qualitative research data analysis software

The Best 10 Qualitative Data Analysis Software Platforms in 2023

Published: June 08, 2023

Qualitative data analysis software has become essential in helping businesses know their customers. In fact, 63% of customers expect you to know their wants and expectations.

Qualitative data analysis software (QDAS) can help you review trends and consumer behavior. You'll then know what your target audience wants and give them great experiences.

In this post, we'll break down some of the best qualitative data analysis software you can use with your team. But first, let's define qualitative data analysis software.

What is qualitative analysis software?

Qualitative data analysis software (QDAS) gathers information beyond pure numbers to help you make better decisions.

These tools scrape your digital presence, chat messages, reviews, and files for customer insights. They then review the details in bulk, saving your team valuable time during reporting.

Additionally, QDAS minimizes data loss, as data is either stored in the cloud or on your computer. These tools account for errors and bias during analysis — common challenges when working with data manually.

With QDAS, your business can test and refine theories to predict future events or customer actions. With great data accuracy, you can reduce risks and achieve greater results.

Survey Tools vs. Qualitative Data Analysis Software

Traditional survey tools just provide you with reporting for quantitative data, such as age and number of visits per month. This narrow view of data limits your analysis to questions with quantifiable answers.

Alternatively, qualitative data analysis software (QDAS) gathers insights beyond numbers. That includes insights from interviews, focus groups, and online reviews. You can then get a broader view of customer concerns.

The 10 Best Qualitative Data Analysis Software

• Raven's Eye
• Square Feedback
• QDA Miner Lite

HubSpot offers a customer feedback tool that generates detailed analytics from surveys and customer reviews. The tool can analyze these responses and provides a detailed breakdown of customer satisfaction.

You can access data from one dashboard. This lets you view different charts and graphs summarizing your customers' responses. With a simple setup, your team has a quick, clean way to review customer insights.

HubSpot's customer feedback tool is part of HubSpot's Service Hub tools. It can collect both quantitative and qualitative customer feedback.

What We Love

• The tool connects with HubSpot's NPS surveys, so you can seamlessly collect and analyze customer data.
• There's an easy-to-use interface.
• The interface gathers customer service KPIs so you can discover growth opportunities.

MAXQDA is a qualitative data analysis software designed for companies analyzing a range of customer data.

The software allows you to import data from interviews, focus groups, surveys, videos, and social media. This way, all your qualitative data can be reviewed in one central location.

Once imported into MAXQDA, you can organize your information into different categories. You can mark specific data with tags and leave notes for other employees to review your work.

MAXQDA even lets you color code data so your team knows exactly what to work on each day.

• You can use MAXQDA with multiple data formats, including social media posts, videos, and images.
• MAXQDA supports both mixed method analysis and statistical analysis.
• You can store project data in one project pack to make collaboration easy.

Quirkos includes a variety of tools that analyze and review qualitative data. That includes comparative analysis, which shows side-by-side views of your data.

Your team can more easily spot trends and identify roadblocks in the customer experience.

With Quirkos, you'll also have unparalleled customization options. Quirkos, unlike other QDAS, has 16 million colors that you can use in your theme, making coding quicker.

This is useful if you have a large coding framework, as you can label your themes with different colors for easy identification and reading.

• Quirkos is compatible with many operating systems, including Linux and Mac.
• There's a drag-and-drop feature for coding sections of text.
• You can connect to SPSS, Word, or Excel to generate custom reports.

4. Qualtrics

Qualtrics comes with two key tools for simplifying your qualitative research process: TextIQ and DriverIQ.

TextIQ uses AI to analyze open-structured data. You can then assess customer sentiment and draw helpful insights from the data.

The DriverIQ tool helps you see what matters most for your customers, from purchase intent to satisfaction. You can then focus on what will have the most impact on your business.

• The TextIQ tools help uncover trends, problems, and opportunities from customer survey responses. Additionally, it acts as a social listening tool, so you can use it to identify brand mentions on social media.
• The advanced drag-and-drop feature allows you to visualize data quickly.
• Sophisticated intelligence tools (AI+ML) make advanced research for different metrics easier.

5. Raven's Eye

Raven's Eye is a qualitative data analysis software that can process and analyze natural language data. One of its most popular features is its audio converter, which uploads audio files into the software and transforms them into text files.

Then, it analyzes the text for unique insights into customer behavior. Raven's Eye is perfect for audio interviews with customers. You can upload the recorded session to Raven's Eye for analysis.

In addition to audio, Raven's Eye processes text documents. The text analyzer can review text samples written in over 65 different languages.

It then uses a "natural language" analysis to determine a variety of unique metrics, ranging from word count to reading ease.

• Raven's Eye quickly, accurately, and reliably convert audio and text files. This will help you understand how customers think and communicate.
• The program is cloud-based, meaning it can be accessed from multiple devices.
• You can explore text and spoken word (or natural language data) in the same way human beings can.

6. Square Feedback

Square Feedback is a free customer feedback collection tool that provides qualitative data reporting. It can analyze survey responses to see how satisfied your customers are.

Square Feedback also comes with historical filter options. With this feature, you can compare past data to current customer information.

• Square Feedback integrates into your digital receipts and easily collects feedback.
• You can privately track customer comments and responses.
• Square Feedback provides rich customer insights that you can use to make informed decisions.

LiGRE can be used by students, business professionals, and researchers to analyze interviews and large bodies of text. It has both free and premium plans.

LiGRE's most important analysis tools include the following:

• Automatic transcription (for transcribing both audio and video files).
• A survey builder.
• Data merging.
• A laboratory (a teaching platform where you can open your own qualitative research laboratory for a team).

• LiGRE Laboratory lets you teach your team about qualitative research.
• You can work with various media types, including books and articles.
• You can transcribe video and audio in more than 90 languages.

8. QDA Miner Lite

QDA Miner can analyze interviews, open-ended questions, and transcripts. Best of all, the user interface is easy and free.

QDA Miner can quickly analyze interviews, open-ended responses, journals, and still images. Plus, there are seven text search and retrieval tools, helping you reliably code your text in less time.

QDA Miner Lite also offers a Boolean text search tool. This gives you the unprecedented ability to retrieve and code text segments.

• QDA Miner Lite has an easy-to-use interface for coding, retrieving, and reviewing data. You can also present results in a variety of file formats.

Dedoose has qualitative and mixed method capabilities for anyone looking to analyze text, audio, images, videos, and surveys.

It claims to support traditional qualitative data management, coding, and analysis. Dedoose is a wise choice for analyzing raw consumer or market research data.

Dedoose is free for 30 days. Afterward, you can upgrade to a premium plan with rates adjusted depending on your needs or your group size.

• Dedoose encourages teamwork and collaboration.
• Dedoose presents information in interactive visuals, such as charts, plots, and tables for easy analysis.

10. Glimpse

Glimpse is ideal for customer success teams that want to analyze qualitative data related to consumer behaviors.

The easy-to-use tool comes with a qualitative sentiment analysis survey. This collects data from different sources and presents it in a straightforward format, whether chart, graphs, or heat maps.

• Information is organized or displayed concisely for easy analysis.
• You can collect information from various platforms and display them simultaneously.
• It identifies trends and patterns in products, industries, and companies.
• The software uses machine learning and what-if analysis tools to collect and present real-time data.

Choosing the Right QDAS

Ever since qualitative data analysis software was invented in the 80s, these tools have helped businesses harness data. For those in customer success, QDAS allows businesses to create memorable experiences for customers.

If you're looking for a QDAS to help you reach the right buyer, consider which tool aligns most with your business needs.

Editor's note: This post was originally published in June 2019 and has been updated for comprehensiveness.

Don't forget to share this post!

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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

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Guide index

Qualitative Data Analysis Software

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Showing 1 - 20 of 94 products

Strategy & Research

Qualtrics CoreXM is a cloud-based platform designed to help organizations generate sales insights and optimize product prices through analysis of customer experiences in real-time. Key features include feedback collection, concept... Read more about Strategy & Research

4.8 ( 411 reviews )

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Lucky Orange

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4.7 ( 196 reviews )

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LiveSession

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Sisense goes beyond traditional business intelligence by providing organizations with the ability to infuse analytics everywhere, embedded in both customer and employee applications and workflows. Sisense customers are breaking th... Read more about Sisense

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4.5 ( 2273 reviews )

VWO Testing

VWO Testing helps businesses design, deploy and manage custom tests for websites and mobile/web applications. The platform enables organizations to create A/B, split and multivariate tests to run surveys and analyze customer exper... Read more about VWO Testing

4.5 ( 90 reviews )

Customer-centric businesses want to know what issues are hurting their customer journey. But it’s hard to come up with a data-driven plan to address them. Keatext gives you a head start by analyzing feedback and generating AI-bas... Read more about Keatext

3.8 ( 19 reviews )

Thematic is a text analytics solution that helps enterprises collect and analyze customer feedback from various sources using artificial intelligence (AI) technology. Professionals can utilize the sentiment analysis tool to identi... Read more about Thematic

4.9 ( 15 reviews )

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Luminoso is a text analytics solution that helps enterprises upload, evaluate and view customer insights data using Natural Language Processing (NLP) and Artificial Intelligence (AI) technology. Its multilingual capabilities enabl... Read more about Luminoso

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Wolfram Mathematica

Wolfram Mathematica is a technical computing solution that provides businesses of all sizes with tools for image processing, data visualization and theoretic experiments. The notebook interface enables users to organize documents ... Read more about Wolfram Mathematica

4.6 ( 169 reviews )

Remesh is an innovative platform for engaging and understanding large groups of people online and in real-time, allowing you to have a conversation with up to 1,000 participants at once. The Remesh platform uses artificial intelli... Read more about Remesh

4.5 ( 11 reviews )

Applied Analytics

Applied Analytics is a cloud-based data analytics solution for businesses in the insurance industry. It offers configurable reporting tools, allows users to produce charts and graphs and lets users monitor client retention, sales ... Read more about Applied Analytics

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5.0 ( 1 reviews )

JMP is an on-premise data analytics solution that helps scientists, engineers and data explorers understand complex data relationships and visualize them via interactive dashboards. The data acquisition and cleanup functionalities... Read more about JMP

4.6 ( 50 reviews )

As a market or sales representative, you might be monitoring qualitative data to determine customer behavior and understand market trends. Qualitative data refers to conceptual findings used to recognize customer experience and patterns. If you have a variety of qualitative data touch points such as feedback, surveys, reviews, and interviews, the collected data can be convoluted.

Extracting essential information from unorganized data sets is a major challenge for data analysts and market researchers. It often results in inefficient insights and market predictions. Qualitative data analysis software can help you break down data silos, understand patterns, and get actionable insights on customer challenges. It allows businesses to simplify and automate various manual data processes including data collection, cleaning, and analysis.

In this buyers guide, we will learn more about qualitative data analysis software as well as its features and benefits.

Here is what we'll cover:

What is qualitative data analysis software?

Common features of qualitative data analysis

What type of buyer are you?

Benefits of qualitative data analysis

Key purchase considerations for qualitative data analysis

Qualitative data analysis software is used by data scientists and business researchers to analyze unstructured and text-based data obtained from first-hand observations, surveys, feedback, interviews, among other sources. The software helps in discovering and understanding patterns, such as customer sentiment, presented in a variety of formats including texts, audio files, videos, and images.

Qualitative data analysis software includes features such as data visualization, data coding, text analysis, content analysis, and reporting and statistics. These tools assist data scientists and researchers to analyze, audit, and manage huge amounts of qualitative data. Furthermore, the software helps in saving time by eliminating the need for manual data handling.

An example of qualitative data analysis software dashboard in Tableau ( Source )

Common features of qualitative data analysis software

The right type of qualitative data analysis software depends on your business requirements, which are often tied to business size:

Large businesses (over 500 employees): These buyers include large organizations and enterprises with a huge database of customers. Large businesses have separate teams of market and sales representatives, working together to obtain elaborate and crucial market research. These buyers need both quantitative and qualitative data analysis tools to generate accurate analytical reports. They also need to store data and share stats with other teams. These buyers should consider solution tools with mixed methods research, data visualization , and collaboration tools.

Some free PDF editors or reasonably priced basic versions with features including optical character recognition, electronic signatures, page rearrangement, and text and image editing can work for these buyers.

Small and midsize businesses (SMBs; up to 500 employees): SMBs usually have a small team of market and sales representatives, and a limited budget. These buyers are seeking basic data analysis and market prediction tools to organize data and obtain crucial insights into customer behavior. SMBs should consider qualitative data analysis software that reduces manual errors, lowers management costs, and assists in making quick fact-based decisions. They should go for a solution with consumer sentiment analysis, collaboration tools, and text analysis .

Benefits of qualitative data analysis software

Improves customer behavior analysis: Qualitative data analysis software helps businesses understand consumer sentiment and behavior. This software makes it easy for market researchers to comprehend who their customers are, what challenges they are facing, and how these issues can be resolved.

