qualitative research analysis types and software

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.

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

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

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

• Supported Methods
• Decision Factors
• Compare QDA Software

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

Qualitative techniques supported by  QDAS

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

QDAS  supported methodologies

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

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

Decision factors for your research

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

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

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

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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.

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10 Best Qualitative Data Analysis Software of 2024

Choosing the right qualitative data analysis software can be overwhelming, especially with so many choices available. Whether you’re a researcher, business leader, or marketer, analyzing qualitative data is essential for understanding your audience and making informed decisions.

From customer feedback to market research, qualitative insights help you understand people’s thoughts, feelings, and behaviors. However, without the right tools, analyzing all that data can be challenging.

In this blog, we’ll explore the 10 best software options available today. We’ll highlight their key features and benefits to help you find the one that best suits your needs. Let’s see how the right software can help you gain valuable insights and make smarter decisions!

What is qualitative data analysis software?

Qualitative data analysis software helps researchers, analysts, and professionals manage, organize, and analyze non-numerical or qualitative data. Qualitative data refers to:

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

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

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

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

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

Why should you use qualitative data analysis software?

Using qualitative data analysis software is important for researchers and professionals who want to analyze and gain insights from qualitative data efficiently. Here are five reasons to consider using these tools:

1. To simplify data analysis

Qualitative data analysis tools make analyzing large amounts of text much easier. They help you organize and process data quickly, saving time and effort.

2. To uncover deeper insights

These tools allow you to find hidden patterns, emotions, and themes in your data, which can help you better understand your qualitative research project or business issue.

3. To use both qualitative and quantitative data

Many of these tools support qualitative and mixed methods research, meaning you can combine both qualitative and quantitative data . This gives you a broader perspective and more well-rounded results.

4. To perform statistical analysis

Some tools include features for statistical analysis, which lets you apply numbers to your qualitative research. This can help verify your findings and add credibility to your conclusions.

5. To collaborate and report more easily

These tools make it easier to work with others, share insights, and generate detailed reports, helping you present your results clearly and professionally.

Using these tools can help you analyze your data more deeply, make smarter decisions, and communicate your findings effectively.

Best 10 qualitative data analysis software

Qualitative data analysis software is great for businesses wanting to understand their customers better. It gives detailed insights into what customers think, prefer, and how they behave. Let’s explore the top 10 software options for qualitative data analysis .

01. QuestionPro

QuestionPro is great for analyzing customer feedback. This qualitative data analysis software is easy to use and helps businesses better understand their customers by gathering data through surveys, reviews, and more. Its features for analyzing qualitative data also make it a great choice for businesses wanting detailed customer insights .

Key Features:

• Data visualization and analysis
• Text analysis
• Sentiment analysis
• Word cloud generation
• Feedback analysis
• Integration with 3rd party tools
• QuestionPro is great for creating customizable surveys and questionnaires .
• Real-time sentiment analysis to get customer sentiments.
• The automated sentiment analysis categorizes responses as positive, negative, or neutral.
• Free license never expires. Upgrade anytime.
• Pricing for premium features and packages starts from $99/month.

MAXQDA is a versatile tool designed to handle all types of data, like interviews, surveys, videos, and social media content.

Best Features:

• Import text, audio, and video files
• Advanced coding and tagging
• Mixed methods analysis support
• Visual exploration of data
• Easily handle different data formats.
• Combine qualitative and quantitative data.
• Supports various coding methods.
• Free version is limited; full features require a license.
• Premium plan starts from $45/month.

03. Quirkos

Quirkos focuses on comparing data side by side, helping users identify trends and customer behavior .

• Comparative analysis
• Drag-and-drop coding
• Real-time data visualization
• Theme color customization
• Integration with SPSS, Word, Excel
• Works on different operating systems.
• Easy drag-and-drop and color-coding features improve user experience.
• Color-coded themes enable quick identification and data segmentation analysis .
• Lacks some advanced features found in other tools.
• Does not have advanced AI-driven features for automated sentiment analysis or survey data insights.
• Premium plan starts from $5/month.

04. Raven’s Eye

Raven’s Eye is ideal for analyzing natural language data. Its standout feature is the ability to convert audio to textual data, making it perfect for studying customer interviews .

• Natural language audio and text analysis
• Real-time data processing
• Audio transcription
• Converts audio to text for easy analysis, particularly useful for interview data.
• Supports multiple languages.
• Helps analyze both text and spoken words.
• Accuracy depends on audio quality.
• Premium plan starts from $35/month.

05. Square Feedback

Square Feedback is a free tool for collecting customer feedback, with added features for qualitative analysis.

• Integrates with digital receipts
• Historical data comparison
• Simplifies customer feedback collection.
• Tracks and analyzes comments.
• Visualizes insights in charts and graphs.
• Limited advanced analysis features.
• Available upon request.

LiGRE caters to students, researchers, and professionals. It’s great for analyzing interviews and large text datasets.

• Automatic transcription of audio and video
• Survey building and data merging
• Multimedia data support
• Collaboration via LiGRE laboratory
• Saves time by transcribing audio and video.
• Easy survey creation for data collection.
• A collaborative platform for teams to work together on qualitative research projects.
• Requires some time to learn and adapt features and functionalities.
• Limitations with extensive data sources management.
• Requires compatible hardware and software for efficient usage.

07. QDA Miner Lite

QDA Miner Lite is simple to use for analyzing interviews, open-ended responses, and other qualitative data. This tool is particularly advantageous for researchers seeking to uncover intricate patterns and insights within qualitative data.

• Easy data coding and retrieval
• Visual presentation of results
• Mixed methods analysis
• Supports various data formats.
• Helps retrieve specific text for analysis.
• New users may require time to master.

