various analytical needs.

When exploring the need for qualitative data analysis tools, several key points emerge. First, they enhance the ability to organize and code data efficiently. Next, they facilitate collaboration among team members, enabling multiple users to work together seamlessly. Additionally, these tools often include visualization features, which help in presenting findings clearly and impactfully. Finally, using accessible tools allows researchers to focus on their analysis without getting bogged down in technical complexities, fostering deeper insights and informed decision-making.

Why Qualitative Data Analysis is Crucial

Qualitative data analysis plays a vital role in understanding complex human behaviors and feelings. By interpreting text, audio, and video data, researchers can uncover nuanced insights that numbers alone cannot provide. This type of analysis allows analysts to explore participants’ stories and identify themes, ultimately leading to richer findings. Utilizing top free QDA tools can make this process easier and more efficient for researchers, enabling them to focus on deriving meaningful conclusions rather than getting bogged down in tedious data processing.

Moreover, qualitative analysis helps to eliminate biases that might skew research outcomes. With the right tools, researchers can ensure their findings are robust and actionable. These online resources not only support automated transcription and analysis but also promote collaboration among teams. Investing time in qualitative data analysis ultimately pays off by providing depth to research, informing better decision-making, and leading to more effective strategies.

Key Features to Look for in QDA Tools

When exploring top free QDA tools, several key features can significantly enhance the qualitative data analysis experience. First, user-friendly interfaces and intuitive navigation are crucial, as they allow researchers to organize and analyze their data effortlessly. Additionally, robust tagging and coding capabilities enable users to categorize responses effectively, which helps in identifying themes and trends.

Another important aspect is the ability to visualize data through charts, graphs, and word clouds. These visual tools not only present findings clearly but also foster better understanding and communication of insights. Furthermore, built-in reporting functions can streamline the process of creating comprehensive reports, making it easier to share findings with stakeholders.

Lastly, consider tools that offer collaboration features, allowing team members to work together seamlessly on projects. By focusing on these features, users can select the best free QDA tools that meet their analysis needs and enhance research outcomes.

Top Free QDA Tools Available Online

In the realm of qualitative data analysis, several free QDA tools stand out for their usability and functionality. These tools cater to various research needs, enabling users to analyze text, audio, and video efficiently. One notable tool is NVivo, which offers a free trial for enhanced qualitative analysis capabilities, allowing users to code and categorize data with ease. Another option is Atlas.ti, which provides a user-friendly interface suitable for beginners and allows for detailed coding of qualitative data.

Additionally, Quirkos is a unique choice that visualizes data in an intuitive manner, making it easier to identify themes and patterns. For those looking for cloud-based solutions, Dedoose excels in collaboration, enabling team members to work on projects together seamlessly. Each of these Top Free QDA Tools offers distinct features, ensuring a suitable option for every researcher, regardless of experience.

Tool 1: NVivo – A Comprehensive Solution

NVivo stands out as one of the top free QDA tools for qualitative data analysis due to its user-friendly interface and powerful features. It allows researchers to conduct detailed analyses of interview transcripts and other forms of qualitative data with ease. This tool simplifies complex analytical processes, making it accessible to both new and experienced researchers. It also provides functionalities such as coding, theme identification, and collaboration options, enhancing the overall research experience.

Moreover, NVivo's comprehensive approach makes it a favorite among those seeking to extract meaningful insights from their data. The ability to visualize data and generate reports ensures that users can present their findings effectively. By offering both depth and flexibility, NVivo proves to be an excellent choice for many looking to navigate the intricacies of qualitative analysis.

Tool 2: ATLAS.ti – User-friendly Interface for Beginners

For those new to qualitative data analysis, ATLAS.ti stands out as one of the top free QDA tools due to its user-friendly interface. Users appreciate how intuitive the platform is, making it accessible for beginners who may feel overwhelmed by other software options. With straightforward navigation, users can easily import data, tag important themes, and visualize their findings through various reports and diagrams.

Getting started with ATLAS.ti offers several benefits. First, the onboarding process includes helpful tutorials that guide users through its features, ensuring a smooth introduction to qualitative analysis. Second, the platform supports various data formats, allowing researchers to import interviews, surveys, and images seamlessly. Lastly, its collaborative features enable multiple users to work on projects together, enhancing teamwork and improving project outcomes. Overall, ATLAS.ti serves as a solid entry point for those exploring qualitative data without the intimidation often associated with complex tools.

How to Choose the Best Free QDA Tool for Your Needs

When selecting the top free QDA tools, it is essential to consider your specific research needs. Begin by identifying the types of qualitative data you work with, such as text, audio, or video. This clarity helps you narrow down options to those suited for your data type. Next, evaluate the features offered by each tool, such as coding capabilities, transcription options, and collaboration features. These functionalities can greatly affect your analysis efficiency and insight generation.

Finally, consider the usability of the tools, including their learning curves and user support. Some platforms come with robust guides and community support, which can be invaluable as you start using them. By focusing on your unique requirements and comparing the offerings, you can choose the best free QDA tool that enhances your qualitative analysis while ensuring a smooth experience.

Evaluating the Learning Curve and User Support

When evaluating top free QDA tools, it’s essential to consider the learning curve and user support these platforms offer. Many users, especially beginners, find themselves challenged by complex interfaces and jargon-heavy documentation. A user-friendly design can significantly enhance the learning experience, allowing researchers to focus on their data instead of struggling with software features.

Good user support is another critical factor. Responsive help centers and community forums can help users resolve issues quickly. Tutorial videos and step-by-step guides are valuable resources that cater to different learning preferences. By prioritizing both simplicity in use and robust support, researchers can maximize the benefits of their chosen qualitative data analysis tools and streamline their research processes.

Assessing Tool Functionalities and Limitations

When assessing the functionalities and limitations of top free qualitative data analysis (QDA) tools, it's essential to understand how these tools can meet your specific needs. Each tool comes with unique features designed to assist users in organizing and interpreting qualitative data effectively. Features may include text analysis, coding options, and visualization capabilities. However, even the best tools can have restrictions, such as storage limits, lack of advanced functionality, or less user-friendly interfaces.

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Exploring Thematic Analysis in Qualitative Research

• August 2024
• In book: Data Collection and Analysis in Scientific Qualitative Research (pp.253-294)
• Publisher: IGI Global

• Université Ibn Tofail

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• Open access
• Published: 28 August 2024

Facilitators and barriers of midwife-led model of care at public health institutions of dire Dawa city, Eastern Ethiopia, 2022: a qualitative study

• Mickiale Hailu 1 ,
• Aminu Mohammed 1 ,
• Daniel Tadesse 1 ,
• Neil Abdurashid 1 ,
• Legesse Abera 1 ,
• Samrawit Ali 2 ,
• Yesuneh Dejene 2 ,
• Tadesse Weldeamaniel 1 ,
• Meklit Girma 3 ,
• Tekleberhan Hailemariam 1 ,
• Netsanet Melkamu 1 ,
• Tewodros Getnet 1 ,
• Yibekal Manaye 1 ,
• Tariku Derese 1 ,
• Muluken Yigezu 1 ,
• Natnael Dechasa 1 &
• Anteneh Atle 1  

BMC Health Services Research volume  24 , Article number:  998 ( 2024 ) Cite this article

Metrics details

The midwife-led model of care is woman-centered and based on the premise that pregnancy and childbirth are normal life events, and the midwife plays a fundamental role in coordinating care for women and linking with other health care professionals as required. Worldwide, this model of care has made a great contribution to the reduction of maternal and child mortality. For example, the global under-5 mortality rate fell from 42 deaths per 1,000 live births in 2015 to 39 in 2018. The neonatal mortality rate fell from 31 deaths per 1,000 live births in 2000 to 18 deaths per 1,000 in 2018. Even if this model of care has a pivotal role in the reduction of maternal and newborn mortality, in recent years it has faced many challenges.

To explore facilitators and barriers to a midwife-led model of care at a public health institution in Dire Dawa, Eastern Ethiopia, in 2021.

Methodology

: A qualitative approach was conducted at Dire Dawa public health institution from March 1–April 30, 2022. Data was collected using a semi-structured, in-depth interview tool guide, focused group discussions, and key informant interviews. A convenience sampling method was implemented to select study participants, and the data were analyzed thematically using computer-assisted qualitative data analysis software Atlas.ti7. The thematic analysis with an inductive approach goes through six steps: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up.

Two major themes were driven from facilitators of the midwife-led model of care (professional pride and good team spirit), and seven major themes were driven from barriers to the midwife-led model of care (lack of professional development, shortage of resources, unfair risk or hazard payment, limited organizational power of midwives, feeling of demoralization absence of recognition from superiors, lack of work-related security).