Easy-to-read visual data: Using this software, data scientists can visualize their data to readily analyze information, gain insights, and discover trends. The data visualization feature uses geographical maps, heat maps, and timelines to transform unstructured data into actionable insights.

Reduces data errors: Qualitative data analysis software reduces data errors caused by manual intervention in different processes such as data collection and segregation. Clean data results help in making more accurate business decisions.

Key purchase considerations for qualitative data analysis software

Here are a few factors to consider while selecting qualitative data analysis software:

Business objectives: Determine your business objectives and establish desired results. Select a QDA software that consists of suitable data and reporting tools to help you achieve your current as well as future business goals.

System integration: The software you choose should be able to integrate with your existing system. This will allow you to access analytics from applications that you are familiar with and transfer data to/from other systems whenever required.

Type of data analysis: Consider the type or method of qualitative data analysis that will provide accurate and actionable insights for your research objectives. Whether your research objective requires content analysis, transcription analysis, or it requires data coding and text analysis.

Management cost: Before investing in any software, consider the long-term management costs involved such as pricing of additional features and hidden charges.

Note: The application selected in this guide is an example to show a feature in context and is not intended as an endorsement or recommendation. It has been taken from sources believed to be reliable at the time of publication.

8 Great Tools To Perform Qualitative Data Analysis in 2022

Collecting qualitative customer data unlocks a potential goldmine of growth for your organization.

That is, if you know what to do with it. 

Qualitative data tells you how your customers feel and what they want from you. Examining your customer’s experience (CX) and putting the customer at the center of everything you do is likely to lead to an increase in your bottom line. 

However, in order to extract meaningful insights, you have to effectively analyze the data you collect. For this you’ll need the right qualitative data analysis tools.

Traditionally these tools were used exclusively by data specialists or analysts. Nowadays, however, data is so ubiquitous that even those outside of those remits can find themselves needing to make sense of large amounts of data. 

There are a lot of options out there and choosing the ideal software for your needs is not always easy. Here we’ll explain exactly what qualitative data analysis software is, then talk you through some of the best tools on the market. . 

What is Qualitative Data Analysis Software?

The 8 best qualitative data analysis software.

In order to understand what qualitative software can do for us, we need to start with what qualitative data actually is. 

Essentially, qualitative data is data that is non-numerical. It is descriptive and conceptual. Qualitative data is collected from a number of different sources. Some popular types include interviews, focus groups, surveys, e-mails, customer feedback, customer service tickets, observation notes, and phone calls.

This data, when it comes back to you, can be immense. Qualitative data analysis tools can help you organize, process, and analyze data for actionable insights. 

Qualitative data analysis software is used across a wide range of sectors and industries such as healthcare, the legal industry, e-commerce businesses, marketing departments - and everything in between. If your company has large amounts of data, you most likely need QDA software. 

The functionality of these tools varies greatly. At one end of the spectrum, you have software which allows you to tag and highlight important parts of your research. At the other end you have the fastest, most efficient kinds of software which employ the help of artificial intelligence to help you tag, analyze and visualize your research at record speed. 

Let’s jump straight into the ins and outs of 8 of the best qualitative data analysis tools out there.

Here is our list of the 8 top qualitative data analysis software. 

• MAXQDA - A well-established, reliable QDA Software 
• NVivo - Intuitive software offering some automation 
• ATLAS.ti - A powerful QA tool that offers some AI-improved functions 
• QDA Miner - Offers both a free and paid version 
• Quirkos - An easy to use, simplified tool 
• Dedoose - A tool that enables collaboration and team work
• Taguette - A free, open-source, data organization option 
• MonkeyLearn - AI-powered, qualitative analysis and visualization tool 

MAXQDA is a qualitative, quantitative, and mixed method data analysis tool. It lets you input data from a range of sources such as surveys, interviews, and focus groups to name a few. You can then tag and categorize this data for analysis. 

Best for:  In their words, MAXQDA was “created by researchers, for researchers.” This is across the education, non-profit and commercial sectors. 

Strengths: It’s easy to use and can support a number of different languages. It also uses AI to help users with audio transcription. 

This software was founded in 1989, so they have been around for a while and you can trust that their offerings are reliable. 

Weaknesses: Using it collaboratively in a team is not easy as individual users have to save their work and then merge the versions. This can be cumbersome. It’s also not the most attractive to look at compared with other software. 

Pricing: They have three different pricing plans that come with both an annual and perpetual price. They also offer a free trial. You can find more information here .

Like MAXQDA, NVivo is a software tool that allows its users to organize and store their qualitative data ready for analysis. You can also import word docs, PDFs, audio, images, and video. 

Best for: Researchers or academics looking for software with autocoding. 

Strengths: The interface is easy to use and is quite like Microsoft - this makes it instantly familiar and intuitive for many users. It’s much more powerful than some other offerings and offers automated transcription and autocoding. 

Weakness: NVivo struggles with languages that have characters, this isn’t a problem, for instance, for MAXQDA. 

While it is powerful compared to some of its competitors like Taguette, it doesn’t have the power to work with large data sets. After you’ve coded your data, you will still have to analyze your data manually, which could take a long time. 

Pricing: The price will vary according to a few factors, for example, whether you are a student or whether you are purchasing for an organization. More information can be found here.

3. ATLAS.ti

ATLAS.ti is a powerful QDA software tool, it supports large bodies of textual, graphical, audio and video data. Unlike other software in this category such as Quirkos, it has incorporated AI technology as it has evolved. 

Best for: This is best for research organizations, corporations, and academic institutions due to the extra AI features and the added cost.   

Strengths: Its interface is cleaner and sleeker than both Nvivo and MAXQDA, and collaboration is easier than in MAXQDA. It is also more powerful, boating both sentiment analysis and autocoding. 

Weakness: It can get expensive for individual users (such as students). Some users have also complained that the coding features are not that intuitive. 

Pricing: They offer a free trial and an extensive amount of licensing options based on sector and individual needs. More information can be found here.

4. QDA Miner

QDA Miner is a qualitative and mixed-method software that helps you to organize, code and analyze your data. They offer both a paid and free version called QDA Miner Lite.

Best for: Those looking for advanced visualizations and who are working alone.

Strengths: The latest version, QDA Miner 6, which was released in 2020, can link up with Tableau, a top data visualization tool , to give you an array of visualization options. 

Weakness:   QDA Miner doesn’t allow for collaboration which would be a big drawback if you are working on data with a team. Within the free version, the import and export functions are limited, as are the analysis functions. However, it might be enough to get you started or if you’re looking to test the waters. 

Pricing: They offer a number of packages according to sector and needs. Find details here.

Quirkos describes itself as a simple software tool that can help in the analysis of qualitative data. It is affordable and is popular within the education sector.  

Best for: Students and academics. 

Strengths: Quirkos offers a free trial which can be great if you are not sure what software is right for you. It’s also more affordable than some of the more advanced options like Atlas.ti. The drag and drop text functions make it simple and easy to use. You can also work collaboratively in Quirkos, and in real time.

Weakness: Its simplicity means that it offers less functionality. You have to code manually and this could be a deal-breaker if you have a lot of data. It also has fewer import options than other software. 

Pricing: There are three different options. Student, academic, commercial. Prices vary according to version and whether you want it cloud or offline. You can find more information here.

Dedoose is a 100% web-based tool for qualitative analysis. It was created by academics from UCLA and was designed to analyze both qualitative and quantitative data. It’s capable of importing data from a range of different formats, including documents, images, audio, video, and spreadsheets. 

Best for: Those who want a fully web based option where they can easily collaborate with team members.

Strengths: This software is team-oriented and user-friendly. It’s easy to import both text and visual data. It’s also compatible with mobile. 

Weakness: While they boast affordability, Dedoose can work out to be more expensive than other software as they only charge a monthly fee rather than a yearly license. The fact that it’s 100% web based may also be a negative for some. Like Quirkos, there is no AI or machine learning used in this tool. 

Pricing: Dedoose offers different prices depending on whether you’re an individual, student or group. More information can be found here.

7. Taguette

Taguette is a free open source qualitative data analysis tool that allows you to tag your data so you can then export it for analysis. 

Best for: Those looking for a basic, free option to organize their data for analysis. 

Strengths: It is very simple and easy to use. The fact that it’s open-source is also beneficial for many. It offers both an online and local version. 

Weakness: The fact that it is so simple means some drawbacks. Taguette doesn’t support images or video like some of the others. It also comes with zero automation compared to some of the bigger players and can’t analyze your data for you. 

Pricing: Taguette is free to use.

8. MonkeyLearn

MonkeyLearn is a powerful qualitative analysis software. It differs from most of the tools we have listed here in that it harnesses the power of AI and machine learning to make your data analysis process as efficient as possible. 

It offers an intuitive no-code interface that gets you to the stage of analyzing and visualizing your data in much less time. It’s also useful if you are not a data expert and you don’t have hours to spend coding manually or if, due to the size of your datasets, it’s simply impossible to do so. 

The MonkeyLearn Studio comes with pre-trained text analysis models, or, for more accurate insights, you can go ahead and build your own with your data and criteria. 

Once you’ve chosen your model you can start uploading your data from a range of options. Then you’ll be able to analyze this data with different tools like keyword extractor , feedback classifier , or sentiment analyzer . 

With that done, you can then view all your analysis in the interactive Studio dashboard (pictured below).

You can also learn more about our pricing and plans here . 

There are a number of qualitative data analysis software out there which will suit different needs. However, many of these tools require you to manually code your data in order to analyze it. 

If you are not that comfortable with coding or if you are working with datasets so large that this level of manual work is not feasible, you’ll need a tool like MonkeyLearn to help you process your qualitative data. 

MonkeyLearn provides a high level of automation, while still allowing you control of your data. This can make all the difference in terms of speed and cost. You can use your regained time to really understand the insights that crop up. 

Sign up for a free trial today to see how you can use MonkeyLearn Studio to best analyze your qualitative data.

Rachel Wolff

September 29th, 2021

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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: Apr 25, 2024 3:54 PM
• URL: https://guides.library.jhu.edu/QDAS

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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 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, such as text documents, audio recordings, images, videos, and more. Unlike quantitative data analysis software that focuses on numerical data, qualitative data analysis tool is 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.

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:

Efficient 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.

Comprehensive 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.

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.

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.

Collaboration and reporting

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 versatile qualitative data analysis software that empowers businesses to gain deeper insights from customer feedback. Its comprehensive suite of tools enables efficient data collection from surveys, reviews, and other sources. 

While primarily known for its robust survey capabilities, QuestionPro’s qualitative analysis features make it a valuable tool for businesses seeking comprehensive 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.

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.

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.

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.

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.

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.

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.

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.

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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MAXQDA Analytics Pro

MAXQDA Products: Detailed Feature Comparison

You can select from two different MAXQDA products. Compare their specific features and find the MAXQDA that works best for you.

Import and edit text documents (.docx, .odt, .rtf, .txt). Import PDF files in their original format. Rotate PDF pages. Import comments and text highlights from Word and PDF.

Import transcripts that were created with other software programs (manually or by automatic text recognition).

Import focus group transcripts or other multi-speaker texts with automatic speaker detection and many speaker-based analysis tools.

Import media files from common file formats. Analyze media files directly. Transcribe media. MAXQDA’s media player displays waveform, allows analysis down to 10th of a second. Audio Windows: MP3, WAV, WMA, AAC, M4A Audio Mac: MP3, WAV, AAC, CAF, M4A Video Windows/Mac: MP4, MOV, MPG, AVI, M4V, 3GP, 3GGP Video Windows only: WMV

Import survey data from Excel with automatic precoding of open-ended answers and assignment of statistical information. Import SPSS data sets (.sav).

Import survey data directly from SurveyMonkey account with automatic precoding of open-ended answers and assignment of statistical information.

Import Excel spreadsheets (.xlsx). Table view includes common table functionality: sort alphabetically, hide columns, edit or enter text into cells.

Import webpages as text, image, or PDF documents using the free Google Chrome Extension “MAXQDA Web Collector” or by entering their link; import multiple pages using Excel list.

Import video comments and video transcripts directly from YouTube. Autocode video comments with number of replies.

Import photos and images (.png, .jpg, .gif, .tif). Images can be rotated and zoomed.

Import text and PDF articles, as well as meta information about articles, textbooks, etc. from bibliographical software packages Endnote, Mendeley, Citavi, and Zotero.

Import projects that were created on a smartphone or tablet with the free field research app "MAXApp" (iOS / Android).

Import text data from one document, that is automatically coded and split into multiple cases.

Create new texts, tables, notes, summaries, paraphrases, and more directly in MAXQDA.

Import projects from other programs like NVivo that were exported to the REFI-QDA format.

Group documents in user defined folders. Create additional groups of documents with documents sets that documents are linked into.