08. Dedoose

Dedoose is a comprehensive software for qualitative and mixed-method research. It can analyze text, audio, images, videos, and surveys.

• Mixed-method analysis
• Interactive data visualization
• Advanced analytics tools
• Combines qualitative and quantitative research .
• Visually engaging presentations.
• Supports coding and analysis of various types of media and data.
• New users may need time to adjust.
• Limitations on data export formats or options.
• Premium plan starts from $13/month.

09. Glimpse

Glimpse is a great option for customer success teams. It provides insights into customer behavior through sentiment analysis.

• Cross-platform data collection
• Real-time data collection and analysis
• Understands customer emotions and sentiments.
• Collects data from multiple platforms.
• Usages machine learning methods to analyze qualitative data.
• Machine learning features can be complex.
• Premium plan starts from $458/month.

10. HubSpot

HubSpot’s customer feedback tool offers qualitative data analysis, especially from surveys and reviews.

• Customer feedback analysis
• NPS surveys integration
• Integrates NPS surveys to simplify customer feedback collection.
• Provides visual representations of customer insights for better understanding.
• Collects and analyzes both quantitative and qualitative customer feedback .
• Lacks advanced analysis features compared to specialized tools.
• Premium plan starts from $15/month.

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

Why is QuestionPro the best qualitative data analysis software?

When it comes to analyzing qualitative data, especially from surveys or mixed-method research, QuestionPro stands as one of the top tools available. Here’s why:

1. Easy-to-understand data visualization and analysis

One of QuestionPro’s key strengths is its ability to turn complex data into simple visuals. Users can create detailed graphs, charts, and dashboards that make it easier to understand patterns and trends at a glance. This is particularly useful for those working with large datasets and need quick insights.

2. Advanced text analysis tools

QuestionPro is excellent at analyzing z, making it valuable for processing open-ended survey responses or interview transcripts.

• It identifies themes in textual data.
• Extracts important keywords.
• Provides deeper insights into customer feedback.

3. Sentiment analysis for emotional insights

QuestionPro goes beyond words with its sentiment analysis feature. It helps users evaluate the emotional tone in textual data, showing how people feel about topics or products. You can:

• Track sentiment in real-time.
• Improve customer satisfaction metrics .

4. Word cloud for quick theme identification

QuestionPro offers a useful word cloud feature that shows the most common words in your data. This helps researchers quickly identify important themes and ideas.

This qualitative data analysis tool generates word clouds, which highlight frequently mentioned words, making it easier to see recurring topics or concerns. It’s a fast, visual way to recognize key themes without going through a lot of text manually.

The word cloud simplifies theme analysis, which allows you to quickly understand the main focus of your data without spending too much time on detailed coding.

5. Feedback analysis for open-ended responses

Open-ended responses can offer valuable insights, but they can be hard to analyze because they aren’t structured. QuestionPro helps by turning this kind of feedback into useful data.

Whether respondents share detailed suggestions or personal stories, QuestionPro can break down these responses to find key ideas to help improve business performance. By focusing on open-ended feedback, businesses can better understand what their customers think, what they like or dislike, and where improvements can be made.

These insights can help companies boost customer satisfaction, adjust their services, and make sure they’re meeting customer needs. This qualitative data analysis tool ensures researchers get the most out of their qualitative data, without missing any important details.

6. Image analysis for visual feedback

Beyond just text, QuestionPro also handles visual content such as images. This is especially helpful for industries that depend on visual feedback or multimedia.

For researchers working with image-based feedback, QuestionPro’s image analysis tool helps extract valuable insights from visual content, including customer photos and marketing images.

By combining image analysis with text, researchers will get a more detailed and comprehensive view of customer feedback. This makes it a great tool for any research involving multimedia data.

7. Seamless integrations for enhanced workflow

QuestionPro works smoothly with popular tools like Excel, Google Analytics, and social media platforms. This makes it easier to export and analyze data from different sources.

This integration helps teams collaborate better and keeps all the important data in one place, making research more efficient. By connecting with other tools, researchers can automate certain parts of the qualitative analysis process. This will save time for more in-depth insights and strategic planning.

QuestionPro’s qualitative data analysis software is one of the best solutions for researchers who are looking to gain deeper insights from their data. Its powerful visualization, qualitative text analysis, sentiment analysis, and feedback tools make it the ideal platform for mixed methods research.

If you’re looking for a powerful, user-friendly tool for qualitative analysis, QuestionPro is a top choice. Reach out to schedule a demo or learn more about how QuestionPro can transform your research process!

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Frequently Asked Questions (FAQs)

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

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

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

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Are there free qualitative data analysis tutorials?

What are the core functions common to most qda programs.

The main role of qualitative analysis tools is to help researchers analyze non-numerical data. The core functions of QDAs typically include:

• Import a range of data forms such as text, audio, video, and images.
• Organize data into manageable and intuitive groups such as by cases or interview transcripts.
• Assign codes to text data, video, audio, images, and more.
• Develop coding systems to group similar topics, ideas, or sentiment.
• Write notes and memos in data to help with future referencing and reflection.
• Create text documents from audio or video formats to facilitate the data-analysis process.
• Generate visual representations of data such as tables, word clouds, charts, and graphs.
• Identify reoccurring themes that appear throughout the data.
• Make connections using clustering and thematic analysis tools to uncover insights.
• Summarize findings from built-in reporting functions.
• Share coding, reports, and writing with other research team members on the same research project file.

What research methodologies are supported by qualitative analysis tools?

• Thematic Analysis
• Grounded Theory
• Ethnography
• Phenomenology
• Case Studies
• Discourse Analysis
• Narrative Analysis
• Mixed Methods Research
• Content Analysis

What factors should I consider when choosing QDA software?