The midwifery-led model of care is facing considerable challenges, both pertaining to the management of the healthcare service locally and nationally. A multidisciplinary and collaborative effort is needed to solve those challenges.

Peer Review reports

Introduction

A midwife-led model of care is defined as care where “the midwife is the lead professional in the planning, organization, and delivery of care given to a woman from the initial booking to the postnatal period“ [ 1 ]. Within these models, midwives are, however, in partnership with the woman, the lead professional with responsibility for the assessment of her needs, planning her care, referring her to other professionals as appropriate, and ensuring the provision of maternity services. Most industrialized countries with the lowest mortality and morbidity rates of mothers and infants are those in which midwifery is a valued and integral pillar of the maternity care system [ 2 , 3 , 4 , 5 ].

Over the past 20 years, midwife-led model of care (MLC) has significantly lowered mother and infant mortality across the globe. In 2018, there were 39 deaths for every 1,000 live births worldwide, down from 42 in 2015. From 31 deaths per 1,000 live births in 2000 to 18 deaths per 1,000 in 2018, the neonatal mortality rate (NMR) decreased. The midwifery-led care approach is regarded as the gold standard of care for expectant women in many industrialized nations, including Canada, Australia, the United Kingdom, Sweden, the Netherlands, Norway, and Denmark. Evidence from those nations demonstrates that women and babies who get midwife-led care, as opposed to alternative types of care, experience favorable maternal outcomes, fewer interventions, and lower rates of fetal loss or neonatal death [ 6 , 7 , 8 ].

In Pakistan, the MLC was accompanied by many challenges. Some of the challenges were political threats, a lack of diversity (midwives had no opportunities for collaborating with other midwives outside their institutions), long duty hours and low remuneration, a lack of a career ladder, and a lack of socialization (the health centers are isolated from other parts of the country due to relative geographical inaccessibility, transportation issues, and a lack of infrastructure). Currently, in Pakistan, 276 women die for every 100,000 live births, and the infant mortality rate is 74/1000. But the majority of these deaths are preventable through the midwife-led care model [ 7 ].

The MLC in African countries has faced many challenges. Shortages of resources, work overload, low inter-professional collaboration between health facilities, lack of personal development, lack of a well-functioning referral system, societal challenges, family life troubles, low professional autonomy, and unmanageable workloads are the main challenges [ 8 ].

Due to the aforementioned challenges, Sub Saharan Africa (SSA) is currently experiencing the highest rate of infant mortality (1 in 13) and is responsible for 86% of all maternal fatalities worldwide. As a result, it is imperative to look at the MLC issues in low-income countries, which continue to be responsible for 99% of all maternal and newborn deaths worldwide [ 8 , 9 ].

Ethiopia’s has a Maternal mortality rate (MMR) and NMR of 412 per 100,000 live births and 33 per 1000 live births, respectively, remain high, making Ethiopia one of the largest contributors to the global burden of maternal and newborn deaths, placed 4th and 6th, although MLC could prevent a total of 83% of all neonatal and maternal fatalities in an environment that supports it. The MMR & infant mortality rate (IMR) in the research area were indistinguishable from that, at 150 per 100,000 live births and 67 fatalities per 1,000 live births, respectively [ 10 , 11 , 12 , 13 ].

Since the Federal Ministry of Health is currently viewing midwifery-led care as an essential tool in reducing the maternal mortality ratio and ending preventable deaths of newborns, exploring the facilitators and barriers of MLC may have a great contribution to make in reducing maternal and newborn mortality [ 14 ]. Since there has been no study done in Ethiopia or the study area regarding the facilitators and barriers of MLC, the aim of this research was to explore the facilitators and barriers of MLC in Dire Dawa City public health institutions.

In so doing, the research attempted to address the following research questions:

What were the facilitators for a midwife-led model of care at the Dire Dawa city public health institution?

What were the barriers to a midwife-led model of care at the Dire Dawa city public health institution?

Study setting and design

Institutional based qualitative study was conducted from March 01-April 30, 2022 in Dire Dawa city. Dire Dawa city is one of the federal city administrations in Ethiopia which is located at the distance of 515killo meters away from Addis Ababa (the capital city) to the east. The city administration has 9 urban and 38 rural kebeles (kebeles are the smallest administrative unit in Ethiopia). There are 2 government hospitals, 5 private hospitals, 15 health centers, and 33 health posts. The current metro area population of Dire Dawa city is 426,129.Of which 49.8% of them are males and 50.2% females. The total number of women in reproductive age group (15–49 years) is 52,673 which account 15.4% of the total population. It has hot temperature with a mean of 25 degree centigrade [ 15 ].

Study population and sampling procedure

The source population for this study included all midwives who worked at Dire Dawa City public health facilities as well as key informants from appropriate organizations (the focal person for the Ethiopian Midwives Association and maternal and child health (MCH) team leaders). The study encompassed basically 41 healthcare professionals who worked in Dire Dawa public health institutions in total, and the final sample size was decided based on the saturation of the data or information.

From the total 15 Health centers and 2 Governmental Hospitals found in Dire Dawa city administration, 8 Health centers and 2 Governmental Hospitals were selected by non-probability purposive sampling method. In addition to that a non-probability convenience sampling method was used to select midwives who were working in Dire Dawa city public health institutions and key informants from the relevant organization such as Ethiopian midwives association focal person and MCH team leaders. Midwives who were working for at least six months in the institution were taken as inclusion criteria while those who were working as a free service were excluded from the study.

Data collection tool and procedures

Focus groups, in-depth interviews, and key informant interviews were used in collecting data. A voice recorder, a keynote-keeping, and a semi-structured interview tool were all used to conduct the interviews. Voluntary informed written consent was obtained from the study participant’s before they participated in the study. Then an in-depth interview and focus group discussion were held with midwives chosen from various healthcare organizations. The MCH department heads and the Dire Dawa branch of the Ethiopian Midwife Association served as the key informants. In-depth interview (IDI) and key informant interviews (KII) with participants took place only once and lasted for roughly 50–60 min. In the midwives’ duty room, the interview was held. Six to eight people participated in focus group discussions (FGD), which lasted 90 to 100 min. Two midwives with experience in gathering qualitative data gathered the information.

Data quality control

The qualitative design is prone for bias but open-ended questions were used to avoid acquiescence and 2 day proper training was given for the data collector regarding taking keynotes and recording using a tape recorder. For consistency and possible modification, a pretest was done in one FGD and In-depth interviews at non selected health institutions of Dire Dawa city administrations. A detailed explanation was given for the study participants about the objectives of the study prior to the actual data collections. All (FGDs, key informant interview and In-depth interviews) were taken in a silent place.

Data analysis

Atlas.ti7, a qualitative data analysis program, was used for analyzing the data thematically. An inductive approach to thematic analysis involves six steps: familiarization, coding, generation of themes, review of themes, defining and naming of themes, and writing up. By listening to the taped interview again, the data was transcribed. The participants’ well-spoken verbatim was used to extract and describe the inductive meanings of the statements. The data was then coded after that. Each code describes the concept or emotion made clear in that passage of text. Then we look at the codes we’ve made, search for commonalities, and begin to develop themes. To ensure the data’s accuracy and representation, the generated themes were reviewed. Themes were defined and named, and then the analysis of the data was written up.

Trustworthiness of data

Meeting standards of trustworthiness by addressing credibility, conformability, and transferability ensures the quality of qualitative research. Data triangulation, data collection from various sites and study participants, the use of multiple data collection techniques (IDI, KII, and FGD), multiple peer reviews of the proposal, and the involvement of more than two researchers in the coding, analysis, and interpretation decisions are all instances of the methods that were used in order to fulfill the criteria for credibility. To increase its transferability to various contexts, the study gave details of the context, sample size and sampling method, eligibility criteria, and interview processes. To ensure conformability, the research paths were maintained throughout the study in accordance with the work plan [ 16 , 17 ].

Background characteristics of the study participants

In this study, a total of 41 health care providers who are working in Dire Dawa public health facilities participated in the three FGDs, six KIIs, and fifteen IDIs. The years of experience of study participants range from one year to 12 years. The participants represented a wide age range (30–39 years), and the educational status of the respondents ranged from diploma to master’s degree. (Table  1 )

As shown in Table  2 , from the qualitative analysis of the data, two major themes were driven from facilitators of MLC, and seven major themes were driven from barriers to MLC. (Table  2 ).