Group codes in hierarchical order with up to 10 levels. Create un-hierarchical groups of codes with code sets that codes are linked into. Organize code system in map view.

Automatically backup projects in a user-defined time interval.

Check for spelling errors within documents and memos (English UK, English US, German, Italian, Spanish).

Large files are included in a project as linked external documents. Bundle all external documents in one zip folder to move to a different computer.

Easy to use drag & drop functionality for coding, code & document organization, memo positions, adding elements to dialogue windows, and more.

Adjustable screen layout, optimized views for exploration, transcription, coding, literature reviews. Open multiple documents in tabs or in a second window for side-by-side comparison.

Switch between line and paragraph numbering in text documents.

Switch between a light and a dark interface theme. Dark mode offers light text on a dark background, which is easier to read for low-vision users.

Speed control, rewind control, automatic time stamps that link audio and video to written transcript. Automatically insert speaker abbreviations. Define text snippets. Automatic speaker labels for focus group transcripts.

Increase transcription speed by using foot pedals. Supported foot pedals include Science I and Science II pedals.

Time stamp recognition when importing transcripts from another software that uses a supported time stamp format.

Wide variety of coding options: drag & drop to code in all data types, create code names from coded text segments (in-vivo coding and “open coding” mode), highlight coding (marker functionality) and emoticode (code with symbols). Assign keyboard shortcuts and code favorites for frequently used codes. Assign weight scores and add comments to coded segments.

Rearrange the code system and merge codes via drag & drop. Group codes in codesets. Several options for the display of code frequencies in the code system.

Compare coded segments of two or more codes in an interactive environment.

Write memos and attach them to documents, codes, or data segments or write free memos. Pick from 12 different memo symbols and create your own memo labels. Display memos in a sidebar next to the data. Special memo workspace to search, filter, and edit memos. Link memo content to parts of the data. Export memos or transform memos into documents.

Search for multipe search strings across documents or memos. Search for word combinations that can be connected with AND/OR/NOT search operators. Search with “regular expressions”.

Create interactive dashboards to explorer word or code occurrences. The dashboards visualize where in the data a word or code was used, which other words or codes are often applied nearby, and offers multiple interactive links to the original material.

Interactive display of coded data for multiple user defined search criteria, e.g. demographic data, code color, code weights. Complex coding query for coded segments. Search options include overlapping, if inside, only one code, and more.

Create and interrogate groups of cases or focus group speakers using demographical or other standardized data.

Link text, image, video, or memo segments within a project to each other. Link to external sources like files, webpages, or geographical locations in Google Earth™ / Maps™.

Journalize important steps of your research project and analysis in a logbook.

Automatic speaker detection for import of transcript with multiple speakers. Analyze focus group transcripts as a whole document or interrogate by speaker. Add demographic data or other standardized information for each speaker. Speaker-based analysis also available in several visualization and mixed methods tools.

Paraphrase text, image, audio, and video segments. View paraphrases in interactive workspace to compare paraphrases for multiple cases or to build up a category system.

Summarize coded data into your own words. Display and present summaries for cases or groups in interactive workspace. Attach a case or topic summary to a code or document memo.

Visually arrange and structure codes and themes on a blank canvas and transform the finalized structure into the MAXQDA Codes window.

Automatically code results of a text search or coded data search with new codes.

Display coded segments or code frequencies in synoptic table overviews.

Display code combinations in a table view to analyze co-occurrences of categories, aspects, dimensions, etc. (for any number of codes, or for subcodes of up to 6 codes).

Display the amount of data that has been coded with selected codes.

Categorize and code responses to open-ended questions in interactive workspace. Use text search to autocode responses.

Discover the sentiment of tweets, survey responses, or coded segments. Autocode data with their sentiment score.

Import a code system with code definitions from an Excel spreadsheet or from another QDA software program (REFI Codebook). Transport a code system from one MAXQDA project to another.

Import and export survey data from Excel or SPSS. Automatic detection of standardized data and open-ended responses. Standardized data is transformed into variables, open-ended responses are coded automatically with question text.

Joint display to compare content of coded segments across user-defined groups in an interactive table view.

Joint display to compare code frequencies and percentages across user-defined groups. Interactive result view to access underlying qualitative data.

Transform code frequencies of codes into document variables, that indicate how often the code occurs in cases.

Joint display to compare statistical information (frequency, mean, and standard deviation) for groups based on qualitative coding work.

Joint display to view and compare results of a qualitative and a quantitative study side by side in an interactive table.

Joint display to compare coded segments for groups. Groups are formed by standardized information.

Joint display to compare statistics (frequencies, mean, deviation) for groups. Groups are formed by assigned qualitative codes to cases.

Compare and explore data differences and similarities by looking at both qualitative and quantitative information and analyzing which documents are the most similar based on codes, code frequencies and standardised information.

Display or hide unlimited amount of coding stripes next to documents, highlight coded segments inside documents with code colors in MAXQDA products. Choose from millions of user-defined code colors.

Visualize memo types with memo type icons. Memos visually displayed in text margin, code system, document system. View memo preview at mouse over.

Visualizations are interactively connected to project data. Double-click on any visualization element to view source data.

Visualize data and findings in map view e.g., in the context of grounded theory research. Add project data (documents, codes, memos, coded segments, paraphrases, summaries) as well as external data (images, links, georeferences) to a map. Add text fields and freely arrange, group, or link elements with labeled arrows. Elements stay connected to original source data.

Use templates to automate the creation of concept maps for visual analysis and presentation of cases, code relationships, summaries, code hierarchies, and more.

Visually compare code assignments and code patterns of multiple text and table documents in a line-by-line chart.

Visually compare code assignments and variable values for documents, document groups, or focus group participants in a line chart.

Visualize the number of segments coded with each code by document, document group, or focus group participant.

Visualize co-occurring assignments of codes in the data.

Visualize sequence of code assignments within a single document by paragraph (text document), page (PDF document), row (table) or minute/second (media file).

Visualize code occurrence in single documents and sort by sequence, color, and frequency.

Create word cloud display of most frequently used words in documents or document groups. Interactively exclude words by adding them to a stop list.

Visually compare word occurrences across the sections of a document or across multiple documents. Interactively search for words. Interactively exclude words by adding them to a stop list.

Visually compare how code frequencies change throughout the span of one document – or across multiple documents at once. Interactively search for codes or add codes via drag-and-drop.

Create categories of words in MAXDictio and visually compare the categories’ frequencies across the sections of a document or across multiple documents.

Create word cloud display of the names of most frequently used codes.

Visually cluster cases on a map to analyze similarities with regard to the assignment of codes and variables in the documents.

Visually cluster codes on a map in MAXQDA products to analyze similarities with regard to the co-occurrence of codes.

Create bar and pie charts for code frequencies or variable values. Adjust design (fonts, colors, etc.) and save it for reusage.

Connect MAXQDA to your MAXQDA account to collaborate on a project file with colleagues. Additional TeamCloud license needed to start & manage team projects. This feature is available for singler user, network, and portable licenses.

Merge several projects into one project file.

Transfer specific project base from one project file to another, e.g., transfer codes you applied to documents to the same documents in a team members’ project file.

Review how different team members coded the same data. Check intercoder agreement based on code occurrence, code frequency, or code position. Calculate kappa coefficient (Brennan & Prediger, 1981).

Assign user roles and permissions to team members (e.g., admin, project manager, coding assistant) to protect your data from unwanted changes. Password protected login for project files.

Gather important visualizations, notes, quotes, codes, and other analytics results in an interactive workspace. Organize these results by topic in worksheets and add conclusions and insights as you go. Export worksheets as Word documents.

Create professional and customizable Word document reports of coded data that are ready-for-print.

Export table overview for coded segments, memos, codes, links, paraphrases, and summaries.

Export code system together with code definitions to present category system in reports to Word.

Export code relationships from the Code Relations Browser as adjacency matrix for import in network analysis tools such as Gephi.

Export table overview for documents that includes the document memo, selected variables, and frequencies of selected codes.

Display project data overview containing the project memo and automatically updated list of standardized project information on the amount of data.

Created projects can be opened and viewed with the free MAXQDA Reader software.

Export data, tables, analyses, graphics, etc. in various formats: DOCX for Word, XLSX for Excel, PNG/SVG images, etc. Save coded video or audio segments as individual video or audio clips.

Export a copy of a project, in which passages with selected codes are automatically anonymized.

Archive original data sources (optionally supplemented by statistical data, memos, code system, media files) in an easy-to-understand folder structure or as a compressed ZIP file.

Export projects to the universal REFI exchange format.

Full online manual in English and German. Video tutorials for many features. Getting Started Guide for new users.

Help button in every dialogue window jumps directly to corresponding chapter in online manual.

Global network of professional MAXQDA trainers with detailed profiles on MAXQDA webpage that can be contacted for workshops and consulting.

Free online support for all users of the current and preceding version.

Active user community discussing technical questions and best-practises in MAXQDA user forum.

User interface in more than 10 languages: English, German, Spanish, French, Japanese, Italian, Chinese simplified, Chinese traditional, Portuguese-PT, Portuguese-BR, Russian, Turkish, Polish.

Analyze data in any language (Unicode support), including symbol-based languages, for example Chinese, Japanese, or Korean. Supports left-to-right and right-to-left languages.

List frequencies of words and phrases of up to five words for selected texts or text segments. Interactive display of results. Use stop and go word lists to exclude or include words.

Visualize word frequencies and word combinations in interactive, tree-like structure.

Interactive display of search words including their surrounding context.

Create, import, and edit dictionaries. Analyze frequencies of dictionary items differentiated by texts or text groups. Automatically code items of the dictionary in the data, e.g, in responses of open-ended questions and tweets.

Visualize frequencies of dictionary word categories by documents or document groups in a matrix display

Lemmatization of text search results; different forms of a word (feel, feels, feeling) can be grouped together. Available for these languages: Bulgarian, Catalan, Czech, English, Estonian, French, German, Hungarian, Italian, Polish, Portuguese, Spanish, Swedish, Ukrainian.

Transfer document variables (e.g., demographic data) and code frequencies to MAXQDA’s Stats module for statistical analysis. Define variable labels, value labels and missing values. Use results of statistical analysis to group MAXQDA project data for further qualitative analysis.

Open and analyze external data sets from SPSS or Excel. Define variable labels, value labels, and missing values. Save data as SPSS files.

Full range of descriptive and inferential statistics:

– Calculate frequency tables, mean, median, standard deviation, mean error, confidence intervals, etc.

– Analyze crosstabs: chi-square, Phi, contingency coefficient C, Cramer’s V.

– Correlation: Pearson’s r, Spearman’s rho.

– Analyze multiple responses and matrix questions: crosstabs and grouped bar charts.

– Compare groups: t-Test for independent sampes (Shapiro-Wilk, Cohen’s d, Hedges’ g). Mann-whitney U-test for independent samples.

– Cluster analysis: typology table and line chart.

– Analysis of variance: Levene test for homogeneity of variances, Scheffe post-hoc test, Cohen’s d, Hedges’ g for pairwise distances.

– Reliability analysis for scales: Cronbach’s Alpha.

Merge and delete table rows and columns with automatic recalculation. Sort and rearrange result views. Use selected cases from table outputs as filters in the Data-Editor or for in-depth analysis of sub-samples. Unlimited undo for changes.

Create horizontal and vertical bar charts, pie charts, box plots, and histograms. Interactive scatterplot: identify and filter outliers and special cases for in-depth analysis.

Calculate new variables. Recode variables and/or code frequencies into same or new variables. Recode multiple variables at once.

Display results in organized output viewer in MAXQDA products. Copy results to clipboard for use in reporting programs like Word. Export to SPSS or other statistical packages.

The new optional add-on is available for subscription users and student license holders. A free MAXQDA account is required to use AI Assist.

Create automatic summaries of a selected text passages or coded segments.

Create automatic summaries of entire documents.

Create automatic summaries for the coded segments of a topic.

Have text or coded segments analyzed and potential (sub)codes suggested to you.

Create automatic paraphrases of a selected text passages.

Automatically define terms and elaborate concepts.

Automatically transcribe your media to text using AI with MAXQDA Transcription.

Interact directly with PDF or text documents by asking questions about them.

The world’s most powerful AI-based qualitative data analysis solution.

QualAI utilizes advanced AI technology to increase researcher efficiency, enhance data reliability, and mitigate bias.

researchers

QualAI aids researchers with data codification, thematic analyses, and content summaries to increase data reliability and mitigate bias.

organizations

QualAI helps organizations with market research, consumer analysis, business development, data aggregation and interpretation.

See how QualAI helps students analyze large-scale qualitative data sets, codify transcripts, and generate themes to reduce bias and increase efficiency.

ERIK ALANSON, Ph.d.

Co-Founder, QualAI

Academic Researcher

University Professor

tonkia bridges, ed.d.