When sorting through qualitative analysis tools for your research project, there are a few important questions to ask:

• How many features does the software include that support your methodology?
• What is the total cost of the software, and are there any student/academic discounts?
• Is the software easy to learn or intuitive, and is there a library of tutorials and training documentation?
• Does the product offer quality support for quick assistance?
• Can the tool produce the types of visualizations needed to communicate results?
• How does the software handle data security and privacy?
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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
• Introduction

What is qualitative data analysis?

Qualitative data analysis methods, how do you analyze qualitative data, 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
• Qualitative data analysis software

Qualitative data analysis

Analyzing qualitative data is the next step after you have completed the use of qualitative data collection methods . The qualitative analysis process aims to identify themes and patterns that emerge across the data.

In simplified terms, qualitative research methods involve non-numerical data collection followed by an explanation based on the attributes of the data . For example, if you are asked to explain in qualitative terms a thermal image displayed in multiple colors, then you would explain the color differences rather than the heat's numerical value. If you have a large amount of data (e.g., of group discussions or observations of real-life situations), the next step is to transcribe and prepare the raw data for subsequent analysis.

Researchers can conduct studies fully based on qualitative methodology, or researchers can preface a quantitative research study with a qualitative study to identify issues that were not originally envisioned but are important to the study. Quantitative researchers may also collect and analyze qualitative data following their quantitative analyses to better understand the meanings behind their statistical results.

Conducting qualitative research can especially help build an understanding of how and why certain outcomes were achieved (in addition to what was achieved). For example, qualitative data analysis is often used for policy and program evaluation research since it can answer certain important questions more efficiently and effectively than quantitative approaches.

Qualitative data analysis can also answer important questions about the relevance, unintended effects, and impact of programs, such as:

• Were expectations reasonable?
• Did processes operate as expected?
• Were key players able to carry out their duties?
• Were there any unintended effects of the program?

The importance of qualitative data analysis

Qualitative approaches have the advantage of allowing for more diversity in responses and the capacity to adapt to new developments or issues during the research process itself. While qualitative data analysis can be demanding and time-consuming to conduct, many fields of research utilize qualitative software tools that have been specifically developed to provide more succinct, cost-efficient, and timely results.

Qualitative data analysis is an important part of research and building greater understanding across fields for a number of reasons. First, cases for qualitative data analysis can be selected purposefully according to whether they typify certain characteristics or contextual locations. In other words, qualitative data permits deep immersion into a topic, phenomenon, or area of interest. Rather than seeking generalizability to the population the sample of participants represent, qualitative research aims to construct an in-depth and nuanced understanding of the research topic.

Secondly, the role or position of the researcher in qualitative data analysis is given greater critical attention. This is because, in qualitative data analysis, the possibility of the researcher taking a ‘neutral' or transcendent position is seen as more problematic in practical and/or philosophical terms. Hence, qualitative researchers are often exhorted to reflect on their role in the research process and make this clear in the analysis.

Thirdly, while qualitative data analysis can take a wide variety of forms, it largely differs from quantitative research in the focus on language, signs, experiences, and meaning. In addition, qualitative approaches to analysis are often holistic and contextual rather than analyzing the data in a piecemeal fashion or removing the data from its context. Qualitative approaches thus allow researchers to explore inquiries from directions that could not be accessed with only numerical quantitative data.

Establishing research rigor

Systematic and transparent approaches to the analysis of qualitative data are essential for rigor . For example, many qualitative research methods require researchers to carefully code data and discern and document themes in a consistent and credible way.

Perhaps the most traditional division in the way qualitative and quantitative research have been used in the social sciences is for qualitative methods to be used for exploratory purposes (e.g., to generate new theory or propositions) or to explain puzzling quantitative results, while quantitative methods are used to test hypotheses .

After you’ve collected relevant data , what is the best way to look at your data ? As always, it will depend on your research question . For instance, if you employed an observational research method to learn about a group’s shared practices, an ethnographic approach could be appropriate to explain the various dimensions of culture. If you collected textual data to understand how people talk about something, then a discourse analysis approach might help you generate key insights about language and communication.

The qualitative data coding process involves iterative categorization and recategorization, ensuring the evolution of the analysis to best represent the data. The procedure typically concludes with the interpretation of patterns and trends identified through the coding process.

To start off, let’s look at two broad approaches to data analysis.

Deductive analysis

Deductive analysis is guided by pre-existing theories or ideas. It starts with a theoretical framework , which is then used to code the data. The researcher can thus use this theoretical framework to interpret their data and answer their research question .

The key steps include coding the data based on the predetermined concepts or categories and using the theory to guide the interpretation of patterns among the codings. Deductive analysis is particularly useful when researchers aim to verify or extend an existing theory within a new context.

Inductive analysis

Inductive analysis involves the generation of new theories or ideas based on the data. The process starts without any preconceived theories or codes, and patterns, themes, and categories emerge out of the data.

The researcher codes the data to capture any concepts or patterns that seem interesting or important to the research question . These codes are then compared and linked, leading to the formation of broader categories or themes. The main goal of inductive analysis is to allow the data to 'speak for itself' rather than imposing pre-existing expectations or ideas onto the data.

Deductive and inductive approaches can be seen as sitting on opposite poles, and all research falls somewhere within that spectrum. Most often, qualitative data analysis approaches blend both deductive and inductive elements to contribute to the existing conversation around a topic while remaining open to potential unexpected findings. To help you make informed decisions about which qualitative data analysis approach fits with your research objectives, let's look at some of the common approaches for qualitative data analysis.