Facilitators of midwife-led model of care at a public health institution of Dire Dawa city, Eastern Ethiopia, in 2021

Professional pride.

This study found that saving the lives of mothers and newborns was a strong facilitator. Specifically, it was motivational to have skills within the midwifery domain, such as managing the full continuum of care during pregnancy and labour, supporting women in having normal physiologic births, being able to handle complications, and building relationships with the women and the community, as mentioned below by one of the IDI participants.

“I am so proud since I am a midwife; nothing is more satisfying than seeing a pregnant mother give birth almost without complications. I always see their smile and happiness on their faces , especially in the postpartum period , and they warmly thank me and say , “Here is your child; he or she is yours.” They bless me a lot. Even sometimes , when they sew me in the transport area , cafeteria , or other area , they thank me warmly , and some of them also want to invite me to something else. The sum total of those things motivates me to be in this profession or to provide midwifery care.“ IDI participants.

This finding is also supported by other participants in FGD.

“We have learned and promised to work as midwives. We are proud of our profession , to help women and children’s health. The greatest motivation is that we are midwives , we love the profession , and we are contributing a great role in decreasing maternal and child mortality….” FGD discussant.

Good teamwork

The research revealed that good midwifery teamwork and good social interaction within the staff have become facilitators of MLC. FGD participants share their experiences of working in a team.

“In our facility , all the midwives have good teamwork; we have good communication , and we share client information accurately and timely. In case a severe complication happens , we manage it as a team , and we try to cover the gap if some of our staff are absent. Further from that , we do have good social interactions in the case of weeding , funeral ceremonies , and other social activities. We do have good team spirit; we work as a team in the clinical area , and we also have good social relationships. “If some of our staff gets sick or if she or he has other social issues , the other free staff will cover her or his task.” FGD discussant.

Another participant from IDI also shared the same experience regarding their good teamwork and their social interactions.

“As a maternal and child health team , we do have a good team spirit , not only with midwives but also with other professions. We are not restricted by the ward that we assign. If there is a caseload in any unit , some midwives will volunteer to help the other team. Most of the time in the night , we admit more than 3 or 4 labouring mothers at the same time. Since in our health center only one midwife is assigned in the night , we always call nurses to help us. This is our routine experience.” IDI participants.

Barriers of midwife-led model of care at a public health institution of Dire Dawa city, Eastern Ethiopia, in 2021

Lack of professional development.

This study revealed that insufficient opportunities for further education and updated training were the main barriers for MLC. Even the few trainings and update courses that were actually arranged were unavailable to them, either because they did not meet the criteria seated or because the people who work in administration were selected. Even though opportunities are not arranged for them to upgrade themselves through self-sponsored. One of the participants from IDI narrates her opinion about opportunities for further education as follows:

“Training and updates are not sufficient; currently we are almost working with almost old science. For example , the new obstetrics management protocol for 2021 has been released from the ministry of health , and many things have changed there. But we did not receive any training or even announcements. Even the few trainings and update courses that were truly organized and turned in to us are unavailable since the selection criteria are not fair. As a result , we miss those trainings either because we did not meet the selection criteria or because those who work in administration are prioritized.” IDI participant.

FGD discussants also support this idea. She mentioned that even though opportunities are not arranged for them to upgrade themselves through self-sponsorship,

“There is almost no educational opportunity in our institution. Every year , one or two midwives may get institutional sponsorship. Midwives that will be selected for this opportunity are those who have served for more than five to ten years. Imagine that to get this chance , every midwife is expected to serve five or more years. Not only this , even if staff want to learn or upgrade at governmental or private colleges through self-sponsored programmes , whether at night or in an extension programme , they are not cooperative. Let me share with you my personal experience. Before two years , I personally started my MSc degree at Dire Dawa University in a weekend programme , and I have repeatedly asked the management bodies to let me free on weekends and to compensate me at night or any time from Monday to Friday. Since they refuse to accept my concern , I withdraw from the programme.“ FGD discussant.

Shortage of resource

The finding indicates that a shortage of equipment, staff, and rooms or wards was a challenge for MLC. Midwives claimed they were working with few staff, insufficient essential supplies, and advanced materials. This lack of equipment endangers both the midwives and their patients. One of the participants from IDI narrates her opinion about the shortage of resources as follows:

“Of course there is a shortage of resources in our hospital , like gloves and personal protective devices. Even the few types of medical equipment available , like the autoclave , forceps , vacuum delivery couch , and BP apparatus , are outdated , and some of them are unfunctional. If you see the Bp apparatus we used in ANC , it is digital but full of false positives. When I worked in the ANC , I did not trust it and always brought the analogue one from other wards. This is the routine experience of every staff member.“ IDI participants.

Another participant from IDI also shared the same experience regarding the crowdedness of rooms or wards.

“In our health center , there are no adequate wards or rooms. For example , the delivery ward and postnatal ward are almost in one room. Postnatal mothers and neonates did not get enough rest and sleep because of the sound of laboring mothers. Not only is this , but even the antenatal care and midwifery duty rooms are also very narrow.“ IDI participants.

The study also revealed midwifery staff were pressured to work long hours because they were understaffed, which in turn affected the quality of midwifery care. The experience of a certain midwife is shared as follows:

“I did not think that the management bodies understood the risk and stress that we midwives face. They did not want to consider the risk of midwives even equal to that of other disciplines but lower than the others. For example , in our health centre , during the night , only one midwife is assigned for the next 12 hours , but if you see in the nurse department , two or more nurses are assigned at night in the emergency ward.” IDI participants.

The discussion affirms the fact that being understaffed and not having an adequate allocation of midwife professionals on night shifts are affecting labouring mothers’ ability to get sufficient health midwifery care. The above narration is also supported by the FGD discussant.

“In our case , only one midwife is assigned to the labour ward during the night shift. I think this is the main challenge for midwives that needs attention. Let me share with you my experience that happened months before. While I was on night shift , two labouring mothers were fully dilated within three or four minutes. It was very difficult for me , to manage two labouring mothers at the same time. Immediately , I call one of my nurse friends from the emergency department to help me. If my friend was so busy , what could happen to the labouring mother and also to me? This is not only my experience but also the routine experience of other midwives.” FGD discussant.

Unfair risk or hazard payments

It is reported that the compensation amount paid for risk is lower than in other health professions. The health risks are not any less, but the remuneration system failed to capture the need to fairly compensate midwifery professionals. The narration from the FGD discussant regarding unfair payment is mentioned below.

“Only 470 ETB is paid for midwives as risk payments , which is incomparable with the risks that midwives are facing. But contrary to that , the risk payments for nurses (in emergencies) are about 1200 Ethiopian birr (ETB) , and Anesthesia is 1000 ETB. I did not want to compare my profession with other disciplines , but with the lowest cost , how the risk of midwifery cannot be equal to that of nursing and other professions. I did not know whose professionals made such types of unfair decisions and with what scientific background or base this calculation was done . ” FGD discussant.

The above finding is also supported by an IDI participant.

“………………………….Even though the midwifery profession is full of risks , with the current Ethiopian health care system , midwives are being paid the lowest risk payments compared to other disciplines…………….” IDI participants.

Limited organizational power of midwives

Midwives’ interviews reported that limited senior midwifery positions in the health system have become the challenge of midwifery care. This constrains the decision-making power and capability of midwives. This was compounded by limited opportunities for midwifery personnel to address their concerns to the responsible bodies, as stated by one of the key informants.

“Our staff has many concerns , especially professional-related concerns , which can contribute to the quality of midwifery care. Personally , as department head , I have tried to address those concerns in different management meetings at different times. But since the leadership positions are dominated by other disciplines , many of our staff concerns have not been solved yet. But let me tell you my personal prediction… If those concerns are not solved early and if this trend continues , the quality of midwifery care will be in danger.“ Participant from Key Informant.

The above finding is also supported by another IDI participant.

“In our hospital , at every hierarchal and structural level , midwives are not well represented. That is why all of our challenges or concerns have not been solved yet. For example , as a structure in the Dire Dawa Health Office (DDHO) , there is a team of management related to maternal and child health. But unfortunately , those professionals working there are not midwives. I was one of three midwives chosen to meet with Dr. X (former DDHO leader) to discuss this issue. At the time , we were reaching an agreement that two or three midwives would be represented on that team. But since a few months later the leader resigned , the issue has not gotten a solution yet.“ IDI participant.

Feeling of demoralization

One of the main concerns reported by the participants during the interviews was a feeling of demoralization induced by both their clients and their supervisors about barriers to midwifery care. They reported having been verbally abused by their patients, something that made them feel that their hard work was being undermined, as stated by an FGD participant.