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webQDA – Qualitative Data Analysis Software

webQDA is a qualitative, web-based data analysis software intended for all researchers and professionals conducting qualitative research. webQDA allows you to analyze text, image, video, audio, tables, PDF files, Youtube videos, etc. in a collaborative, synchronous or asynchronous manner.

and intuitive

Collaborative

environment

Price adjusted

to your needs

Easy Importing of Projects

With webQDA Software you can import your projects quickly and easily. Get to work right away.

Intuitive software

The Platform makes your user experience unique, making your day-to-day work easier.

100% online

Work on your Projects on any device with internet access.

100% compatible

The Platform is compatible with all existing operating systems.

Total data security

Work in a safe environment!

Seven Essential Steps (Cross-cutting Subtasks) for Qualitative Data Analysis with Integration of webQDA Software

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Ethics in Qualitative Research: a reflection… António Moreira Research Center on Didactics and Technology in the Education of Trainers (CIDTFF), Department of Education […]

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Six ways to represent results graphically with webQDA By: Sonia Verdugo (University of Salamanca) The visual representation of results offers the researcher […]

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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation

Challenges of qualitative research

Qualitative software, objectives and applications, choosing the right qda software, the utility of qualitative analysis software, qualitative analysis software.

Qualitative analysis looks into people's behavior by examining many artifacts and utterances. To be sure, researchers still talk about the days of manually sifting through printed pages by hand to analyze qualitative data. However, qualitative software supports contemporary researchers today by providing qualitative data analysis (QDA) tools to help us with our research.

Today, it can be hard to imagine a time when qualitative research , particularly in the social sciences, was a very analog, very manual process. These days, analyzing qualitative data is often accomplished through software, but it's essential to examine how we got here.

Researchers would talk about the old days of having dozens or hundreds of pages of data scattered on a floor in their living space as they organized their documents in a form that facilitated data analysis. Coding qualitative data would involve a highlighter or sticky notes to represent themes visually . In many research projects, the most advanced technology would often be a printer or a photocopier to commit data to a physical form.

Times have changed as the needs of qualitative researchers have evolved. Qualitative customer data can involve thousands or even tens of thousands of records of customer feedback and purchase history. In contrast, qualitative and mixed methods research have since highlighted the importance of statistical analysis in once-purely qualitative disciplines.

As the scope of qualitative data expands in breadth and depth, the challenges of analyzing all this information to make informed decisions require an intuitive interface to facilitate the research process. Let's look at some reasons why this is the case.

Data management difficulties

One of the significant challenges researchers face when conducting qualitative research is the management of large amounts of data. This issue is often compounded by the diverse nature of qualitative data, including interview transcripts , field notes , images, and audio and video files .

The data volume and its unstructured nature can make it hard for researchers to organize , manage, and track information systematically. QDA software offers powerful data management tools to handle large volumes of unstructured data. It provides functionalities to categorize, code , and annotate data efficiently, facilitating easier retrieval and organization of data.

Time and resource constraints

Qualitative research analysis can be time-consuming, especially when manually coding and categorizing data . The process can be labor-intensive and requires repeated data readings to ensure comprehensive understanding and coding accuracy.

QDA software provides solutions to this problem by streamlining coding and categorization processes. Advanced software even includes automatic coding features based on machine learning and artificial intelligence to expedite data analysis and interpretation .

Complexity of ensuring trustworthiness

Ensuring the trustworthiness or credibility of qualitative research is crucial yet challenging. The subjective nature of data interpretation often leads to concerns about the consistency of data coding and theme generation .

QDA software can help address these issues. It allows for a clear audit trail of coding decisions, providing transparency and improving the accountability of the research. Some software also offers inter-rater agreement features , facilitating the comparison of coding between different researchers and thus enhancing credibility.

Difficulties in data visualization and interpretation

Qualitative research often deals with abstract themes and patterns, which can be challenging to visualize and interpret. QDA software has tools for visualizing data patterns , connections, and hierarchies. This can include functionalities for creating models, charts, or graphs based on coded data, allowing for better data interpretation and communication of findings.

Challenge of integration and synthesis of data

Researchers often grapple with synthesizing different types of qualitative data or integrating qualitative and quantitative data in mixed methods research . QDA software can aid in the integration and synthesis of diverse data types. It provides tools to link different types of data (like text, audio, video ), interrelate findings from various data sources, and, in some cases, integrate qualitative data with quantitative data.

The issue of transparency

The qualitative research process requires a high degree of transparency for others to build on or audit the research. This becomes complex due to the volume and diversity of the data and the multiplicity of analytical steps. QDA software can help enhance the transparency of research through features that help to systematically document the research process, including data collection , coding decisions, and analytical reasoning.

Data analyzed typically include a huge variety of formats and usually consist of interviews (stored in audio, video , or written records) or questionnaires . The desired information is extracted from the collected data with various techniques and analyzed for trends and other features.

In quantitative research, this leads to quantitative analyses of the data, such as statistical analysis. This approach is used in several fields of study, including nursing, medicine, education, business, and the social sciences and humanities. This list is not exhaustive, as analysis is common in many disciplines, both in and out of scientific research.

Quantitative researchers have long relied on software to carry out their analyses, and qualitative researchers also have a host of digital tools they can count on to facilitate their analyses. Researchers may analyze data from focus groups to uncover perspectives of groups of people, while marketers analyze customer data in order to identify accurate insights regarding products or services.

Processing vast amounts of data, coding your original data sources , keeping track of interrelations, and even visualizing their importance in your coding scheme is best accomplished by using dedicated qualitative analysis software. ATLAS.ti is arguably the most powerful of the available qualitative analysis software packages today.

While the use of a full-blown qualitative analysis software package may not be considered necessary in some cases (e.g., to code only a few interviews ), the use of such research software allows researchers to play around with their data and quickly familiarize themselves with the package to code confidently. And let's not forget that any kind of analysis, including qualitative data analysis , is time-consuming, so learning to use specialized software can be a valuable investment for your future research needs.

Coding , a crucial stage of qualitative data analysis , can be much less tedious and time-consuming when carried out with the aid of a qualitative analysis software package. And although it may take some time to get acquainted with a proper qualitative analysis software package to code qualitative data electronically, this effort usually more than pays off in the long run.

Make ATLAS.ti your data analysis solution

An intuitive interface for powerful qualitative data analysis tools. Check us out with a free trial today.

There are countless considerations for the various software packages available for your data analysis . Let's look at a few of the key considerations.

Alignment with research objectives and methodology

One of the most important aspects to consider when selecting a QDA software package is whether it aligns with the specific objectives and methodology of your research. Different QDA software has different strengths.

For example, some software may excel in handling large datasets, while others may be more geared toward visual data analysis . Review the features and capabilities of the QDA software, understand how they align with your research requirements, and choose accordingly.

Ease of use

The user-friendliness of QDA software plays a significant role in its effectiveness. A steep learning curve can be a significant hindrance, especially in time-sensitive research projects. Moreover, a useful software package should be available for use across multiple operating systems. When researchers use multiple devices for their research, it's important to ensure that data analysis can seamlessly take place regardless of device or context.

Consider software that has an intuitive user interface, clear instructions, and accessible learning resources like tutorials and guides. It might be beneficial to trial the software or view demos to ensure that the interface and the operational logic are understandable and manageable.

Coding capabilities

Coding is fundamental to qualitative data analysis, so the software's coding capabilities should be carefully considered. You need to check whether the software offers a variety of coding options that suit your needs, such as creating inductive codes , importing deductive codes , generating in vivo codes , and so on. For instance, if you are following the grounded theory methodology , you can check how the software can be used to conduct open , axial , and selective coding.

A good software package should also allow for easy modification and reorganization of codes . Features such as auto-coding and code frequency analysis can also enhance your analysis process.

Tools that can conduct sentiment analysis and comparative analysis are useful when extracting insights from textual data, which can help researchers in all areas, from academic discourse research to commercial market research. In other words, the best software for qualitative text analysis has the most relevant tools for coding your data systematically and efficiently.

Data management features

Effective data management is a crucial part of qualitative research . The ideal QDA software should enable efficient organization, storage, and retrieval of data. It should support a wide range of data formats, such as text, images, audio, and video files.

The capability to cross-link different types of data can also be very beneficial. Moreover, software that allows the use of filters or search functions to quickly locate and retrieve specific data or codes can greatly streamline the research process.

Data visualization tools

Visual representation of data can be a powerful tool for understanding complex patterns and relationships. QDA software that provides robust data visualization options, like graphs, models, or network diagrams, can be beneficial for interpreting and communicating your research findings.

Ensure the software can visualize data in a way that aligns with your analytical needs. Additionally, you can check if the software you employ can export data seamlessly to other programs like Microsoft Excel that can generate data visualizations that are useful to you.

Integration with other software

The ability to seamlessly integrate with other software can be a crucial factor in choosing QDA software. Many researchers use a variety of tools for data collection , data cleaning, qualitative and statistical analysis, and report writing .

A good software package should also be able to interface with statistical analysis software such as SPSS and reference management software like Zotero. Researchers tend to use different programs to address aspects of their research agenda (e.g., writing the report, creating the presentation, or conducting statistical analyses in mixed methods studies ), so it is important that the software they choose can seamlessly import data from or export data to all of the programs the researcher uses.

Inter-rater agreement features

Inter-rater agreement can powerfully bolster the trustworthiness of the findings. If your research involves multiple coders, you may want to consider QDA software that allows for comparing and analyzing the consistency between different coders. This feature can be very useful to verify coding systems or triangulate findings.

Software support and community

Good customer support and an active user community can be extremely valuable, especially when encountering difficulties or when learning how to use new features. Software with a responsive support team and a vibrant user community for knowledge exchange is likely to provide a smoother user experience.

Budget considerations

Budget constraints are a reality for many researchers. While there are high-cost, high-feature options, there is also affordable or even free QDA software that offers a decent range of features suitable for many research projects.

The key is to balance your budget with your needs. Be mindful that some software requires subscription fees, which might not be sustainable in the long term for some projects.

Software can save great time and effort in relatively mechanical processes of research. There are many dedicated programs of various kinds available to social researchers that can be used for a variety of different tasks.For example, software could locate particular words or phrases; make lists of words and put them into alphabetical order; insert key words or comments; or count occurrences of words or phrases.

In addition, qualitative analysis software can facilitate data management, analysis , and visualization . Harnessing such tools can make research not only more efficient but also more flexible by making it easier to explore different angles in the data.

Limitations of data analysis software

Some analysis software will retrieve text, analyze text, and help to build theory. Although a computer can undertake these mechanical processes, it cannot think about, judge, or interpret qualitative data.

While automated text analysis tools are improving at breathtaking speeds, they may have limitations with handling certain complex analytical processes, such as the nuanced consideration of cultural factors or the dynamism of human emotions. Using such tools also requires careful input and regular monitoring to ensure accurate coding and categorization, as automated processes can make mistakes or overlook subtleties.

Despite the many advantages QDA software offers, it's important to remember that it is a tool, not a replacement for the human researcher. While software can help manage and code data , the process of interpretation – of identifying patterns, making connections, and extracting meaning – is distinctly human. The researcher's subjectivity , intuition, and understanding of context are key elements in qualitative research , shaping rich, nuanced insights that software alone cannot produce.

For qualitative data analysis software, turn to ATLAS.ti

Qualitative research tools easily available at your fingertips, starting with a free trial.

This is the end of Part 2 of The Ultimate Guide to Qualitative Research. For Part 3 click here

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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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Qualitative data analysis software

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Qualitative data analysis software: comprehensive guide

We at Dovetail understand the importance of qualitative data analysis in making informed decisions. With the advent of technology, qualitative data analysis software has become an indispensable tool in research. 

We aim to provide a comprehensive overview of qualitative data analysis software to help you decide on choosing the right software for your needs.

What is qualitative data analysis software? 

Qualitative data analysis software is a tool that helps in the systematic analysis of non-numerical data. This software helps researchers to organize, code, and analyze qualitative data, making it easier to identify patterns and themes in the data.

Why use qualitative data analysis software? 

Qualitative data analysis software provides several benefits to researchers. It saves time and reduces errors, making the analysis process more efficient and accurate. It also enables collaboration and sharing of data among team members.

Features to consider when choosing qualitative data analysis software when choosing qualitative data analysis software, consider the following features:

Compatibility with your operating system

Ease of use

Data import and export options

Codebook creation and management

Data visualization and reporting capabilities

Collaboration features

Customer support

Remember, the right software can make all the difference in the quality and efficiency of your analysis.

Best practices for effective qualitative analysis

Data preprocessing: ensure data is properly organized and tagged before analysis to save time during coding.

Constant learning: stay updated with the software's features and updates to maximize its potential.

Collaborative communication: maintain open communication within your research team to ensure everyone benefits from the software's capabilities.