Content analysis is a research method used to identify patterns and themes within qualitative data. This approach involves systematically coding and categorizing specific aspects of the content in the data to uncover trends and patterns. An often important part of content analysis is quantifying frequencies and patterns of words or characteristics present in the data .

It is a highly flexible technique that can be adapted to various data types , including text, images, and audiovisual content . While content analysis can be exploratory in nature, it is also common to use pre-established theories and follow a more deductive approach to categorizing and quantifying the qualitative data.

Thematic analysis is a method used to identify, analyze, and report patterns or themes within the data. This approach moves beyond counting explicit words or phrases and focuses on also identifying implicit concepts and themes within the data.

Researchers conduct detailed coding of the data to ascertain repeated themes or patterns of meaning. Codes can be categorized into themes, and the researcher can analyze how the themes relate to one another. Thematic analysis is flexible in terms of the research framework, allowing for both inductive (data-driven) and deductive (theory-driven) approaches. The outcome is a rich, detailed, and complex account of the data.

Grounded theory is a systematic qualitative research methodology that is used to inductively generate theory that is 'grounded' in the data itself. Analysis takes place simultaneously with data collection , and researchers iterate between data collection and analysis until a comprehensive theory is developed.

Grounded theory is characterized by simultaneous data collection and analysis, the development of theoretical codes from the data, purposeful sampling of participants, and the constant comparison of data with emerging categories and concepts. The ultimate goal is to create a theoretical explanation that fits the data and answers the research question .

Discourse analysis is a qualitative research approach that emphasizes the role of language in social contexts. It involves examining communication and language use beyond the level of the sentence, considering larger units of language such as texts or conversations.

Discourse analysts typically investigate how social meanings and understandings are constructed in different contexts, emphasizing the connection between language and power. It can be applied to texts of all kinds, including interviews , documents, case studies , and social media posts.

Phenomenological research focuses on exploring how human beings make sense of an experience and delves into the essence of this experience. It strives to understand people's perceptions, perspectives, and understandings of a particular situation or phenomenon.

It involves in-depth engagement with participants, often through interviews or conversations, to explore their lived experiences. The goal is to derive detailed descriptions of the essence of the experience and to interpret what insights or implications this may bear on our understanding of this phenomenon.

Whatever your data analysis approach, start with ATLAS.ti

Qualitative data analysis done quickly and intuitively with ATLAS.ti. Download a free trial today.

Now that we've summarized the major approaches to data analysis, let's look at the broader process of research and data analysis. Suppose you need to do some research to find answers to any kind of research question, be it an academic inquiry, business problem, or policy decision. In that case, you need to collect some data. There are many methods of collecting data: you can collect primary data yourself by conducting interviews, focus groups , or a survey , for instance. Another option is to use secondary data sources. These are data previously collected for other projects, historical records, reports, statistics – basically everything that exists already and can be relevant to your research.

The data you collect should always be a good fit for your research question . For example, if you are interested in how many people in your target population like your brand compared to others, it is no use to conduct interviews or a few focus groups . The sample will be too small to get a representative picture of the population. If your questions are about "how many….", "what is the spread…" etc., you need to conduct quantitative research . If you are interested in why people like different brands, their motives, and their experiences, then conducting qualitative research can provide you with the answers you are looking for.

Let's describe the important steps involved in conducting research.

Step 1: Planning the research

As the saying goes: "Garbage in, garbage out." Suppose you find out after you have collected data that

• you talked to the wrong people
• asked the wrong questions
• a couple of focus groups sessions would have yielded better results because of the group interaction, or
• a survey including a few open-ended questions sent to a larger group of people would have been sufficient and required less effort.

Think thoroughly about sampling, the questions you will be asking, and in which form. If you conduct a focus group or an interview, you are the research instrument, and your data collection will only be as good as you are. If you have never done it before, seek some training and practice. If you have other people do it, make sure they have the skills.

Step 2: Preparing the data

When you conduct focus groups or interviews, think about how to transcribe them. Do you want to run them online or offline? If online, check out which tools can serve your needs, both in terms of functionality and cost. For any audio or video recordings , you can consider using automatic transcription software or services. Automatically generated transcripts can save you time and money, but they still need to be checked. If you don't do this yourself, make sure that you instruct the person doing it on how to prepare the data.

• How should the final transcript be formatted for later analysis?
• Which names and locations should be anonymized?
• What kind of speaker IDs to use?

What about survey data ? Some survey data programs will immediately provide basic descriptive-level analysis of the responses. ATLAS.ti will support you with the analysis of the open-ended questions. For this, you need to export your data as an Excel file. ATLAS.ti's survey import wizard will guide you through the process.

Other kinds of data such as images, videos, audio recordings, text, and more can be imported to ATLAS.ti. You can organize all your data into groups and write comments on each source of data to maintain a systematic organization and documentation of your data.

Step 3: Exploratory data analysis

You can run a few simple exploratory analyses to get to know your data. For instance, you can create a word list or word cloud of all your text data or compare and contrast the words in different documents. You can also let ATLAS.ti find relevant concepts for you. There are many tools available that can automatically code your text data, so you can also use these codings to explore your data and refine your coding.

For instance, you can get a feeling for the sentiments expressed in the data. Who is more optimistic, pessimistic, or neutral in their responses? ATLAS.ti can auto-code the positive, negative, and neutral sentiments in your data. Naturally, you can also simply browse through your data and highlight relevant segments that catch your attention or attach codes to begin condensing the data.

Step 4: Build a code system

Whether you start with auto-coding or manual coding, after having generated some first codes, you need to get some order in your code system to develop a cohesive understanding. You can build your code system by sorting codes into groups and creating categories and subcodes. As this process requires reading and re-reading your data, you will become very familiar with your data. Counting on a tool like ATLAS.ti qualitative data analysis software will support you in the process and make it easier to review your data, modify codings if necessary, change code labels, and write operational definitions to explain what each code means.