“I don’t think there is any midwife who would be happy for anybody to lose their baby , or that there is any midwife who would want a woman to die. These things are accidents , but the patient and leaders will always blame the midwife.” FDG discussant.

A narration from an IDI participant also mentioned the following:

“……….If something happens , like a conflict with the patients or clients , the management is on the patient side. Not only that , the way in which they communicate with us is in an aggressive or disrespectful manner . ” IDI participant.

Absence of recognition or /motivation from superiors

This study revealed that midwives experience a loss of motivation at work due to limited support from their superiors. Their effort is used only for reporting purposes. A midwife from FGD shared her experience as follows.

“In our scenario , till the nearest time , the maternal and child health services are provided in a good way. But this was not easy; it is the cumulative effort of midwives. But unfortunately , only those in managerial positions are recognized. Nothing was done for us despite our efforts. To me , our efforts are used only for reporting purposes.” FGD discussant.

This finding was also supported by IDI participants.

“Even though we have good achievements in the MCH services , there is no motivation mechanism done to motivate midwives.” But if something or a minor mistake happens , they are on the front lines to intimidate us or write a warning letter. Generally , their concern is a report or a number issue. We are tired of such types of scenarios.” IDI participant.

Insufficient of work-related security

One of the main concerns reported by the participants during the interviews was the work related security, which has become a challenge for MLC. The midwives’ work environment was surrounded by insecurity, especially during night shifts, when midwives were facing verbal and even physical attack, as mentioned by participants.

“In the labour ward , especially at night , we face many security-related issues. The families of labouring mothers , especially those who are young , are very aggressive. Sometimes they even want to enter the delivery room. They did not hear what we told them to do , but if they hear any labour sounds from their family , they disturb the whole ward. This leads to verbal abuse , and sometimes we face physical abuse. There may be one or two security personnel at the main gate , but since the delivery ward is far from the main gate , they do not know what is happening in the delivery ward. When things become beyond our scope , we call security guards. Immediately after the security guards go back , similar things will continue. What makes it difficult to manage such situations is that only one midwife is assigned at night , and labouring mothers will not get quality midwifery care.” IDI participant.

FGD discussants also shared their experience that their working environment is full of insecurity.

“In case any complications occur , especially at night , it is very difficult to tell the labouring mother’s family or husband unless we call security personnel. It is not only swearing that we face but also that they intimidate us.” FDG discussant.

Discussions

The aim of this study was to explore facilitators’ and barriers to a midwifery-led model of care at Dire Dawa public health facilities. In this study, professional pride was the main facilitator of the midwifery-led model of care. Another qualitative study that examined the midwifery care challenges and factors that motivate them to remain in their workplace lends confirmation to this conclusion. It was found that a strong feeling of love for their work was the main facilitator’s midwifery-led model of care [ 9 ]. Having a good team spirit was also another facilitator’s midwifery-led model of care in our study. Another study’s findings confirmed this one, which emphasizes that building relationships with the midwives, women, and community was the driving force behind providing midwifery care [ 7 , 18 ].

The midwives in this study expressed a need for additional professional training, updates, and competence as part of their continuing professional development. Similar findings have been reported in the worldwide literature that midwives were struggling for survival due to a lack of limited in-service training opportunities to improve their knowledge and skills [ 19 ]. This phenomenon does not seem to differ between settings in high-, middle-, and low-income countries [ 7 , 9 , 18 ], in which midwives experienced difficult work situations due to a lack of professional development to autonomously manage work tasks, which made them feel frustrated, guilty, and inadequate. As such, this can contribute to distress and burnout, which in turn prevent midwives from being able to provide quality care and can eventually cause them to leave the profession [ 19 ].

Shortages of resources (shortage of staff, lack of physical space, and equipment) were the other reported barriers to midwifery care explored in this study. They reported that they are working in an environment with a shortage of resources, which leads to poor patient outcomes. This finding is supported by many other studies conducted around the globe [ 20 , 21 , 22 , 23 ]. Another qualitative finding, which likewise supports the aforementioned finding, which emphasizes that a shortage of resources was reported as a barrier to providing adequate midwifery care [ 19 ]. Delivery attended by skilled personnel with appropriate supplies and equipment has been found to be strongly associated with a reduction in child and maternal mortality [ 24 ].

The feeling of demoralization and lack of motivation from their superiors were other barriers to midwifery care explored in this study. This finding is concurrent with other studies conducted around the globe [ 19 , 25 , 26 , 28 ]. The above finding is also is in accord with another qualitative narration, which emphasizes that feelings of demoralization and a lack of motivation were the main challenges of midwifery care [ 22 ]. Positive support from supervisors has been demonstrated to be important for the quality of services that health workers are able to deliver. In the World Health Organization’s report on improving performance in healthcare, the WHO stresses that supportive supervision can contribute to the improved performance of health workers [ 27 ].

Unfair risk payment was the other challenge identified by the current study. Even though there is no difference in the risk they face among health professionals, the risk payment for midwives is very low compared to others. This finding was in conformity with another qualitative narration, which emphasizes that the lack of an equitable remuneration system was experienced by the DRC midwives, and it has also been confirmed to be highly problematic in other studies in low- and middle-income settings [ 7 , 8 , 22 , 28 ], leading to serious challenges. In settings where salaries are extremely low or unpredictable, proper remuneration is seen as crucial to worker motivation and the quality of midwifery care [ 29 , 30 ].

The limited organizational power of midwives was another identified challenge of MLC. This finding was in step with other studies that emphasize that limited senior midwifery positions in the health system constrain the decision-making power and capability of midwives. This was compounded by limited opportunities for midwifery personnel to address their concerns to the responsible bodies. Hence, midwives need to take control of their own situations. When midwives are included in customizing their work environments, it has proven to result in improved quality of care for women and newborns around the globe [ 8 , 15 ].

Lack of work-related security was another barrier to MLC explored in this study, in which the midwives’ work environment was surrounded by insecurity, especially during night shifts, when midwives are facing verbal and even physical attack, as mentioned by participants. This finding is supported by many other studies conducted around the globe [ 22 , 23 , 25 , 31 ]. The above finding is also in agreement with another qualitative narration, which emphasizes that the midwives’ work environment was surrounded by insecurity, especially during night shifts due to a lack of available security personnel; they often felt frightened on their way to and from work [ 7 ]. In order for midwives to provide quality care, it is crucial to create supportive work environments by ensuring sufficient pre-conditions, primarily security issues [ 31 ].

Conclusions

The study findings contribute to a better understanding of the facilitators’ and barriers of a midwifery-led model of care in the case of Dire Dawa public health facilities. Professional pride and having good team spirit were the main facilitators of midwifery-led model care. Contrary to that, insufficient professional development, shortage of resources, feeling of demoralization, lack of motivation, limited organizational power of midwives, unfair risk payment, and lack of work-related security were the main barriers to a midwifery-led model of care in the case of Dire Dawa public health facilities. Generally, midwifery care is facing considerable challenges, both pertaining to the management of the healthcare service locally and nationally.

Study implications

The findings of the study have implications for midwifery care practices in Eastern Ethiopia. Addressing these areas could potentially contribute to the reduction of IMR and MMR.

Strengths and limitations

The first strength of the study is that the participants represented different healthcare facilities, both urban and rural, thereby offering deeper and more varied experiences and reflections. A second strength is using a midwife as a moderator. She or he understood the midwives’ situation, thereby making the participants feel more comfortable and willing to share their stories. However, focusing solely on the perspective of the midwives is a limitation.

Recommendations

To overcome the barriers of midwifery care, based on the result of this study and in accordance with the 2020 Triad Statement made by the International Council of Nurses, the International Confederation of Midwives, and the World Health Organization, it is suggested that policymakers, Ethiopian federal ministry of health, Dire dawa health office, and regulators in Dire Dawa city and settings with similar conditions coordinate actions in the following:

To the Ethiopian federal ministry of health (FMOH)

Should strengthen regular and continuous educational opportunities, trainings, and updates for midwives, prioritizing and enforcing policies to include adequate and reasonable remuneration and hazard payment for midwives. Support midwifery leadership at all levels of the health system to contribute to health policy development and decision-making.

To dire Dawa health Bureau

Ensure decent working conditions and an enabling environment for midwives. This includes reasonable working hours, occupational safety, safe staffing levels, and merit-based opportunities for career progression. Special efforts must be made to ensure safe, respectful, and enabling workplaces for midwives operating on the night shift. Midwifery leaders should be involved in management bodies within an appropriate legal framework. Made regular mentorships on the functionality of different diagnostic instruments in respective health facilities.