Can I use qualitative data analysis software for quantitative data?

No, qualitative data analysis software is specifically designed for non-numerical data. For quantitative data, dedicated statistical analysis software is more appropriate.

Is there a free version of qualitative data analysis software available?

Yes, some qualitative data analysis software offers free versions with limited features. However, for comprehensive functionalities, investing in a paid version is advisable.

How steep is the learning curve for using qualitative data analysis software?

The learning curve varies based on the complexity of the software and your familiarity with qualitative analysis concepts. Most software providers offer tutorials and support to aid in the learning process.

Can I collaborate with team members using these tools remotely?

Most qualitative data analysis software allows for remote collaboration, enabling team members to work together regardless of their physical location.

What are some emerging trends in qualitative data analysis?

Emerging trends include the integration of AI for advanced sentiment analysis, improved visualization techniques, and the incorporation of data from multiple sources for richer insights.

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FEEDING YOUR QUALITATIVE NEEDS

Have you ever searched "free qualitative research software" only to be disappointed that nothing lets you tag your materials? Search no more! Taguette is a free and open-source tool for qualitative research. You can import your research materials, highlight and tag quotes, and export the results!

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Taguette works both on your local computer (macOS, Windows, Linux) and on a server. When running Taguette locally, your data is as secure as your computer . That means that if you can have your data on your computer, you can run Taguette with no worries.

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Taguette allows you to upload your research materials and tag them, just as you would use different color highlighters with printed paper. You can add new tags, then just select some text, click 'new highlight', and add whatever tag you find to be most relevant!

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Export Results

After you've done your work highlighting materials in Taguette, you can export in a variety of ways -- your whole project, codebook, all your highlighted quotes (or ones for a specific tag!), and highlighted documents. It's a good practice to keep an archival copy of your work!

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Coding the Real World: Understanding Real-World Evidence in Healthcare with NVivo Qualitative Data Analysis Software

Think about the last time you picked up a prescription from the pharmacy. If you read the information in the package insert, you may have assumed that the data about reported side effects and other patient outcomes was all drawn from randomized controlled trials. This may not be the case. Instead, you may have been reaping the benefits of a form of qualitative research output known as real-world evidence (RWE).

In the webinar Coding the Real World: What Is Real-World Evidence in Health and Why Is NVivo Critical? , three senior researchers from Cerner Enviza, an Oracle Company, explained why RWE is increasingly critical in drug development and healthcare research. They also described how coding qualitative research with NVivo helps generate robust analyses from unstructured data that communicate insights to research teams, journal publishers, regulators, and commercial stakeholders.

Representing Cerner Enviza were:

• Kathleen Beusterien, MPH – Principal, RWE
• Colleen Welsh-Allen, RN – Qualitative Research Subject Matter Expert in the U.S. for Commercial, RWE, and Regulatory
• Rebecca Nash, PhD – Business Leader, RWE

In this article, we’ll highlight the main discussion points – from their research methods to using NVivo coding software for qualitative data analysis or QDA.

Defining Real-World Evidence and Its Applications in Healthcare Research

“If you're like me, a qualitatively trained social scientist,” said Dr. Rebecca Nash, “the term ‘real world’ seems a little bit funny, because isn't everything the real world?” However, she explained, RWE within the healthcare and pharmaceutical industries simply means any evidence that isn’t gathered within the context of a randomized controlled trial. RWE can also mean any evidence that isn’t generated by a lab test or imaging technique, such as a blood analysis or MRI.

Real world evidence examples in the areas of epidemiology, natural history of disease, diagnosis and treatment pathways, risk management and effectiveness, and patient outcomes, and cost of disease.

Usually, RWE is gathered through qualitative interviews focused on understanding patient preferences or health outcomes – any change in their status following a treatment or procedure. “Rigorous qualitative analysis is so important in bringing forth the voice of the patient,” said Kathleen Beusterien.

Insights drawn from RWE complement randomized controlled trials and help inform drug development at every stage. RWE can influence clinical trial design, regulatory compliance reporting, treatment or diagnosis pathways, and marketing. It also informs the prescription medication inserts that explain side effects which can’t be measured with lab tests like pain, fatigue, or symptoms of depression.

Alt text: Timeline showing where real world qualitative data and research may happen within the product lifecycle. Phases before the launch include early development, trial design and execution, regulatory submissions and after the launch include medical, value, and access, and commercial.

The Cerner Enviza team presented three examples of how RWE helped researchers understand patient needs and experiences during drug development:

• Interviews with the families of pediatric patients coping with a rare genetic disorder called neurofibromatosis type 1 gave researchers insight into the symptoms that potential therapies could help treat.
• RWE from parents of children with severe allergies helped researchers develop an interactive diagnosis pathway that explained how clinicians can help families move from the chaos of an initial attack to informed self-management of their child’s condition.
• After conducting qualitative research, development teams were better able to understand why diabetes patients preferred the nasal spray form of glucagon – a drug that stops potentially fatal hypoglycemic episodes – rather than the injectable form.

Qualitative Analysis and Research Methodologies for Gathering RWE

The Cerner Enviza team explained that while the FDA and other regulators increasingly accept or even require RWE about patient preferences or health outcomes, there are guidelines about how to conduct this kind of qualitative research. There are two main methodologies for capturing RWE in the healthcare or pharmaceutical context.

First, there is concept elicitation (CE). CE involves an open-ended, narrative-style one-to-one interview. “It's a fancy name for qualitative interview,” said Colleen Welsh-Allen. She went on to explain that these interviews can be conducted over the phone, in person, via an internet video tool such as Zoom, or in an online chat. In addition to yielding insight into patient needs and experiences, CE can also help inform the development of survey questions for quantitative studies.

The second methodology for gathering RWE is a cognitive interview. Cognitive interviewing as a methodology was developed in the 1980s , and is often required by federal agencies as a step in the development of a quantitative survey. In a cognitive interview, “we really are hoping to understand how a subject arrives at their answer,” explained Colleen Welsh-Allen. Researchers will ask patients to answer a question while explaining their reasoning. “We want to make sure that everybody has universal understanding of the intent of the question,” Welsh-Allen clarified.

After conducting qualitative research, it’s time to analyze it. That’s where NVivo comes in.

Coding in NVivo for Qualitative Research

Because healthcare and pharmaceutical research is subject to institutional review boards (IRBs, or research ethics committees), it’s required to have verbatim transcripts for all interviews conducted, to obtain counts, and to carry out rigorous coding of interviews. This may not always be required for qualitative research in other fields, but where necessary, it can be incredibly helpful to employ a qualitative data analysis tool for transcription, data visualization, and to analyze data in large quantities.

While it’s possible to analyze qualitative research in a variety of ways – for example, through discourse analysis or grounded theory methodologies – the Cerner Enviza team uses thematic analysis because it allows for a bottom-up approach that lets patient concerns or experiences emerge from the data.

NVivo’s coding features allow for robust thematic analysis and speeds up the coding process with user-driven machine-powered autocoding . This automated tool for qualitative coding of textual data saves valuable time and can reveal themes and sentiments that might otherwise have gone unnoticed.

With NVivo, RWE researchers can:

• Tease out repeated patterns and construct themes by analyzing qualitative interview transcripts.
• Indicate whether qualitative data is spontaneous/unaided versus prompted from interviewer questions.

NVivo can also generate a range of visualizations, such as saturation grids, which help analysts determine whether follow-up interviews may be necessary. Plus, with powerful NVivo Transcription , researchers can import audio and video files to produce verbatim transcriptions with 90% accuracy.

The Cerner Enviza team was also able to work together efficiently with the help of NVivo Collaboration Cloud . With this tool, they could securely share data and insights to the same project and update, code, and analyze research in real-time.

NVivo’s rigor and adaptability makes it possible for research that includes RWE to meet the standards of medical and academic journals, conference organizers, and regulators. With text analysis, content analysis, and sentiment analysis supported by NVivo, teams can gather RWE that gives patients a voice in the treatments and interventions that can advance healthcare.

Learn More About RWE and NVivo Qualitative Data Analysis Software (QDA Software)

Interested in watching the full presentation on using coding software for qualitative research and RWE in drug development? Fill out the form on this page to access the full webinar recording and handout materials.

Start exploring what’s possible in your research project with NVivo by requesting a 14-day free trial of the most cited qualitative analysis tool today.

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Recent Articles

Parenting Emerging Adults with Multiple Sclerosis: A Qualitative Analysis of the Parents’ Perspective

• Original Paper
• Open access
• Published: 15 May 2024

Cite this article

You have full access to this open access article

• Federica Graziano   ORCID: orcid.org/0000-0001-9370-6050 1 , 2 ,
• Martina Borghi 2 , 3 ,
• Silvia Bonino 1 &
• Emanuela Calandri 1  

Parents of emerging adults are requested to adjust their level of support and control according to their child’s developmental age and to foster their autonomy. This developmental task may be more difficult when emerging adults are suffering from a chronic illness. Parenting emerging adults with a chronic illness is an under-investigated topic, especially with reference to multiple sclerosis (MS), a chronic neurological disease usually diagnosed in emerging adulthood. The study aims to qualitatively explore the characteristics of the relationship that parents report having with their emerging adult children (18–29 years) with MS. Specifically, we investigated how the dimensions of support and control emerge from the parents’ perspective, whether overparenting (characterized by both oversupport and overcontrol) emerges, and its characteristics. Eleven semi-structured interviews were conducted with parents of emerging adults with MS, and a qualitative content analysis was performed through Atlas.ti 6.0 software, combining a deductive and an inductive approach in relation to the study aims. A system of 13 codes was defined and a total of 141 quotations were codified. Overparenting appears to be the most frequent relational mode among the parents interviewed. Most quotations referred to oversupport (in particular, parents report anticipatory anxiety about child’s well-being and show excessive indulgence and permissiveness) and overcontrol (in particular, parents report a vicarious management of daily life and medical therapies). The study gives indications for psychological interventions helping parents to adequately support their children while encouraging their autonomous management of daily life and illness-related difficulties.

Most of the parents interviewed report overparenting their emerging adult children with MS.

Oversupport is characterized primarily by parental anticipatory anxiety, excessive permissiveness and infantilization.

Overcontrol is characterized primarily by vicarious parental management of both daily life and medical therapies.

For some of the parents interviewed, adaptive parenting may coexist with overparenting.

Some of the parents interviewed manage to balance support and control, even when relationship difficulties arise due to MS.

Avoid common mistakes on your manuscript.

Emerging Adults and Chronic Illness

Developmental psychology has long emphasized that individual development takes place across the entire lifespan (Baltes, 1997 ; Elder & Shanahan, 2006 ; Magnusson & Stattin, 2006 ) and is promoted by challenges or crisis; when people are successful in facing a challenge and their resources are transformed and increased, development has occurred (Hendry & Kloep, 2002 ). These challenges are mainly linked to typical and normative developmental tasks of each period of the lifespan (Elder & Shanahan, 2006 ). For example, the developmental tasks of emerging adulthood (18–29 years of age) are related to gaining independence from parents and making choices about study and future career paths, as well as about committed relationships and parenthood (Arnett, 2000 ; Arnett et al., 2014 ; Smorti et al., 2020 ). Challenges can also be related to non-normative transitions; specifically, the diagnosis of a chronic illness during emerging adulthood is an unexpected event for this period of the lifespan and represents a great challenge for the individual.

Chronic illnesses usually diagnosed in emerging adulthood include multiple sclerosis (MS), a degenerative neurological disease that affects women three times more than men in Italy as in the rest of the world (ATLAS of MS, 2020 ). Young people diagnosed with MS have to face a double challenge, linked to the developmental tasks typical of emerging adulthood combined with the life-long event represented by illness (Bonino, 2021 ). Multiple sclerosis is characterized by various symptoms (fatigue, pain, motor and sensory disorders, bladder problems, sexual disturbances, and cognitive impairment) and usually has an unpredictable and fluctuating course with relapses and periods of symptom remission (relapsing–remitting MS [RR-MS]). The main fear of people with MS is that they will suffer a severe motor disability that will force them to use a wheelchair, although this condition only affects a minority of individuals. Like for most chronic illnesses, pharmacological therapies are not resolutive, but they treat attacks and slow down the progression of the disease (Lublin et al., 2014 ). The presence of multiple and fluctuating symptoms and the unpredictable course have a huge psychological impact on individuals. Although emerging adults diagnosed with MS usually have moderate physical disability (Solari et al., 2008 ), depressive symptoms, anxiety, and reduced quality of life are generally reported (Buchanan et al., 2010 ; Rainone et al., 2016 ). MS can be defined as an unexpected break in the process of identity redefinition (Calandri et al., 2020 ; Charmaz, 1983 ) which deeply affects emerging adults’ future life projects. While there are studies on the experience of emerging adults with MS, the parents’ perceptions about an emerging adult child with MS are still largely unexplored.