Step 5: Query your coded data and write up the analysis

Once you have coded your data, it is time to take the analysis a step further. When using software for qualitative data analysis , it is easy to compare and contrast subsets in your data, such as groups of participants or sets of themes.

For instance, you can query the various opinions of female vs. male respondents. Is there a difference between consumers from rural or urban areas or among different age groups or educational levels? Which codes occur together throughout the data set? Are there relationships between various concepts, and if so, why?

Step 6: Data visualization

Data visualization brings your data to life. It is a powerful way of seeing patterns and relationships in your data. For instance, diagrams allow you to see how your codes are distributed across documents or specific subpopulations in your data.

Exploring coded data on a canvas, moving around code labels in a virtual space, linking codes and other elements of your data set, and thinking about how they are related and why – all of these will advance your analysis and spur further insights. Visuals are also great for communicating results to others.

Step 7: Data presentation

The final step is to summarize the analysis in a written report . You can now put together the memos you have written about the various topics, select some salient quotes that illustrate your writing, and add visuals such as tables and diagrams. If you follow the steps above, you will already have all the building blocks, and you just have to put them together in a report or presentation.

When preparing a report or a presentation, keep your audience in mind. Does your audience better understand numbers than long sections of detailed interpretations? If so, add more tables, charts, and short supportive data quotes to your report or presentation. If your audience loves a good interpretation, add your full-length memos and walk your audience through your conceptual networks and illustrative data quotes.

Qualitative data analysis begins with ATLAS.ti

For tools that can make the most out of your data, check out ATLAS.ti with a free trial.

Qualitative Data Analysis Software | MAXQDA

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Unlock the full potential of your qualitative research with the leading Qualitative Data Analysis Software

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

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

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

Developed by and for researchers – since 1989

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

Sally S. Cohen – NYU Rory Meyers College of Nursing

Qualitative Data Analysis is Faster and Smarter with MAXQDA

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

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

User-Friendly Tools for Qualitative Coding

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

Organize Your Qualitative Data with MAXQDA’s Memo Tools

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

Text Search and Autocoding Tools

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

Visual Text Exploration

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

A lot more than just a Qualitative Data Analysis Software

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

Visualize your qualitative data

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

AI Assist: Qualitative data analysis software meets AI

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

Free tutorials and guides on qualitative data analysis software

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

Free MAXQDA Trial for Windows and Mac

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Qualitative Research: Analysis Types & Tools

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First published in 1990. There was a time when most researchers believed that the only phenomena that counted in the social sciences were those that could be measured. To make that perfectly clear, they called any phenomenon they intended to study a 'variable', indicating that the phenomenon could vary in size, length, amount, or any other quantity. Unfortunately, not many phenomena in the human world comes naturally in quantities. If we cannot even give a useful answer to what qualitative analysis is and how it works, then it seems rather incongruent to try and involve a computer, the very essence of precision and orderliness. Isn't qualitative analysis a much too individualistic and flexible an activity to be supported by a computer? Won't a computer do exactly what qualitative researchers want to avoid, namely standardize the process? Won't it mechanize and rigidify qualitative analysis? The answer to these questions is NO, and this book explains why.

TABLE OF CONTENTS

Chapter 1 | 4  pages, what this book is about, chapter 2 | 3  pages, how to read this book, chapter 3 | 11  pages, history of qualitative research, chapter 4 | 12  pages, qualitative research in sociology, chapter 5 | 10  pages, qualitative research in psychology, chapter 6 | 11  pages, qualitative research in education, chapter 7 | 21  pages, types of qualitative research, chapter 8 | 26  pages, types of qualitative analysis, chapter 9 | 9  pages, the mechanics of structural qualitative analysis, chapter 10 | 22  pages, the mechanics of interpretations qualitative analysis, chapter 11 | 12  pages, organizing systems and how to develop them, chapter 12 | 20  pages, qualitative analysis programs (ms-dos), chapter 13 | 9  pages, how to deal with a computer, chapter 14 | 3  pages, how to read the descriptions of text analysis programs, chapter 15 | 17  pages, text retrievers, chapter 16 | 12  pages, data base managers, chapter 17 | 12  pages, chapter 18 | 13  pages, text analysis package (tap), chapter 19 | 14  pages, chapter 20 | 19  pages, the ethnograph, chapter 21 | 16  pages, textbase alpha, chapter 22 | 12  pages, chapter | 7  pages.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to use and assess qualitative research methods

Loraine busetto.

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

Wolfgang Wick

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

Christoph Gumbinger

Associated data.

Not applicable.

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

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

How to conduct qualitative research?

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

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

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

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

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

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

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

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

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

no external funding.

Availability of data and materials

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

The authors declare no competing interests.

Publisher’s Note

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8 best software for qualitative data analysis.

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In today's fast-paced research environment, qualitative data analysis has emerged as a critical component in deriving meaningful insights. Researchers and analysts often face the challenge of sifting through vast amounts of qualitative data to uncover patterns and themes that drive informed decisions. This section will introduce you to the top qualitative tools designed to simplify this complex process and enhance your analytical capabilities.

As you navigate through this exploration, you'll discover software solutions that cater to various analytical needs, from academic research to market analysis. These tools are user-friendly, enabling both seasoned researchers and novices alike to efficiently extract valuable insights from interviews, surveys, and focus group discussions. By understanding the features and benefits of these top qualitative tools, you'll be better equipped to make informed choices that align with your research goals.