To Dire Dawa public health facility’s

Create an arena for dialogue and implement a more supportive leadership style at the respective health facilities. Should address professional-related concerns of midwives early. Ensure midwives’ representation at the management bodies. Ensure the selection criteria for educational opportunities and different trainings are fair and inclusive. Ensure the safety and security of midwives, especially those who work night shifts. Should assign adequate staff (midwives and security guards) to the night shifts.

Ethiopian midwifery association

Should influence different stakeholders to solve midwife’s concerns like hazards payment and educational opportunity.

Data availability

All the datasets for this study are available from the corresponding author upon request.

Abbreviations

Focused group discussion

In-depth interview

Infant mortality rate

Key informant interview

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Acknowledgements

We are very grateful to Dire Dawa University for the financial support for this study and to the College of Medicine and Health for its monitoring ship. All study participants for their willingness to respond to our questionnaire.

this work has been funded by Dire Dawa University for data collection purposes. The Dire Dawa University College of Medicine and Health Sciences was involved in the project through monitoring and evaluation of the work from the beginning to the result submission. However, this organization was not involved in the design, analysis, critical review of its intellectual content, or manuscript preparation, and its budget did not include publication.

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Contributions

MH developed the study proposal, served as the primary lead for study implementation and data analysis/interpretation, and was a major contributor in writing and revising all drafts of the paper. AM, DT, NA, LA, and SA supported study implementation and data analysis, and contributed to writing the initial draft of the paper. YD, TW, MG, TH and, NM supported study recruitment and contributed to writing the final draft of the paper. TG, YM, TD, MY, ND and, AA conceptualized, acquired funding, and led protocol development for the study, co-led study implementation and data analysis/interpretation, and was a major contributor in writing and revising all drafts of the paper. All authors contributed to its content. All authors read and approved the final manuscript.

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All methods were followed in accordance with relevant guidelines and regulations. The institutional review board of Dire Dawa University has also examined and evaluated it for its methodological approach and ethical concerns. Ethical clearance was obtained from Dire Dawa University Institutional Review Board and an official letter from research affairs directorate office of Dire Dawa University was submitted to Dire Dawa health office and it was distributed to selected health institutions. Voluntary informed written consent was obtained from the study participant’s right after the objectives of the study were explained to the study participants and confidentiality of the study participants was assured throughout the study period. Participants were informed that they have the right to terminate the discussion (interview) or they can’t answer any questions they didn’t want to answer.

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Hailu, M., Mohammed, A., Tadesse, D. et al. Facilitators and barriers of midwife-led model of care at public health institutions of dire Dawa city, Eastern Ethiopia, 2022: a qualitative study. BMC Health Serv Res 24 , 998 (2024). https://doi.org/10.1186/s12913-024-11417-x

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Academic self-discipline as a mediating variable in the relationship between social media addiction and academic achievement: mixed methodology

• Özge Erduran Tekin   ORCID: orcid.org/0000-0002-4052-1914 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1096 ( 2024 ) Cite this article

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This study examines the mediating role of academic self-discipline in the effect of university students’ social media addictions on academic achievement. The study sample consisted of 520 university students with a daily social media usage time of four hours or more, selected using the convenience sampling method. Data were collected from 36 cities in Turkey. Bergen Social Media Addiction Scale, Academic Self-discipline Scale, Personal Information Form, and Semi-structured Interview Form were used as data collection tools. The relationships between variables were analyzed using Pearson correlation analysis and regression analysis using Process Macro (model 4). In the regression analysis, mediation was tested with the Bootstrap technique. According to the analysis results, social media addiction predicts academic achievement. In addition, academic self-discipline has a partial mediating role in the relationship between social media addiction and academic achievement. As a result of the content analysis of the interviews, three themes were reached: “The Reasons for Social Media Addiction, The Effect of Social Media Addiction on Academic Achievement, and The Role of Academic Self-discipline in the Effect of Social Media Addiction on Academic Achievement.” The qualitative results obtained supported the quantitative results. Based on all these, suggestions were made based on increasing academic self-discipline to prevent social media addiction from affecting academic achievement.

Introduction

Excessive and misuse of social media is a crucial problem for the young age group, and social media addiction is quite common among university students (Chen and Peng, 2008 ; Ehrenberg et al. 2008 ; Kalaitzaki et al. 2020 ). When the reasons that push university students to use social media intensively are analyzed; the reasons such as communicating with others more easily, having fun, getting away from disturbing experiences and emotional situations and spending time are in the first place (Cohen et al. 2019 ; Özdemir, 2019 ; Zachos et al. 2018 ). Social media addiction has many negative consequences, including academic failure (Kaplan and Özdemir, 2023 ; Masood et al. 2020 ; Shi et al. 2020 ; Zhao, 2023 ). Problematic social media users are unable to control themselves, at this very point self-discipline may be important in preventing social media addiction (Li et al. 2010 ; Zahrai et al. 2022 ). According to research, self-discipline affects problematic internet use behaviors more than any other variable (Kim et al. 2007 ; Li et al. 2013 ). On the contrary, no study in the literature examines the mediating role of academic self-discipline, which represents the sustainability of self-discipline in academic subjects, in the relationship between social media addiction and academic achievement.

Relationship between social media addiction and academic achievement

Academic achievement is shaped by very comprehensive variables and is challenging to systematize in a specific model (Thomas and Maree, 2021 ; Whelan et al. 2020 ). However, the reflection of generally learned knowledge and skills in students’ examination performances is accepted as a critical indicator of academic achievement. Given that academic achievement significantly affects the student’s development in future processes, it is considered significant to examine what factors increase and decrease academic achievement (Shi and Qu, 2022 ).

It is important to examine the relationship between social media addiction, which is accepted as one of the problematic behaviors, and academic achievement (Zhao, 2023 ). Social media is an inclusive concept that describes the social networking sites and messaging applications that young people frequently use (Wartberg et al. 2020 ). Social media addiction, which is also accepted as an impulse control disorder, consists of problematic behaviors that manifest themselves as an individual’s irresistible desire for social media applications and loss of willpower, neglecting work, relationships, and daily routines due to intensive social media use (Andreassen and Pallesen, 2014 ; Young, 1996 ). Social media use has both positive and negative effects from an academic perspective. Social media can facilitate the exchange of information by connecting students and classrooms, provide a personalized learning environment, address different learning domains, and improve learning motivation (Arquero and Romero-Frı́as, 2013 ; Gazibara et al. 2020 ; Hrastinski, 2008 ; Kasperski and Blau, 2020 ). Therefore, the findings suggest that social media contributes to learning processes when used correctly (Jain et al. 2012 ). In addition, studies show that excessive and improper use of social media applications causes a decrease in academic achievement (Masood et al. 2020 ; Wu and Cheng, 2019 ). Social media negatively affects academic achievement because it distracts attention, causes procrastination, and reduces the time allocated to lessons by causing time loss (Junco, 2012 ; O’Keeffe et al. 2011 ). On the other hand, the self-control levels of individuals may be effective in preventing behaviors, such as spending a much longer time on social media applications than normal usage times and often not being aware of this situation and postponing daily tasks that need to be performed during this time (Diker and Taşdelen, 2017 ; Tutgun-Ünal and Deniz, 2015 ; Zahrai et al. 2022 ).

The mediating role of academic self-discipline

Behavioral theories try to explain the limitations of self-control, and the Uses and Gratifications Theory defines the lack of self-control as the inability to neglect smaller and momentarily pleasurable rewards to achieve larger goals (Katz et al. 1973 ). The conscious development and maintenance of self-control is defined as self-discipline (Bear, 2009 ). However, many disciplines use the concept of self-discipline by, it has also been expressed as self-regulation, self-control, self-motivation, and willpower. Duckworth and Seligman ( 2006 ) used the concepts of self-discipline and self-control interchangeably and defined self-discipline as a conscious and sustainable effort to suppress momentary desires to achieve planned goals. Therefore, it can be said that the concept of self-discipline, which refers to taking and maintaining responsibility, refers to the continuity in the ability of individuals to postpone their wants consciously and needs to achieve their long-term goals (Duckworth and Seligman, 2006 ). Academic self-discipline, which is the equivalent of self-discipline in the academic field, is defined as the ability to stay away from various stimuli by controlling oneself to achieve one’s academic goals, to focus one’s attention on the subject to be studied, and to maintain a certain working order in a planned manner (Pustika, 2020 ). By the definition, the scale used to measure academic self-discipline in this study has two sub-dimensions: Planned study and attention. Therefore, academic self-discipline was analyzed in this study, especially within the scope of these two dimensions.