Parenting Emerging Adults with a Chronic Illness

Development tasks throughout the lifespan concern not only individuals but also the family in which they live. Specifically, the main developmental task of the family with emerging adult children is to promote their need for increasing independence and autonomy and to redefine family relationships (Scabini et al., 2006 ). Parental support and parental control are the central dimensions of the four parenting styles (authoritarian, authoritative, neglectful, and indulgent) described by Baumrind ( 1971 , 2012 ). Support and control are relevant not only during childhood and adolescence, but also during emerging adulthood. They must be developmentally appropriate to promote offspring autonomy and independence (Soenens et al., 2007 ). On the contrary, parental overprotection and excessive control toward emerging adult children have proved to be related to difficulties in the process of identity definition and autonomy acquisition (Inguglia et al., 2016 ; Luyckz et al., 2007 ; Manzeske & Stright, 2009 ). The construct of overparenting (or helicopter parenting ) has been introduced in developmental studies to define a parenting style toward emerging adult children characterized by high support, combined with high parental control and low autonomy-granting (Padilla-Walker & Nelson, 2012 ; Segrin et al., 2012 , 2013 ). Specifically, overparenting is characterized by a control not consistent with the age of the child, an intrusiveness in the children’s choices, a provision of substantial support (i.e., financial, emotional), and an anticipatory problem-solving which limits emerging adult autonomy (Manzeske & Stright, 2009 ; Reed et al. 2016 ; Schiffrin et al., 2014 ; Winner & Nicholson, 2018 ). This dependence is especially detrimental in emerging adulthood, when the main developmental task of young people should be to increase independence from parents and reach greater self-reliance (Arnett, 2000 ). Although the links between overparenting and offspring adjustment are complex, it has proved to have negative consequences on youth development (Miano & Palumbo, 2021 ; Nelson et al., 2021 ). Specifically, overparenting was related to decreased self-confidence and maladaptive coping strategies (Givertz & Segrin, 2014 ; Odenweller et al., 2014 ), emotion dysregulation (Love et al., 2022 ), poorer academic functioning (Love et al., 2020 ; Luebbe et al., 2018 ), increased depression and anxiety and lower life satisfaction (Cook, 2020 ; Reed et al., 2016 ; Schiffrin et al., 2014 , 2019 ).

The challenges faced by the family system with emerging adult children may be increased when they suffer from a chronic illness. According to family systems theory, when illness enters a family, it deeply affects relationships and makes it necessary to redefine family roles according to the illness characteristics and the specific moment of family lifespan (Olson, 2000 ; Walsh, 2016 ). The family system is likely to become overprotective when a member experiences illness: family members tend to focus on intrafamily relationships, showing excessive emotional closeness and often limiting the individual space. This greater cohesion might be adaptive to manage the crisis but can have negative consequences in the long term for the process of adjustment to the illness as well as for family well-being (Walsh, 2015 ). An adaptive family functioning should in fact balance cohesion (i.e., the emotional bonding among family members and the amount of individual autonomy) and flexibility (i.e., the ability of the family to adapt rules to deal with stressors) (Olson, 2000 ).

Existing studies have largely focused on parenting young children and adolescents diagnosed with various chronic illnesses (the most frequently investigated are type 1 diabetes, cancer, asthma, and inflammatory bowel disease), whereas parenting a chronically ill emerging adult is still an under-investigated topic. Overparenting seems to be the normative parenting when having ill children and adolescents (Baudino et al., 2022 ; Haegele et al., 2022 ; Hullmann et al., 2010 ; Trojanowski et al., 2021 ). It has proved to be related to higher affective well-being among children with chronic illness, but it becomes increasingly negative as children move toward adolescence and emerging adulthood and need increasing autonomy (Gagnon et al., 2020 ). To our knowledge, only two studies have investigated parenting emerging adults with a chronic illness. The study of Sherman ( 2015 ) described overparenting toward people aged 18–30 who were diagnosed with cancer during childhood. Parents showed overprotection, characterized by infantilization and excessive preoccupations, and overcontrol, characterized by intrusiveness, as well as problem-solving, decision-making, and therapies management on the behalf of offspring. Overparenting was related to increased levels of anxiety and depression in children through the mediating effect of ineffective coping strategies (Sherman, 2015 ). Another study explored the links between parenting and identity among people between the ages of 18 and 25 with type 1 diabetes: emerging adults who reported parental overprotection showed greater difficulties in the process of integrating the illness into their identity. The authors claimed that overinvolved and intrusive parents were likely to decrease offspring self-efficacy in managing illness and this in turn negatively affected the process of identity definition (Raymaekers et al., 2020 ). Parenting emerging adults with chronic illness seem therefore characterized by the same overprotection and overcontrol identified among some parents of healthy youth and this overparenting has negative consequences for the adjustment to the illness as well.

The Present Study

There is a lack of research on parenting emerging adults with a chronic illness and to our knowledge no previous study has investigated the relationships between parents and emerging adult children diagnosed with MS. This situation is specific and only partially comparable to that described in the above-mentioned studies. The diagnosis of MS during emerging adulthood introduces itself into a normative individual and family history unexpectedly, bringing with it huge change. This is profoundly different from having a disease which started in childhood/adolescence and then experience the transition to emerging adulthood with the illness (Raymaekers et al., 2020 ; Sherman, 2015 ). Moreover, MS is not life-threatening, but it is a life-long condition characterized by great unpredictability. The role of parents as caregivers of emerging adults with MS is peculiar: due to the characteristics of MS (multiple symptoms, unpredictable and fluctuating course) parents experience a state of psychological uncertainty and different preoccupations about the future (Calandri et al., 2022 ; Strickland et al., 2015 ). These parents should normally adjust their level of involvement and control according to their child’s developmental level and MS adds a further and unexpected challenge. That is why it is crucial to investigate the characteristics of parenting, and whether it involves characteristics of overparenting. This knowledge can have clinical implications for health professionals to support parents who experience difficulties during this developmental transition.

The socio-cultural background of this study refers to the Italian situation, where most emerging adults live with their parents and leave home at around the age of 30, later than in other European countries (EUROSTAT, 2022 ). In Italy young people finish high school at 19, the average age at graduation is 25, and the entry in the job market is often difficult and temporary, with higher rates of unemployment than in other European countries (STATISTA, 2023 ). Young people in general postpone adult choices like involving themselves in a committed relationship and having children (Crocetti et al., 2012 ; ISTAT, 2021 ). Moreover, family relationships are usually characterized by strong bonding and conservative family values, like in other Mediterranean countries (Carrà et al., 2014 ). This long transition toward adulthood (Crocetti & Meeus, 2014 ) and prolonged cohabitation with parents have consequences on family relationships. Specifically, higher levels of overparenting are reported among Italian healthy youth when compared to other countries (Pistella et al., 2022 ). It is therefore important to deepen our knowledge about parenting emerging adult children with MS in this cultural situation. Interviews with parents make it possible to further explore their perceptions about parenting an emerging adult child with MS and the qualitative analysis makes it possible to gain knowledge on a still under-investigated topic.

To sum up, the present study had the following aims:

To explore the characteristics of the relationship that parents report having with their emerging adult children (18–29 years) with MS; in particular, to investigate how the dimensions of support and control emerge from the parents’ perspective.

To explore whether situations of overparenting (characterized by both oversupport and overcontrol) emerge and how they manifest themselves.

The COREQ checklist (Consolidated Criteria for Reporting Qualitative Research) (Tong et al., 2007 ) was followed to ensure a comprehensive and rigorous report of our qualitative study.

Research Team

The research team consists of SB, emeritus professor, expert in developmental psychology and psychological aspects of MS; EC, Ph.D, associate professor, expert in developmental and family psychology and in qualitative research; MB, psychologist and psychotherapist working in the MS Clinical Center were participants were recruited and trained in qualitative research; FG, psychologist, Ph.D, working at the Psychology Department of the local University and trained in qualitative research.

Participants and Procedure

Parents were recruited at a regional MS clinic center located in a large urban area [blinded for review]. The inclusion criteria were as follows: (1) having a child aged 18–29 with a diagnosis of MS; (2) that the child had a mild to moderate level of disability (Expanded Disability Status Scale [EDSS] score ≤ 5). The EDSS score (Kurtzke, 1983 ) is the most widely used measure of disability in MS and is evaluated by a neurologist (range 0–10). Patients scoring <5 are autonomous, ambulatory, sometimes needing rest or aid when walking long distances, with mild neurological deficits that impair full daily activities or require minimal assistance; (3) that the child has not had any cognitive deficit or psychiatric problems. Clinical information was obtained from the patients’ case sheets compiled by the neurologist. Eligible participants were contacted via telephone by a psychologist from the research team (MB) who gave detailed information about the research goals. Of the 17 parents initially contacted, 5 declined to participate due to family or work commitments. After the first interview, slight modifications were made to the wording of the questions, and this pilot interview was not included in the study. Therefore, a group of 11 parents participated in the study and their characteristics are reported in Table 1 . There were three couples (mother and father agreed to participate in the study and were interviewed separately). The interviews refer to 8 MS patients (3 females, 5 males), with a mean age of 23 years (SD = 3.1; range 19–28). All emerging adults lived with their parents. All were diagnosed with RR-MS, and the mean disease duration was 3.9 years (SD = 2.9; range 1–10 years). Interview abbreviations for each parent and characteristics of children are reported in Table 2 . Parents arranged date, time, and place (Hospital or University) for the interview with the psychologist who conducted the interview (FG and MB). Before the interview, participants were given an anonymous questionnaire to fill in to obtain sociodemographic information and evaluation of some psychological variables for descriptive purposes (Italian validated scales were used and measures are reported in Appendix 1 ). As for the HADS (Hospital Anxiety and Depression Scale, Zigmond & Snaith, 1983 ), depression scores were under the cut-off for the presence of significant symptoms (<7), whereas anxiety mean scores were between normal (<7) and borderline levels (8–10) indicating the presence of significant anxiety symptoms in some participants. The study was approved by the Hospital Ethics Committee (protocol no. 0013772) and all participants gave written informed consent before the interview. No benefit was given to participants for taking part in the research.

The Interview

Semi-structured interviews were based on a predetermined topic guide developed by the entire research team and investigating the following areas: (a) feelings and thoughts experienced when the child was diagnosed with MS and at present; (b) the relationship with the child, difficulties, and strategies in dealing with them; and (c) representation of the future of the child and future plans as a parent. Flexible and open-ended questions allowed participants to introduce topics important to them and made it possible for the interviewer to explore their responses in depth (the interview guide is reported in Appendix 2 ). Interviews were conducted by two members of the research team (FG and MB). Both psychologists had experience in conducting interviews and were trained in the use of qualitative methodology. They had no relationships with participants prior to the study. Participation of parents in the study was voluntary and independent of whether the children were receiving psychological support in the MS Center. These aspects allowed us to control for potential bias in the research.

During the interview, no one else was present besides the parent and the interviewer. On average, each interview lasted 50 min (range 30–70 min). The interviews were digitally recorded and transcribed verbatim by reconstructing the nonverbal and contextual aspects also thanks to the field notes written by the interviewer. Data saturation was reached after 11 interviews therefore no other participants were recruited. It was not possible to conduct a second meeting with participants to return them the transcripts and to get feedback on the results.

The study was conducted in Italy and interviews were conducted in Italian. The quotes in the article were translated into English by a professional native speaker translator in a process of constant discussion with the research team to verify that the content remained true to the original after translation.

Data Analysis

Interview transcripts were considered a single text corpus and underwent qualitative content analysis through Atlas.ti 6.0 software. Content analysis is a research method based on a systematic process of coding textual data (APA, Dictionary of Psychology). In relation to the study aims, a directed approach to content analysis was used (Hsieh & Shannon, 2005 ). According to this approach, theory on parenting practices deductively guided the definition of the initial codes, then new codes were inductively generated starting from the textual material (Mayring, 2014 ; Schreier, 2014 ). The analysis was carried out through the following steps: (1) transcripts were read several times by two team researchers (FG and MB) to achieve familiarization with data; (2) following a deductive approach and starting from the theoretical constructs of support and control (Baumrind, 1971 , 2012 ) and overparenting (Segrin, 2012 , 2013 ), four categories were defined after discussion within the research team: balanced support, balanced control, oversupport, and overcontrol; (3) two researchers (FG and MB) independently identified the excerpts that corresponded to these categories and regularly met to discuss the ongoing analysis; (4) starting from elements emerging from the textual material, the initial categorization was refined following an inductive approach with an iterative process involving the entire research team. Three categories (balanced control, oversupport, and overcontrol) were split into subcategories with specific codes to capture more nuanced aspects, and a new category was added (i.e., enmeshment) to codify an aspect that was not anticipated in the initial categories. The final system of 13 codes was discussed and approved by the research team and it is reported in Table 3 ; (5) after this final procedure, the two researchers then coded all the transcripts independently again. The inter-rater agreement was high (Cohen’s kappa = 0.79; p  < 0.001) (Cohen, 1960 ; McHugh, 2012 ); (6) discrepancies were resolved through discussion with a third judge (EC) until a consensus was reached on each uncertain use of codes.