Importance of Qualitative Data Analysis Tools

Qualitative data analysis tools hold significant importance in today's research landscape, especially when understanding complex human behaviors and emotions. The top qualitative tools enable researchers to uncover deeper insights from raw data, allowing for a more nuanced interpretation of findings. By utilizing these tools, teams can streamline their workflows, improving overall productivity in managing text, audio, and video data.

Furthermore, qualitative analysis software significantly reduces the manual workload associated with transcription and analysis. Researchers often find themselves overwhelmed with data, making it challenging to maintain high quality in their outputs. Automated features within these tools not only enhance accuracy but also help mitigate potential biases. By adopting the best software available, teams can focus more on strategic interpretation rather than being bogged down in the details of data management. This shift not only drives efficiency but fosters innovation in research practices.

Criteria for Selecting the Best Software

Choosing the best software for qualitative data analysis requires thoughtful consideration of several key criteria. First, examine the capabilities for extracting insights from your data. It’s essential to evaluate how effectively the software can analyze verbatim data, providing meaningful interpretations. Look for tools that can transform raw data into visual reports or theme summaries, as this can greatly enhance your understanding of the collected insights.

Another critical aspect is the software's integration capabilities. The ideal tool should seamlessly fit into your existing systems, reducing manual work and improving workflow efficiency. Consider how well the software interacts with other platforms to facilitate data sharing without extensive additional steps. Ultimately, you want a user-friendly interface that fosters collaboration and encourages the adoption of these top qualitative tools within your team. By focusing on functionality and integration, you can make a more informed decision that aligns with your research needs.

In-Depth Look at Eight Top Qualitative Tools

Understanding the top qualitative tools available can significantly enhance your research capabilities. When exploring the eight leading software options, each one offers unique features designed to simplify the qualitative data analysis process. These tools can help streamline your workflow, allowing you to focus on extracting meaningful insights from complex data sets.

For instance, some tools prioritize user-friendly interfaces, making it easier for beginners to navigate without extensive training. Others may specialize in advanced analytical functionalities that cater to experienced researchers. Consider attributes such as collaboration features, data visualization capabilities, and compatibility with various data types. Each option in this selection presents distinct strengths, guiding you toward selecting the best fit for your specific qualitative research needs. By delving deeper into these top qualitative tools, you can discover which solutions will ultimately support and elevate your analysis efforts.

Comprehensive Review of Leading Software

A comprehensive review of leading software for qualitative data analysis evaluates the top qualitative tools available in the market today. These software options cater to researchers seeking to streamline their workflows by automating data transcription, analysis, and reporting. By addressing common challenges, such as the time-consuming nature of manual processes, these tools enhance the efficiency and quality of insights derived from qualitative data.

To enhance your understanding, we can categorize the top qualitative tools based on essential attributes. First, consider user-friendly interfaces, as they significantly simplify the research process. Second, some tools incorporate advanced AI capabilities that aid in reducing bias and improving accuracy in data interpretation. Lastly, security features play an important role, ensuring that sensitive information remains protected during analysis. By selecting the right software, researchers can transform their qualitative data analysis into a more efficient and reliable endeavor.

NVivo: A Versatile Research Assistant

NVivo serves as a powerful tool for managing qualitative research, addressing many challenges faced by researchers. This versatile research assistant simplifies the process of collecting, analyzing, and interpreting qualitative data. By offering features such as data visualization, coding, and collaboration tools, NVivo enables teams to work more efficiently.

With NVivo, researchers can import diverse data formats, including text, audio, and video, facilitating a comprehensive analysis. The software supports coding, allowing users to identify patterns and themes within data. This organized approach not only enhances insights but also improves the reporting process. Ultimately, NVivo stands out in the realm of top qualitative tools, making it an invaluable asset for those engaged in qualitative analysis, whether they are seasoned researchers or new to the field.

ATLAS.ti: Power and Flexibility

When exploring top qualitative tools, one stands out for its impressive versatility and power. Users can create comprehensive datasets that simplify complex data into manageable insights. This tool enables researchers to formulate tailored questions, summarize findings, and draw actionable conclusions from their data. The capability to perform multi-project search queries adds another layer, allowing for in-depth analysis across various datasets.

Moreover, the tool's visual capabilities enhance the user experience significantly. For instance, users can generate journey maps to visualize client processes and identify areas for improvement. By utilizing sector-specific templates, it caters to various industries, ensuring that researchers can address unique challenges effectively. Overall, this combination of advanced functionalities and visual tools makes it an exceptional option for qualitative data analysis, suitable for researchers seeking power and flexibility.

Specialized Qualitative Tools for Niche Needs

Qualitative research often requires specialized tools that cater to specific needs. These tools can enhance the depth and quality of data analysis, leading researchers to more nuanced insights. Understanding the goals and methodologies of your project is vital in selecting the right software for qualitative data analysis.

Several factors can guide your choice of tools. First, consider the type of data you will analyze, such as interviews, focus groups, or surveys. Next, evaluate the software's features, such as coding capabilities or visual data representation. Additionally, think about user-friendliness and customer support, especially if your team members are less experienced. These considerations will help ensure you select top qualitative tools tailored to your project’s unique demands.

MAXQDA: Integration and Innovation

In the realm of qualitative data analysis, integration and innovation play pivotal roles in enhancing research productivity. MAXQDA excels in providing researchers with comprehensive tools that streamline data handling and foster collaboration among teams. Its support for various data types—such as text, audio, and video—embodies an integrated approach to qualitative analysis. This flexibility allows researchers to derive meaningful insights more efficiently than traditional methods.

Moreover, the software incorporates innovative features that reduce manual labor in data transcription and analysis. By automating these aspects, teams can focus on interpreting findings rather than being bogged down by time-consuming processes. This emphasis on efficiency not only enhances productivity but also minimizes bias, leading to higher-quality outputs. As one of the top qualitative tools available, the software redefines how researchers conduct studies, ensuring that critical insights emerge from their rigorous efforts.