Concentration is the ability to maintain one’s attention despite being bored, tired, or other negative emotional states by focusing sufficiently on the subject one is working on (Taylor et al. 2002 ). Considering that one of the dimensions of academic self-discipline is concentration, it is assumed that increased academic self-discipline may be effective in academic achievement. Planning is another dimension considered in terms of academic self-discipline (Cao and Cao, 2004 ). When considered within the scope of self-discipline theory, planning also includes perseverance and self-motivation and provides convenience in achieving individual goals and fulfilling tasks (Shi and Qu, 2022 ). Students can achieve their goals for better performance (Malte et al. 2009 ). In a study, it was observed that there was no significant difference between students with high and low academic achievement in terms of self-discipline. However, there were differences in various dimensions of time management, including planning (Fang and Wang, 2003 ). Unlike self-discipline, it can be meaningful to examine academic self-discipline in the axis of planning and attention separately and examine the relationship between academic self-discipline and academic achievement in depth through both quantitative and qualitative data to make more concrete suggestions to increase academic achievement.

The present study

Self-discipline, which consists of three dimensions: concentration, impulse control, and delayed gratification, allows one to consider the possible advantages and disadvantages of the action before taking the action. Thus, it helps the person to stay away from risky behaviors and postpone immediate pleasures by being patient for long-term gains (Taylor et al. 2002 ). Therefore, self-discipline is recognized as one of the main factors affecting students’ academic achievement (Liang et al. 2020 ; Van Endert, 2021 ). Students with insufficient self-discipline face academic failure (Tominey and McClelland, 2011 ). Although studies have shown a relationship between self-discipline and academic achievement (Duckworth et al. 2019 ), it is still unclear how self-control and self-discipline affect academic achievement (Shi and Qu, 2022 ). In addition, it requires self-discipline to focus on academic tasks by moving away from harmful internet or social media use that causes time loss (Mbaluka, 2017 ). Self-discipline is important in controlling behavioral addiction types, such as social media addiction. In addition, considering that self-discipline significantly affects academic achievement (Duckworth and Carlson, 2013 ; Zhao and Kuo, 2015 ), it suggests that high academic self-discipline may have a protective role in the negative effects of impulse behavior disorders, such as social media addiction, on academic achievement. Although there are studies examining the relationship between self-control, self-discipline, and social media addiction (Koç et al. 2023 ; Sağar, 2021 ; Zahrai et al. 2022 ), there is no study investigating the mediating role of academic self-discipline in the relationship between social media addiction and academic achievement with a mixed method. Based on all these, this study aimed to examine the mediating role of academic self-discipline in the relationship between university students’ social media addictions and their academic achievement. The quantitative and qualitative sub-objectives created within the scope of this purpose are as follows.

Quantitative sub-objectives:

-Is there a significant relationship between social media addiction, academic achievement, and academic self-discipline in university students?

Is there a mediating role of academic self-discipline in the effect of social media addiction on academic achievement in university students?

Qualitative sub-objectives:

What are the common themes related to university students’ experiences of social media addiction, academic achievement, and academic self-discipline?

How do academic self-discipline experiences mediate the relationship between university students’ social media addictions and academic achievement?

This study was designed as an exploratory sequential mixed design. With mixed methods, a weak or missing aspect of one of the quantitative and qualitative methods can be covered by the strengths of the other (Creswell and Plano Clark, 2018 ). The calculated regression coefficients guide the examination of the relationship between social media addiction and academic achievement and the mediating role of academic self-discipline. However, to better understand the effect of social media addiction on academic achievement and make academic self-discipline-based intervention suggestions, a mixed-method approach was used in this study, considering that it is critical to collect data from students through their opinions.

Participants

The sample of this study consisted of 520 university senior students ranging from 22 to 27 ( \(\bar{x}\)  = 24.43; Sd  = 1.49) studying in various departments (Psychology, Law, Engineering, and Teaching) at state universities in Turkey in the 2023–2024 academic year. Data were collected from 36 cities in Turkey. Considering the ease of accessing the data, the convenience sampling method was preferred. In the convenience sampling method, due to the limitations in terms of the labor force and time, the sample is selected from easily accessible and applicable units (Büyüköztürk et al. 2014 ). Firstly, data were collected from 873 students through Google Forms. Then, the analyses continued with the data obtained from 520 people who stated that their daily social media usage time was four hours or more. It was aimed to provide more reliable information about the effect of social media addiction on the academic achievement of the 520 students, 299 (57.5%) were female, and 221 (42.5%) were male. Of the participants, 307 (57.9%) defined their socioeconomic level as low, 135 (26%) defined their socioeconomic level as medium, and 84 (16.2%) defined their socioeconomic level as high. The students wrote the transcript grade median, which consisted of the calculation of the end-of-term grades of the courses they had taken until the last year. To obtain more comprehensive information about their academic achievements throughout their university life, the sample consisted of final-year university students. In this context, 182 (35%) of the students who participated in the study had general weighted grade point averages between 1.00 and 2.00, 179 (34.4%) between 2.00 and 3.00, and 159 (30.6%) between 3.00 and 4.00.

In the qualitative phase of this study, 20 volunteer students were selected by purposive sampling method from the students who participated in the quantitative data collection phase. To ensure maximum diversity, male and female students studying in different departments were selected and interviews were conducted with students with high levels of social media addiction to obtain in-depth information. In this context, nine of the students aged between 22 and 25 were male and 11 were female. Three of them were law students, four were teachers, four were psychology students and nine were final-year students studying in various engineering departments.

Data collection tools

Bergen Social Media Addiction Scale developed by Andreassen et al. ( 2016 ) was adapted to Turkish culture by Demirci ( 2019 ). The scale was constructed to fulfill the basic addiction criteria of mental preoccupation, mood change, tolerance, withdrawal, conflict, and failed quit attempts. The scale, graded on a five-point Likert scale, consists of six items. The scores that can be obtained from the scale vary between 6 and 30. In the adaptation study, the Cronbach’s alpha internal consistency coefficient of the scale was calculated as 0.83 and the test-retest reliability coefficient was calculated as 0.83. The suitability of the scale for use in this study was examined by reliability analysis, and exploratory factor analysis was applied. In this context, the KMO value was found to be 0.89 and the Barlett Sphericity test p  < 0.000, and it was accepted that the data set was suitable for factor analysis. The unidimensional structure of the scale (explaining 64% of the total variance) was also confirmed in this data set. The fact that the factor loadings of the scale ranged from 0.77 to 0.85 indicates that the items of the scale are compatible with the structure in which it is located. In addition, the Cronbach’s alpha reliability coefficient of the scale was calculated as 0.89 in this study. Based on all these, it can be said that it is appropriate to use the scale within the scope of this study. Some of the items that make up the Bergen Social Media Addiction Scale are as follows:

Have you felt the urge to use social media more and more?

Has using social media too much negatively affected your work/studies?

Would you be uncomfortable or distressed if you were banned from using social media?

The Academic Self-discipline Scale was developed by Şal ( 2022 ) to measure the academic self-discipline levels of university students and adapted to Turkish culture by Erduran Tekin and Şal ( 2023 ). The scale, which consists of eighteen items, has two sub-dimensions “planned work” and “attention.” The scale is scored on a five-point Likert scale and items 6, 7, and 16 are evaluated as reverse. While the scores obtained from the scale vary between 18 and 90, an increase in the scores means an increase in academic self-discipline. In the adaptation study, the structure consisting of two sub-dimensions was confirmed, and the goodness of fit indices of the model indicated an acceptable fit. In the adaptation study, Cronbach’s alpha coefficient of the Academic Self-discipline Scale was calculated as 0.86. The suitability of the scale for use in this study was examined by reliability analysis, and exploratory factor analysis was applied. In this context, the KMO value was found to be 0.87 and the Barlett Sphericity test p  < 0.000, and the data set was accepted as suitable for factor analysis. The two-dimensional structure of the scale (explaining 40% of the total variance) was also confirmed in this data set. The factor loadings of the scale ranged from 0.39 to 0.74, indicating that the items belonging to the scale are compatible with the structure in which it is located. In addition, the Cronbach’s alpha reliability coefficient of the scale was calculated as 0.85 in this study. Based on all these, it can be said that it is appropriate to use the scale within the scope of this study. Some of the items that make up the Academic Self-discipline Scale are as follows:

I have my schedule where I plan my study time.