Results of the qualitative content analysis are summarized in Table 4 . Using the code system previously described (13 codes), we codified 141 quotations.

Some parents spoke very little about parenting; in particular, 4 quotations were codified in the interviews P2 and P9, and 5 quotations in the interview P5. On the contrary, other parents addressed this issue to a greater extent; in particular, more than 20 quotations were codified in interviews P8 and P10. Three types of interviews can be identified: (1) interviews where codes referring to adaptive parenting prevail (interviews P1, P2, and P5); (2) interviews where codes referring to adaptive parenting and to overparenting are present in equal measure (interviews P3, P7, and P9); (3) interviews where codes referring to overparenting prevail (interviews P4, P6, P8, P10, and P11). For the sake of clarity, we have reported below results obtained following these three types of interviews, citing some of the most representative quotations.

Adaptive Parenting

The adaptive parenting is based on parental support and proximity appropriate to the child’s age and recognition of their need for autonomy. The following sentences well illustrate these aspects:

The relationship is good…as far as dialogue is concerned; he tells me a lot, and I feel very comfortable with him, … he’s 21…we’ve planted the seeds we needed to plant…you can repeat them to him, remind him about them…but we can’t spend the rest of our lives saying no… (P5)

We’re talking about a person who’s 28, so you need to adjust your behavior in such a way as to not be meddlesome, because she’s got his own life (P2)

Concerning the illness, parents highlight the need to support their children through the difficulties caused by MS and to foster the autonomous management of treatment. Some parents speak of a slow process of change in their relationship with their children, toward more and more respect for their decision-making autonomy. For example, two mothers say:

My biggest preoccupation is to support her as much as I can, so that she doesn’t live / experience these inadequacies the illness gives her as an obstacle to what she can do (P2)

Now I’m pretty calm about it and if he has a relapse, I let him deal with it the way he wants to … if he doesn’t call the doctors, that’s his business, I don’t do it for him …I try to respect his choices now (P1)

These quotations suggest how parents showing an adaptive parenting try to develop a progressive awareness about their child’s situation and implement a gradual distancing. This is a process that takes time and needs to be continually redefined.

Adaptive Parenting and Overparenting

The words of some parents reveal how aspects of adaptive parenting and overparenting are present in equal measure, showing a parental ambivalence. For one mother, the relationship with her daughter is difficult, but the illness led to greater support and proximity. This aspect is acknowledged as positive because it enriched their relationship. She said:

(The relationship) between mother and daughter was difficult before and remained difficult, but not because of the illness … because I’m not saying that the illness got us closer (…) but we did spend a lot of time together, which is maybe something we hadn’t done for a while …. And, from this, we learned to say I love you to each other a bit more (P3)

However, the balance between adequate support and oversupport is very delicate: the parent’s anxiety sometimes leads to oversupport. The parent realizes this and recognizes the need to relax control over the child. The same mother said:

I’ve always been worried about what could happen, then the illness came and at times I can be a bit obsessive…I have this idea that I’ve always got to have everything under control…but I’ve got to start thinking that it won’t always be like that (P3)

These words highlight how important it is for parents to gradually develop awareness of their own attitudes to change them so as not to be intrusive in their relationship with their children.

The ambivalence of some parents is manifested in relation to the dimension of control. A father acknowledges that his son is autonomous in the management of daily life and therapy. However, during the interview, the same father reports controlling behavior toward his son that sometimes leads to conflict. He said:

I’ve got to say he knows how to organize himself … he’s good…when he’s got to study, he doesn’t go out…he always gives himself his little shot in the legs every Sunday evening (P9)

At times I tell him to cover up because it’s cold, to not come home too late …and he always comes home late …you fight over little things (P9)

The links between overcontrol and oversupport also emerge from this father’s words: overcontrol seems to be justified by the need to support the son, but in a way that is not age appropriate. Even the use of the word “little shot”, in addition to minimizing the difficulties related to therapy, seems to be more appropriate for a relationship with a child than with an emerging adult.

Another father recognizes that his child is an adult and therefore an age-appropriate relationship is necessary. He said:

We get on well, but mom and dad always act like they’ve got a child at home, but you’ve got to realize that you’ve got a twenty-five-year-old adult …I’d like to talk to him…but I also don’t want to force these conversations on him, it’s his life, he deserves respect (P7)

In particular, he recognizes that one should not be overprotective and indulgent, for example, by avoiding certain speeches or behavior just because the child is ill.

If I have to tell him off, I don’t think: poor kid! He’s got problems! Because I think that’s the wrong way to go about it, i.e., I don’t want to keep him in a protective bubble (P7)

As mentioned earlier, parents who adopt an adaptive parenting try to avoid attitudes that are not age appropriate because they are dealing with an adult and no longer with a young child. However, the balance between balanced control and overcontrol is very difficult. For example, when it comes to obtaining medical information, aspects of overcontrol in the management of the disease emerge. In particular, the ambivalence emerges when this father justifies his desire to have in-depth information from medical professionals so he can be of help to his son.

my son asks me questions about the therapy, I’ve got to be prepared…I’d like to talk over in private with the neurologist…I get the law on privacy, I understand the need for respect, but we’re not disregarding the need for respect…I’m completely informed about what’s going on with my son and I need to be able to answer his questions (P7)

The examples given thus highlight parents’ awareness of how important it is to implement an age-appropriate parenting for their children, but also how difficult this is when they have a chronic illness.

• Overparenting

Overparenting seems to be the most frequent relational mode among the parents interviewed, especially among mothers, and it includes both aspects of oversupport and overcontrol. For a mother, oversupport is mainly manifested through infantilization. She said:

(My son) was diagnosed when he was really young [note: at 16], as a child he was unable to give himself shots. He was afraid of needles, so he was forced to grow up…having to inject the drug into himself every day…his first shots were group shots, i.e., they had to be administered in front of the whole family (P4)

Infantilization is intertwined with vicarious medical management, and it is often facilitated by the need for self-administered medical therapies at home. In the case of hospital therapy, parents are more forced to recognize the need for autonomy, because children relate to other patients and staff. Infantilization seems to be slowly giving way to recognition of greater autonomy on behalf of the child. The same mother quoted above said:

he’s a bit of a sickly child… at first, when he was younger, we always brought him to the hospital to undergo therapy every month, then he got his license and wanted to go on his own (…) I don’t want him to drive after therapy because he comes out of there he’s wiped out, so we worked out a compromise…he goes and eats something and rests, so he lets some time go by, and then he comes back on his own (P4)

For another mother, oversupport is manifested through a strong anticipatory anxiety for her daughter’s well-being and this situation worsened with the disease. She said:

I just can’t be detached … this was probably true even before, then increased with the illness…if she’s got a boyfriend problem, it’s like I were living through it too, that’s my problem (P8)

The illness has led this mother to feeling as if she had an excessive burden to deal with, and the situation worsens every time the daughter must have a routine medical check-up.

what happened was that every time she would call me, a wave of anxiety swept over me, My God! Something’s wrong! So I was in a constant state of incredible anxiety…even now, I feel like I’m much more apprehensive and stressed… I feel like I should do something about it…so, this stuff weighs me down; any time there’s an MRI or a check-up, it’s an ordeal…I start getting worked up six months ahead of time (P8)

During the interview, a strong identification and enmeshment of this mother with her daughter emerges; she goes so far as to say that she herself feels ill and would like to take her daughter’s place even at medical check-ups:

what I feel as a parent…is a bit like I myself were sick…you feel sick too…that is, compared to before, I feel sick…sometimes I say: “I’ll take the MRI in your place” (P8)

In our interviews, excessive anxiety often emerges as an aspect related to oversupport, although only rarely does it reach a lack of distancing and enmeshment.

In an interview with another mother, a relationship with the child based on indulgence emerges. The parent provides too much help to the child because of the illness, tends to justify him for some of his behavior and to concede more than necessary to avoid conflict.

The illness led us to pretty much always let him get his way…we’ve always tried to…give him a bit more than he deserved, we got him a motorcycle, then a car…to help him out as much as possible…which is what I think any parent would do (P10)

When he gets really angry, I’m always afraid it will make things worse…so, maybe to avoid any agitation, you try and pave the way a bit…. (P10)

In this parental couple, the father also acknowledges that he has behaved this way since his son was diagnosed with MS. He said:

It’s probably my fault too, since I’ve been keeping him under a glass dome… he was pretty much always given his way…let’s say it was easier to get things… we didn’t make him responsible in his daily life, even if he’s sick, he takes everything for granted (P11)

Sometimes the child is given goods to help compensate for the difficulties of the illness, without being asked, but to anticipate the solution to problems. This kind of overcontrol emerges from both the mother and the father interviewed:

When he needed an automatic car, we got it for him right away (P10) .

He wanted to be self-sufficient at home…to help him out and not see him climb up the stairs, we got a stairlift, but we didn’t tell him… he used it once and then never used it again, he’s sooner drag himself up the stairs… (P11)

These examples well highlight how much overcontrol reflects a self-centeredness of the parent, who does not put himself from the point of view of the sick child.

The overcontrol also emerges as maternal intrusiveness in the son’s daily life management justified with the excuse of needing to take care of him. Specifically, this mother takes care of preparing healthy meals for the child and does household chores for him. She said:

For almost a year now, every day for lunch I’ve been preparing home-grown vegetables for him, eggs from our own hens, and get milk from the farm because, if we do everything we can to keep his organism healthy, the medicine does a bit less damage. Now I also pay someone to come clean and iron for him (P10)

This type of relationship risks having negative psychological effects both on the child and on family dynamics. This mother’s words induce guilt in the child for what the parent does “for his own good” and the risk of conflictual situations arising in the family is reported.

because he’s got to realize that the sacrifices we’ve made over the years have been for his own good, not out of meanness (P10)

I’ve been paying for his groceries, but his brother pays for all his own expenses and rightfully so, points out that he shouldn’t be handed everything on a silver platter just because he’s sick (P10)

In an interview with another mother, the aspects of overcontrol over decisions concerning work and illness disclosure emerge. According to the mother, the son should communicate the illness to the future employer to have work benefits and submit the documents to obtain a disability certificate, while the son disagrees. She said:

Arguments always come up when we talk about work because I tell him that when he goes to work (…) he has to tell them he has multiple sclerosis because they ask for health records and a bunch of things (…) because it’s right that they know, because he needs treatment, personal days, etc… (P6)

We’re insisting that he …fill out the forms…for disability…he’s got to understand that if he doesn’t fill out the application for invalidity…maybe so he can be put on some special list …he’s going to find it really tough, that’ what I’m afraid of… (P6)

These examples also highlight parents’ inability to put themselves from their child’s point of view; this self-centeredness in our interviews is often accompanied by overcontrol.

During the interview, aspects of overcontrol over decisions concerning the illness also emerge; in particular, this mother feels that her son should face and compare himself with other young people with multiple sclerosis, for example within psychological support groups.

For example, I wouldn’t mind if he went some meetings once in a while to talk with other kids …his doctor should be the one to tell him to go to a psychologist…that way he might think about doing it (P6)

What emerges from this mother’s words is her difficulty to respect her son’s decisions (e.g., regarding the disclosure of the illness) and the impact this has on the family relationship in terms of dialogue and conflict.

he doesn’t want us to talk [about the illness], I’d like to talk about it, I wouldn’t have any problem with it, in fact, it would be liberating for me…. he doesn’t want to talk about it, I respect that, [even if] I can’t understand why …he says he doesn’t want any pity (P6)

However, this mother acknowledges in the interview that changes have occurred over time. She said:

We talk about it a bit more than we did at first, when it was absolutely tabu, we couldn’t bring it up because he’d leave, slamming the door, it was really difficult for the family to deal with (…) now life is a bit calmer, more composed, more serene than before (P6) .

The latter example shows well how parents have to go through a long, slow journey of accepting the child’s wishes, in which the parent tries to provide adequate support and implement an adaptive relationship.

The present study examined parental perceptions of their relationship with an emerging adult child diagnosed with MS. In particular, the interviews with parents explored dimensions of parental support and control and the presence of overparenting practices characterized by both oversupport and overcontrol. From the interviews with parents, it appears that support and control are core elements of their relationship with their adult children and are closely intertwined. This is consistent with the developmental psychology literature, which suggests that both support and control shape parenting from infancy through emerging adulthood (Baumrind, 1971 , 2012 ; Soenens et al., 2007 ). These two dimensions must be developmentally appropriate to promote children’s autonomy and independence, and this is particularly important in parenting emerging adult children, whose most important developmental task is to acquire independence from the family (Arnett, 2000 ; Scabini et al., 2006 ). The diagnosis of a chronic illness such as MS presents an additional challenge to the family system, which is already engaged in this redefinition of family relationships. Our findings suggest that parents respond differently in the face of this challenge: some practice adaptive parenting with balanced support and control, others are ambivalent, and adaptive parenting practices appear to coexist with overparenting, yet others exhibit overparenting practices characterized by both oversupport and overcontrol.