Dedoose: Mixed Methods Made Easy

Dedoose offers an intuitive platform designed for both novice and experienced researchers conducting mixed methods research. Its user-friendly interface allows users to efficiently manage and analyze qualitative and quantitative data without extensive training. This accessibility makes it a preferred choice among various top qualitative tools available today.

Users can easily upload data, transcribe interviews, and categorize findings seamlessly. The tool supports an assortment of media types, enabling researchers to handle diverse sources of information effectively. In addition, its robust visualization options allow users to create compelling reports that highlight key insights. By simplifying complex data analysis, Dedoose proves to be a valuable asset for anyone seeking to integrate multiple data strands into their research, solidifying its place among the best options for qualitative data analysis.

Key Features and Benefits of Top Qualitative Tools

When selecting top qualitative tools, it’s crucial to recognize their key features and benefits. Ease of use is often emphasized as a standout characteristic, enabling both novice and experienced researchers to analyze data effectively. These tools typically offer intuitive interfaces, allowing users to navigate complex datasets without extensive training. This democratizes qualitative analysis, making it more accessible to a wider audience.

In addition to user-friendliness, robust data management capabilities are essential. Top qualitative tools often provide functionalities for organizing, coding, and categorizing vast amounts of qualitative data. They enable researchers to uncover patterns and insights effortlessly. Moreover, advanced analytical features help in visualizing results, which can enrich the interpretation process. User collaboration is another important benefit, allowing teams to work together seamlessly. Overall, investing in quality qualitative tools enhances the research workflow and contributes to more reliable insights.

User-Friendly Interfaces and Learning Curves

User-friendly interfaces are crucial for any software, especially for those navigating qualitative data analysis. The best tools prioritize intuitive designs that enhance user experience. With a clear layout and accessible features, users can focus more on data collection and insights rather than struggling with navigation.

Understanding the learning curve associated with these tools is equally important. Top qualitative tools often provide tutorials, help centers, and community forums that make the onboarding process smoother. Users can familiarize themselves with functionalities at their own pace, reducing the anxiety of starting fresh with a new software system. Efficient training resources ensure that teams spend less time learning and more time gaining actionable insights from their data. Ultimately, software that combines user-friendly interfaces with manageable learning curves fosters a productive environment for qualitative research.

Ease of Use in NVivo and ATLAS.ti

NVivo and ATLAS.ti are recognized among the top qualitative tools for their user-friendly interfaces and intuitive designs. Users often find that both software options prioritize accessibility, making them suitable for researchers of all experience levels. The straightforward navigation and centralized functions help streamline the analysis process, allowing users to focus on their data rather than struggling with complicated features.

One of the significant benefits of these tools is their comprehensive online support, including tutorials and community forums, which guide users through various functionalities. Additionally, both software programs allow for easy data importing and categorization, simplifying the process of organizing research material. Ultimately, both NVivo and ATLAS.ti enable researchers to conduct qualitative data analysis efficiently, enhancing productivity and resulting in clearer insights from their findings. This ease of use is a critical consideration for anyone looking to use the top qualitative tools effectively.

Streamlined Workflows in MAXQDA and Dedoose

In qualitative data analysis, streamlined workflows are critical for enhancing productivity and data accuracy. When using specialized software, users can efficiently manage extensive amounts of text, audio, and video data. By automating various tasks, teams can reduce the burden of manual transcription and analysis, allowing more time to focus on extracting insights from their research. This is key for ensuring that analysis remains rigorous and high-quality, minimizing potential biases.

Both platforms incorporate features that promote collaboration and data organization. Additionally, user-friendly interfaces enable researchers to navigate their projects smoothly, facilitating clear communication among team members. Through these streamlined processes, researchers are empowered to utilize their data more effectively. This ultimately positions these tools among the top qualitative tools available, setting the stage for impactful and trustworthy findings in qualitative research.

Advanced Analytical Capabilities

The advanced analytical capabilities found in top qualitative tools provide researchers with the tools necessary for deeper insight extraction. These capabilities enable users to analyze complex qualitative data efficiently, transforming raw information into actionable insights. Advanced software often includes features like text analysis, coding, and pattern recognition, which allow for a more nuanced understanding of data.

High-quality qualitative analysis software can facilitate collaborative workflows and provide real-time data visualization, enhancing team collaboration. Furthermore, tools that incorporate machine learning can identify trends and relationships in data that might not be immediately apparent. This means researchers can uncover hidden insights and improve decision-making processes significantly. Integrating these advanced analytical capabilities ultimately leads to a richer analysis that benefits both research outcomes and strategic objectives.

Data Visualization in Top Qualitative Tools

Data visualization is a crucial element in top qualitative tools, enhancing the analysis of qualitative data significantly. These tools offer various graphic representations, like charts and mind maps, that help users interpret complex information quickly. By visualizing the relationships between themes and insights, researchers can identify patterns that might be missed through textual analysis alone.

One fundamental advantage is the ability to summarize extensive datasets into digestible visuals. This feature allows for the quick extraction of core insights which are easily shared in presentations or reports. Furthermore, these tools often come with interactive components, enabling users to query the data and modify visuals based on their specific needs. Thus, incorporating data visualization in qualitative analysis tools not only simplifies the interpretation but also enriches the decision-making process.

Collaborative Functions and Reporting

Collaborative functions in qualitative data analysis software enhance teamwork by allowing multiple users to engage with the same data simultaneously. This capability fosters a more dynamic interaction, ensuring diverse insights are collected and analyzed effectively. Tools designed with collaboration in mind often provide features such as shared folders, real-time editing, and commenting systems. These allow team members to communicate directly within the workspace, streamlining the feedback process and improving efficiency.