I organize my study environment so that there are no distractions.

If I have planned to study, I can refuse to hang out with my best friend.”

The participants were asked to fill out the Personal Information Form. The form includes sections for age, gender, city of residence, and graduation grades. The academic achievements of the participants were examined based on the General Weighted Grade Point Average (GPA) written on the last semester’s transcript from the undergraduate program they studied. Those with GPAs between 1.00 and 2.00 were considered low academic achievers (2.00 was considered low academic achievement), and those with GPAs between 2.01 and 3.00 were considered medium academic achievers (3.00 was considered medium academic achievement). Those with GPAs between 3.01 and 4.00 were considered high academic achievers.

In the qualitative dimension of the study, semi-structured interview forms created by the researcher in this study were used. These forms were prepared based on the findings obtained by analyzing the quantitative data and researching the literature. After the forms were examined by two expert lecturers, a pilot application was carried out with a student on the comprehensibility of the questions, and the final version was given after the necessary corrections. While one of the most basic ways to ensure validity in qualitative research is the accuracy and detailed reporting of the categories and interpretations obtained, reliability is to minimize the difference in the interpretation of data by different experts (Büyüköztürk et al. 2014 ; Yıldırım and Şimşek, 2019 ). Thus, two academic members who were experts in their fields were consulted during the data interpretation process. The questions in the interview form are as follows:

What are the things that cause you to spend a lot of time on social media tools during the day?

How do you think spending too much time on social media tools affects your academic achievement?

Do you have academic self-discipline? How do you evaluate yourself?

Do you work in a planned way? How do you think working in a planned way affects academic achievement?

Do you ever get distracted while studying because of social media tools? If yes, what measures do you take to prevent social media tools from distracting you while studying?

What do you think you need to improve your academic achievement?

What should happen so that social media does not affect your academic achievement?

Data collection

Considering the ease of access to the data in the present study, the data were collected online using Google Forms. All ethical rules required by scientific research were followed in data collection. Ethics committee approval dated 23.01.2024 and numbered E-35592990-050.01.04-3222142 was obtained from the National Defense University Scientific Research and Publication Ethics Board for the study.

To examine the suitability of the data for parametric tests, kurtosis, skewness, and Z score analyses were performed (Schumacker and Tomek, 2013 ). Multivariate outliers in the data set were analyzed by calculating the Mahalanobis distance. Skewness and kurtosis values were re-analyzed and presented in Table 1 , and it was assumed that the data were normally distributed. In addition to descriptive analyses in which values, such as arithmetic mean and standard deviation, were calculated, ANOVA and t -test analyses were used to determine differences, and correlation analysis was used to determine the relationships between variables. The presence of multicollinearity, one of the prerequisites for regression analysis, was examined through variance inflation factors (VIF) and tolerance values (TV) of the variables in the model. If VIF ≥ 10 and TV ≤ 0.10, the multicollinearity problem is mentioned and the VIF values of the variables in this model are 2.23, and TV values are 0.45, and there is no multicollinearity problem. In this study, the mediating role of academic self-discipline in the relationship between social media addiction and academic achievement was analyzed with the regression of the Bootstrapping Technique (Hayes, 2017 ). Analyses were conducted with SPSS 26 PROCESS programs.

The “content analysis” method was preferred when analyzing qualitative data. With content analysis, the data obtained are conceptualized and organized in a way to be understood, and themes are created (Yıldırım and Şimşek, 2019 ). Content analysis aims to collect similar data collected through interviews under certain themes and to put these themes into a regular format. Firstly, the data obtained from 20 interviews were transferred to the computer environment in writing. A total of 62 pages of data were obtained from the interviews. Twenty interview data were recorded with student codes (e.g., P1, P2, P3, P4, and P5). Then, these organized data were regularised with MAXQDA 2020 software. For the analysis, the data were read repeatedly and analyzed within the scope of the research questions. The themes obtained were coded by two more academicians except the researcher. The coding reliability percentage was calculated using the formula “Agreement/(Agreement + Disagreement)” suggested by Miles and Huberman ( 1994 ). Coding reliability above 80% (Miles and Huberman, 1994 ), the lowest recommended percentage of agreement, was achieved, and the reliability was calculated as 90%. In cases where consensus could not be reached on the codes, participant confirmation was used to build consensus and increase reliability. The data obtained were given systematically according to the research questions in the findings section and supported with direct quotations to increase credibility.

Pearson analysis

Within the scope of the first sub-objective of this study, the skewness and kurtosis values of the social media addiction, academic achievement, and academic self-discipline scale scores and the correlation analysis results examining the relationships between the variables are presented in Table 1 .

Considering the data obtained from this study, it can be said that the skewness and kurtosis values are in the range of +−2 and the data have a normal distribution (George and Mallery, 2019 ). As a result of the analyses, it was seen that the social media addiction scores ( \(\overline{{\boldsymbol{x}}}\)  = 15.67) of the data constituting the study group were above the average. Considering the relationship between the variables of this study, as seen in Table 1 , there was a negative and highly significant relationship between social media addiction and academic achievement ( r  = −0.63, p  < 0.01) and a negative and highly significant relationship between social media addiction and academic self-discipline ( r  = −0.74, p  < 0.01). When the relationship between academic achievement and academic self-discipline was analyzed, it was seen that there was a positive and highly significant relationship ( r  = 0.60, p  < 0.01). The findings suggest that as social media addiction increases, academic achievement and academic self-discipline decrease, and as academic self-discipline increases, academic achievement increases.

Results of mediation analysis

Within the scope of the second sub-objective of this study, the mediating role of academic self-discipline in the effect of social media addiction on academic achievement was analyzed using the Regression-based Bootstrapping Technique. In the analyses, 1000 bootstrap sampling was used and the estimates were evaluated at 95% confidence intervals reflecting the results adjusted for bias error. The model used for the mediation role was designed according to Model 4 proposed by Hayes ( 2017 ) in the presence of one independent, one dependent, and one mediator variable. The model is shown in Fig. 1 , and the analysis results of the mediation of academic self-discipline in the effect of social media addiction on academic achievement are presented in Table 2 :

Social media addiction predicts academic achievement through academic self-discipline [ R 2  = 0.44; F (2-519)  = 203.530; p  < 0.001].

As shown in Fig. 1 , social media addiction directly predicted academic self-discipline negatively ( a  = −0.74; p  < 0.001). Likewise, it was seen that academic self-discipline directly predicted academic achievement positively ( b  = 0.29; p  < 0.001). In addition, social media addiction directly predicted academic achievement negatively ( c  = −0.63; p  < 0.001). When academic self-discipline, the mediating variable, was included in the model, it was observed that this effect was c 1  = −0.42 and the value was still significant.

There was a partial mediation effect since the coefficient resulting from the inclusion of mediator variables in the model was still a significant coefficient and 95% confidence intervals (CI) for the significance of the indirect effects of partial mediation determined in the model are given in Table 2 .

It was understood that the partial mediation model was significant [ F (2-519)  = 203.530, p  < 0.001]. Social media addiction and academic self-discipline explained 44% of the variance in academic achievement (Table 2 ). In the mediation analysis using the bootstrapping technique, for the hypothesis of this study to be confirmed, the 95% confidence interval (CI) values obtained from the analysis should not contain a zero (0) value to support the research hypothesis (Gürbüz, 2019 ). The mediating role of academic self-discipline in the effect of social media addiction on academic achievement was significant (Bootstrap Coefficient = −0.22, 95% CI [−0.31; −0.14]), and these confidence intervals did not include any zero (0) point.

Qualitative analysis results

Table 3 lists the codes formed as a result of question-based analyses of the data collected from semi-structured interviews with students.

The codes obtained as a result of the analysis of semi-structured interviews conducted to examine the mediating role of academic self-discipline in the effect of university students’ social media addictions on their academic achievement are brought together in three themes and presented in Table 4 .

The quotations related to the most frequently repeated codes explaining the “Reasons for social media addiction” which constitutes Theme 1, are given below:

Desire to receive news “ When I don’t use social media tools, such as Twitter and Instagram, I miss the current news. There is a new event every minute in our country and I see many things on those platforms that I cannot see on television. Therefore, I find myself constantly looking at Twitter and Instagram (P4).”

Entertainment and relaxation “ Life is already hard and boring. At the moment, if you want to do something outside, everything is expensive. Even going to a cinema is incredibly costly for a student. In this case, social media applications are the place where there is content that relaxes, entertains, and makes you laugh at a cheap price (P17).”