Parents who succeed in balancing support and control often report that they have always tried to implement these parenting practices, even as their children grew and moved toward adulthood. These parents have tried to maintain and adapt these practices, even in the face of changes brought on by the illness. For some parents, supportive relationships within the family have deepened since diagnosis, and affective closeness goes hand in hand with respect for children’s autonomy in managing care and making decisions in a process of change that is not easy and is still ongoing. The individual and contextual resources of these parents are likely to enable them to successfully manage the challenge of the relationship with an emerging adult child with a chronic illness and transform it into an opportunity for development (Hendry & Kloep, 2002 ).

Other interviews suggest that the boundaries between adaptive parenting and overparenting are blurred. Some parents appear to oscillate between adaptive parenting and overparenting, reflecting ambivalence and difficulties in maintaining developmentally appropriate control and support. In particular, some interviews suggest that affective closeness risks turning into overprotection in the presence of excessive parental anxiety. This finding is consistent with studies showing that high levels of parental anxiety are often associated with overparenting (Gagnon & Garst, 2019 ; Jiao & Segrin, 2022 ; Segrin et al., 2013 ). The presence of significant anxiety symptoms in the parents in our sample is also confirmed by the scores reported on the anxiety self-assessment questionnaire. Similarly, the need to provide effective support to children sometimes leads parents to justify the need to control them in daily life as well as in coping with illness in ways that are developmentally inappropriate. This finding has been found in the literature on overparenting toward healthy emerging adults (Schiffrin et al., 2014 ; Winner & Nicholson, 2018 ) and also appears to apply to the relationship between parents and emerging adult children with MS. The coexistence of adaptive and non-adaptive parenting practices underscores that the process of adaptation to chronic illness is complex and continuous for the family system (Calandri et al., 2022 , 2022 ; de Ceuninck van Capelle et al., 2016 ; Messmer Uccelli, 2014 ).

Most of the parents interviewed reported overparenting practices that are indicative of the difficulties brought on by the illness, which disturbs the often already delicate balance in the relationship between parents and children. Oversupport and overcontrol are closely related and often originate in parental anticipatory anxiety about their child’s health. Parental anxiety is mainly due to the uncertainty of MS and causes some parents to overprotect their emerging adult children by overindulging or infantilizing them. Previous studies have shown that both indulgence and infantilization, even when well-intentioned and motivated by concern for children’s health, do not promote children’s decision-making autonomy and return them to a state of dependence on their parents (Raymaekers et al., 2020 ; Sherman, 2015 ). Our findings shed light on the specificity of this situation when parenting an emerging adult with MS.

Indulgent parents show excessive tolerance or find solutions to their children’s daily or work problems or offer solutions to cope with illness difficulties. Through indulgence, parents often do not put themselves in the child’s shoes and propose solutions that are not desired and ultimately not accepted. As described in the literature, parental indulgence can reinforce the child’s perception of being ill and the belief that he or she cannot cope with difficult situations independently but must rely on others for help (Sherman, 2015 ).

Other parents show infantilization of their children; they treat them as if they were younger and believe they need their help, reverting to a situation of parental care when they were a child. Infantilization is common when children’s illnesses begin in childhood and parents continue to assume a caring role even when children are adults and they are expected to gradually learn to self-manage their chronic illness (Lerch & Thrane, 2019 ; Sherman, 2015 ). The situation is different for MS because the disease has a sudden onset in emerging adulthood. This transition may result in parents reassuming a caring role, altering a parent–child relationship that may have already been redefined in terms of increasing independence. Although emerging adults with MS may need adequate psychological support from parents to cope with the illness, most do not have debilitating physical disabilities that require parental assistance. Therefore, excessive parental care is often motivated not by an actual need of the children but by anticipation of future difficulties due to the unpredictability and fluctuating course of MS. This creates a vicious cycle that reinforces parental anxiety.

Overprotective parents tend to be overly controlling of their children, especially through vicarious management of their daily lives and medical treatments. Parental intrusiveness may manifest itself not only through behavioral control (e.g., preparing meals for the children, participating in medical examinations) but also through psychological control (e.g., creating feelings of guilt in the children for what the parents do for them, pressuring the children to make a decision, not respecting their decisions). Overcontrol often originates from a parental self-centeredness that results in an inability to place oneself from the child’s point of view. All these parental behaviors limit children’s acquisition of independence and autonomous coping with daily life problems and illness-related difficulties (Givertz & Segrin, 2014 ; Sherman, 2015 ). Moreover, excessive control can lead to difficulties in family relationships and negatively affect parental well-being; indeed, some parents recognize and express concern about the presence of conflict and lack of communication with children. This finding is consistent with studies that emphasize the negative consequences of overparenting practices not only for the child but also for the quality of the parent–child relationship (Segrin et al., 2012 ) and for parental adjustment when children have a chronic illness (Baudino et al., 2022 ). As suggested by Walsh ( 2015 ), overparenting may initially be adaptive to manage change when illness enters the family system but has long-term negative consequences for family well-being.

A separate discussion must be made regarding enmeshment. Some parents show a relationship in which the boundaries between parents and children are lost, leading to parental identification with the children’s feelings and needs. Specifically, one interview revealed that a mother feels as ill as her daughter and wants to represent her in doctor visits and treatments. As outlined in the parenting literature, enmeshment is a psychological construct that is conceptually distinct from family cohesion (Barber & Buehler, 1996 ; Manzi et al., 2006 ). According to the relational-symbolic paradigm (Cigoli & Scabini, 2006 ), cohesion is characterized by the provision of support and emotional closeness among family members without compromising their autonomy; in contrast, enmeshment is a family pattern characterized by psychological fusion among family members that inhibits the individuation process and the development of children’s psychosocial maturity. According to this paradigm, enmeshment cannot be conceptualized as extreme cohesion (Manzi et al., 2006 ). For this reason, in our study, enmeshment was defined as a separate category from oversupport, although it is associated with overparenting practices. As described in the literature, enmeshment is generally associated with internalized symptoms and low psychological well-being in children (Barber & Buehler, 1996 ; Yahav, 2002 ). Similarly, for emerging adults with MS, family relationships characterized by enmeshment are likely to have negative consequences not only for autonomous coping but also for a successful separation-individuation process.

As for gender differences in parenting practices, the qualitative nature of our study does not allow us to draw generalizable conclusions. However, in the group of parents interviewed, it appears that it is mainly mothers who engage in overparenting practices, particularly in the forms of infantilization, enmeshment, and vicarious daily life management. Another interesting aspect related to gender differences in parenting is the consistency of parenting practices between mother and father. For example, one of the couples interviewed appeared to follow the same parenting practices based on indulgence, while two couples appeared to differ, with fathers reporting adaptive parenting and mothers reporting overprotection and overcontrol. Differences between parents have been noted in the literature on parenting, although they generally refer to healthy children and adolescents: Yaffe’s ( 2020 ) review showed that mothers tend to be more accepting and supportive, but also more demanding and autonomy-giving than fathers. Their parenting style is generally authoritative, while fathers tend to exhibit more authoritarian parenting patterns. Differences between parents were also found in parenting a child with a health problem (Pelchat et al., 2007 ). Mothers tend to deal with stress through emotional strategies and seeking social support (e.g., from family, friends, and other parents who are in the same situation), while fathers use both cognitive problem-solving strategies and are more action-oriented, although they are less likely to ask for help. All these differences are deeply rooted in differences in the socialization of men and women and are influenced by cultural variables (Pelchat et al., 2007 ). In the current landscape, where most studies focus on parenting ill children and adolescents, our preliminary descriptive findings provide interesting suggestions for developing future research on gender differences in parenting emerging adults with a chronic illness.

Our results should be considered in the light of the Italian socio-cultural context, where the transition to adulthood is often delayed and emerging adult children live with their parents for a long time. In Italy, the family represents the main source of economic support for its members, especially because of the difficulties for young people to find a job and become financially independent (STATISTA, 2023 ). Moreover, intergenerational support is often needed to compensate for the lack of adequate welfare services (Floridi, 2020 ) and the functions of care and support are mainly fulfilled by mothers (Carrà & Marta, 1995 ). As Scabini et al. ( 2006 ) pointed out, in Italy the transition to adulthood usually takes place within the family, so there are more difficulties in finding a balance in the relationship between parents and children. Italian family context with emerging adult children is often characterized by an “autonomous relatedness” (Manzi et al., 2012 ): autonomy and independence are encouraged by parents along with the maintenance of strong emotional bonding and relatedness with their offspring (Liga et al., 2017 ). This cultural model may make it difficult for parents to implement balanced support and control and previous studies on Italian healthy emerging adults showed that overparenting is more prevalent than in other countries (Pistella et al., 2022 ). Not only overparenting might reinforce offspring dependence, but also emerging adults’ attitudes might contribute to the maintenance of dependence. Most Italian emerging adults consider the emotional independence from parents as the least important criterion for adulthood (Crocetti & Tagliabue, 2016 ). Parental overinvolvement is often perceived as a form of warmth and they are often over-reliant on their parents for things that they should be doing themselves (Kaniušonytė et al., 2022 ; Smorti et al., 2022 ). Therefore, in families where boundaries between closeness and dependence are already blurred, the diagnosis of a chronic illness, like MS, can lead to even more complex family dynamics. Specifically, the disease often forces the family system to exert excessive control, which is often facilitated by cohabitation, and to provide excessive support that is not always motivated by an actual need of the young person. Emerging adults are also at risk of relying too heavily on parents for disease-related aspects that they could handle on their own. Dependence dynamics are thus reinforced and the transition to adulthood is made more difficult.

The study has some limitations. Because this was a qualitative and exploratory study with parent volunteers, a participant selection effect must be considered. Moreover, the qualitative approach purposefully used in this study limits the generalizability of our results, as in any qualitative studies. Moving from suggestion of the present study, future quantitative research should examine gender differences (both on the parents’ and children’s side), as well as inconsistencies in maternal and paternal parenting practices for the effects they may have on family dynamics. Finally, we have only examined the parents’ perspective. It would be useful to interview both parents and children to compare their views of parenting practices. Examining the views of both parents and children would provide practical guidance to help both cope with the illness diagnosis and make the family relationship adaptive.

To the best of our knowledge, this is the first study to examine parental behavior toward emerging adults diagnosed with MS. Our findings contribute to the still scarce literature on parenting emerging adults with chronic illnesses with a deep insight into parental perceptions and have clinical implications for health professionals.

Some findings of the study can be extended to the parenting of emerging adults with other chronic illnesses. Relational dynamics between parents and sick emerging adult children, and in particular the delicate balance between autonomy and dependence, are key aspects to be considered in any chronic illness situation. Specifically, excessive parental concern, oversupport and overinvolvement can be elements that undermine the adaptation of the entire family system to the offspring’ chronic illness. Clinical centers should implement comprehensive psychological support for patients and the relationship systems (e.g., families) in which they are embedded, especially during the initial period after diagnosis of a chronic illness (Rintell & Melito, 2013 ). Parents should be given information about the disease to better manage their anxiety, which is often the initial cause of overparenting practices. Parents who are better able to control their emotions should also feel more able to establish appropriate relationships with their children by balancing support and control. In particular, parenting practices should promote the development of an equal relationship in which emerging adult children are treated as adults and their need for independence and autonomy is recognized.

Other results of our study should be considered with reference to specific aspects of MS that are the sudden diagnosis in emerging adulthood, the unpredictability of the disease course, and the need for medical therapies tailored to the patient. Parents may revert to dynamics typical of when their child was younger and demonstrate intrusiveness in the management not only of daily life, but also of medical treatments. Therefore, parents should be helped to support their children while encouraging their autonomous management of daily life and illness-related difficulties and therapies (Blundell Jones et al., 2017 ; Bonino et al., 2018 ). A very sensitive issue for parents is respecting children’s choices, especially regarding talking about the disease with them and outside the family (e.g., with friends, colleagues, employer). Parents should be helped to separate their own emotional experiences from those of their child, trying to take their child’s point of view but not putting themselves in their child’s place; in this way, they can help them and promote their autonomy. The implementation of adaptive parenting is not a goal that is achieved forever but requires constant changes that go hand in hand with the continuous process of adaptation to MS for both patients and their families.

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Graziano, F., Borghi, M., Bonino, S. et al. Parenting Emerging Adults with Multiple Sclerosis: A Qualitative Analysis of the Parents’ Perspective. J Child Fam Stud (2024). https://doi.org/10.1007/s10826-024-02845-8

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