Communicating with others “ Social media tools are as much a part of our body as our hands and feet. Instagram is the main way I communicate with other people, my friends, my date. For example, I always check Instagram to see what stories he has posted . (P9).”

The quotations related to the most frequently repeated codes explaining “The effect of social media addiction on academic achievement”, which constitutes Theme 2, are given below:

Loss of time “ I sit at my desk to study, and then a notification comes. I pick up my phone, thinking I’ll check it for a minute, but that’s not the case. I find myself constantly scrolling through posts and watching meaningless videos. This creates an incredible waste of time. I realize that hours have passed, but I haven’t studied at all because I was looking at Instagram or Twitter (P20).”

Inability to pay attention to the lessons, “ My mind stays on social media tools and I can’t concentrate on my lessons. I’m always looking at who went where and what story they shared. Then I started to envy people’s virtual happiness even though I knew it was not real. I can’t concentrate on my lessons thinking about all these things (P11).”

Inability to work regularly “ I am preparing a full study plan, I start, and I say I will take a 10   min break. In the meantime, I look at social media applications, 10   min has become 40   min. I say it’s okay and start over, but it’s the same again. Even if I work regularly one day, I cannot work regularly the next day. This cycle repeats itself (P1).”

The quotations related to the most frequently repeated codes explaining Theme 3, “The role of academic self-discipline in the effect of social media addiction on academic achievement” are given below:

To be disciplined “ As long as I am disciplined and can control myself, I can stay away from social media tools. Of course, one of the biggest benefits of this is my academic achievement . As I continue to control myself, use social media tools less, and stay away from them while studying, my attention and interest in the lesson increases. I can be more successful (P17).”

To focus on lessons “ Social media tools are a very serious stimulant. At least while studying, it is very useful to take the phone out of the room, and if the computer is open, it is very useful to keep only the file being studied open without opening different tabs. As usage is limited, study time remains. In this case, it allows us to focus more on the lessons and not pay attention to unnecessary stimuli (P13).”

Adhering to the work plan “ When I work more regularly, when I do not pay attention to other things even for a short time during the study and, I can follow my study program prepared for me, this makes me more successful (P6).”

This study aimed to examine the mediating role of academic self-discipline in the relationship between social media addiction and the academic achievement of university students in Turkey. In this context, the first sub-objective of the present research is to examine the relationship between social media addiction, academic achievement, and academic self-discipline. There is a significant negative relationship between social media addiction and academic achievement. When the relationship between academic self-discipline and academic achievement is analyzed, there is a significant positive relationship. Accordingly, as social media addiction increases, academic achievement and academic self-discipline decrease, and as academic self-discipline increases, academic achievement increases. The results obtained from this study are consistent with studies showing that social media addiction negatively affects academic achievement (Kaplan and Özdemir, 2023 ; Masood et al. 2020 ; Shi et al. 2020 ; Zhao, 2023 ).

The second sub-objective of this study is to examine the mediating role of academic self-discipline in the relationship between university students’ social media addictions and academic achievement. It was found that social media addiction and academic self-discipline explained 44% of the variance in academic achievement. The mediating role of academic self-discipline in the effect of social media addiction on academic achievement was significant. To determine the appropriate preventive and intervention mental health services that can be offered to university students, it is important to examine the variables that may mediate the relationship between social media addiction and academic achievement when the studies conducted in the literature are examined, seen that self-control and self-discipline are related to student’s academic achievement (Duckworth and Carlson, 2013 ; Liang et al. 2020 ; Mbaluka, 2017 ; van Endert, 2021 ; Zhao and Kuo, 2015 ). The themes obtained within the scope of the third and fourth sub-objectives of the research provide information about the causes of social media addiction, how social media addiction affects academic achievement, and what is the mediating role of academic self-discipline in this effect. When the literature is examined, similar to the results obtained from this study, social media addiction negatively affects academic achievement (Zhao, 2023 ), self-discipline significantly predicts academic achievement positively (Lin, 2021 ), and there are negative relationships between self-control and problematic social media use (Wu et al. 2013 ). Ning and Inan ( 2023 ) examined the mediating role of self-control in the effect of social media addiction on academic success. They concluded that self-control has a mediating role in the relationship between social media addiction and academic success, similar to the results obtained from this study. In another study conducted by Putri et al. ( 2022 ) with university students, it was concluded that high self-discipline reduces social media use. According to the same study, although there is no direct relationship between social media use and academic success, academic success is associated with higher self-control (Putri et al. 2022 ). According to Lindner et al. ( 2017 ), students with high self-control show higher attention skills, are more motivated to fulfill their responsibilities, and have higher academic achievement. In a study examining the mediating role of self-discipline in the relationship between cognitive ability and academic success, it was concluded that the mediating role of self-discipline is significant, and planning is a moderating variable in this relationship (Shi and Qu, 2022 ).

According to the qualitative analysis results, university students exhibit social media addiction behaviors for various reasons such as entertainment, relaxation, receiving news, communicating with others, and habit. These results are consistent with other studies in the literature (Cohen et al. 2019 ; Özdemir, 2019 ; Zachos et al. 2018 ). As a result of the interviews, social media use causes a waste of time, prevents students from devoting themselves to lessons and regular study, and increases their reluctance to study. They have difficulty controlling themselves in terms of social media use, which can be considered the negative effects of social media. According to the students, for social media addiction not to affect academic achievement, they should work in a planned manner, use time effectively, remove distracting stimuli from the environment while studying, pay attention to the lessons, continue their study activities in a disciplined manner, improve their self-control, and follow their study plans continuously. These results support other studies emphasizing the impact of attention, planned study, and time management on academic success (Cao and Cao, 2004 ; Shi and Qu, 2022 ; Taylor et al. 2002 ). Based on all these, although the results obtained from this study are consistent with the literature, more studies are needed examining the relationship between social media addiction and academic achievement. On the other hand, no mixed methodology with study has been found that addresses the mediating role of academic self-discipline in the relationship between social media addiction and academic success, and it is assumed that this study will make a significant contribution to the relevant literature. Additionally, this study encourages further examination of the protective role of the academic self-discipline variable in reducing social media addiction and increasing academic achievement.

Conclusion and suggestions

According to the results of this study, while social media addiction reduces academic achievement, academic self-discipline can prevent social media addiction from reducing academic achievement. Thus, it is recommended that academic self-discipline-based psychoeducation practices be implemented to reduce social media addiction among university students and increase their academic achievement. The obtained qualitative results allow us to understand the mediating role of academic self-discipline in this relationship in more detail. Given the themes, it is recommended that preventive guidance activities that can improve academic self-discipline, especially planned work, and attention, should be implemented more by both educators and school psychologists working in universities. Helping students create a structured study plan, providing regular guidance to help them implement this plan, teaching attention exercises and various memory exercises, and giving behavioral assignments to develop self-discipline are examples of these activities. When the themes explaining the reasons that lead students to use the internet are examined, the creation of other entertainment and recreation areas that can replace social media can contribute to distancing university students from social media. On the other hand, more quantitative and qualitative studies are needed in the literature to examine the relationship between social media addiction and academic achievement in terms of different variables.

Limitations

This study is limited to the quantitative and qualitative data obtained from students who continue to study as seniors in the 2023–2024 academic year at public universities in Turkey. Although there is no mixed methodology with studies examining the relationship between social media addiction and academic achievement variables with academic self-discipline, a very limited number of studies have examined the relationship with self-discipline. This study was supported by studies on self-control and self-regulation, which are used by some researchers instead of the concept of self-discipline. To prevent conceptual confusion and reveal the mediating role of academic self-discipline, such a path was followed, which some researchers may consider a limitation.

Data availability

The data generated and/or analyzed during the present study are available from the corresponding author upon reasonable request. The data cannot be directly placed in a public repository due to limited permissions obtained from the participants. However, the corresponding author is willing to share anonymized data with interested researchers upon request.

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Özge Erduran Tekin

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Özge Erduran Tekin collected data, undertook formal analysis, wrote and revised the manuscript.

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Ethics committee approval dated 23.01.2024 and numbered E-35592990-050.01.04-3222142 was obtained from this study’s National Defense University Scientific Research and Publication Ethics Committee. All procedures performed in studies involving human participants were by the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Erduran Tekin, Ö. Academic self-discipline as a mediating variable in the relationship between social media addiction and academic achievement: mixed methodology. Humanit Soc Sci Commun 11 , 1096 (2024). https://doi.org/10.1057/s41599-024-03633-x

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Received : 18 March 2024

Accepted : 20 August 2024

Published : 28 August 2024

DOI : https://doi.org/10.1057/s41599-024-03633-x

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