Additionally, robust reporting features are critical for translating qualitative data into understandable formats. Many top qualitative tools enable users to export findings in various formats, such as PDF and CSV files, making it easy to share results. Advanced reporting functionalities can transform data into engaging presentations, allowing teams to visualize key insights effectively. These combined collaborative and reporting capabilities significantly enhance the authenticity and depth of qualitative research findings, ensuring that insights can drive informed decision-making within organizations.

Conclusion: Choosing the Right Tool for Your Research Needs

Selecting the right qualitative data analysis software is essential for achieving meaningful insights. Tools vary significantly in their features, usability, and adaptability to different research needs, making it crucial to evaluate options carefully. Each of the top qualitative tools presents unique strengths, so your choice should align with the specific requirements of your project.

Consider the scale and complexity of your analysis. Some software options may support extensive datasets, while others excel in user-friendly interfaces ideal for novice researchers. Ultimately, your decision should foster a productive analysis environment, empowering you to extract valuable insights effectively. Keeping your objectives clear will ensure that you choose a tool that enhances your research capabilities.

Summary of Top Qualitative Tools

In the realm of qualitative data analysis, selecting the right tools is essential for researchers to derive valuable insights. The top qualitative tools available today offer user-friendly interfaces and advanced capabilities, enabling researchers to analyze data efficiently. Each tool comes with unique features designed to simplify the research process and enhance collaboration.

Key qualitative tools include software that specializes in qualitative coding, visual data mapping, and real-time collaboration. These tools allow teams to work together seamlessly, no matter their location. Additionally, many of them support diverse data formats, making it easy to import interview transcripts and surveys for comprehensive analysis. With the right qualitative analysis software, researchers can unlock the full potential of their data, transforming raw insights into actionable findings.

Final Thoughts and Recommendations for Researchers

When selecting top qualitative tools, researchers should consider a few key aspects to ensure effective data analysis. Firstly, usability is crucial; the software should have an intuitive interface that accommodates all users, regardless of their technical skill levels. Secondly, compatibility with various data types enhances flexibility. The ability to analyze interviews, focus groups, and open-ended survey responses in one platform streamlines the research process.

Furthermore, effective collaboration features can significantly improve teamwork among researchers. Choose tools that enable easy sharing of insights and allow multiple users to contribute simultaneously. Additionally, robust reporting functionalities are essential; tools should facilitate the generation of clear, comprehensive reports that effectively communicate findings. Lastly, consider the availability of support and resources, as comprehensive assistance can greatly enhance the user experience. By focusing on these elements, researchers can make informed decisions when choosing the best software for qualitative data analysis.

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Qualitative research: Analysis types and software tools

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UX Analysis: How To Collect And Analyze UX Data

If you want to create a great user experience, it is essential that you understand how users interact with your product. UX analysis is about examining data collected from users to uncover insights that can lead to a better user experience.

This process identifies what works well and what needs improvement in a product. By focusing on user behavior, you can make informed decisions that result in better products and happier customers.

UX data gives you a deep understanding of how happy users are when they use your product. By analyzing this data, you can pinpoint areas that frustrate them and discover opportunities for improvement.

The Importance of UX Analysis

UX analysis is more crucial than ever. Users interact with a growing number of digital products. The need for intuitive experiences has become paramount.

It is more than just numbers, though; it’s a window into the minds of your users. By analyzing this UX data, you can not only meet the expectations of your users, but even exceed them. This proactive approach to user experience can lead to positive word-of-mouth and, ultimately, a stronger brand reputation.

Understanding UX Research Data

Before diving into UX analysis, it’s important to understand what we’re discussing. 

UX stands for User Experience, which refers to the overall experience that a user has while using a product or service.

Data analysis is the process of examining all the data generated by users as they interact with a product or service. In the case of online products, it includes where they click, how much time they spend on a page, where their eyes travel across the page, what information they’re looking for, and whether they find it.

Typically, UX analysts study this data to draw conclusions. They ask questions like: Is the product helping users achieve their goals? Are users performing the actions we want them to on the product? The answers lie in the data.

The Role of UX Research in Data Collection

How can you go about gathering the necessary data? UX research plays a pivotal role. This research involves various methods, such as usability testing, surveys, and using analytical tools. The data collected through UX research forms the foundation for UX analysis. 

UX research helps in understanding not only what users are doing but also why they are doing it. This deeper understanding is crucial for making the right design and product development decisions.

How to Integrate UX Research with UX Analysis

Integrating UX research with UX analysis ensures that the data you collect is relevant and actionable. This integration involves a continuous cycle of research, data collection, analysis, interpretation, and iteration.

A successful integration of UX research and UX analysis requires a clear understanding of the goals of both processes.

You should conduct UX research to answer specific questions about the behavior of your users, while UX analysis should focus on interpreting the data to give you actionable insights.

This collaborative approach ensures that the data collected is not only useful but also aligned with the objectives of the company.

Let’s go over the types of UX data you can collect to improve your user experience.

Types of UX Data: Quantitative vs. Qualitative

Quantitative data refers to numerical data that can be measured, while qualitative data is more descriptive. Let’s define them further:

• Quantitative Data : This includes metrics like “Task Completion Rates,” measured in percentages, and “Time On Task,” measured in seconds or minutes. Quantitative UX data provides concrete evidence of how users interact with a product.
• Qualitative Data : This data is descriptive and captures the “why” behind users’ behaviors. It offers a deeper understanding of the user experience. For example, for an e-commerce business, quantitative data might show how many users complete a purchase, while qualitative data might reveal why some users abandon their shopping cart.

The Value of Quantitative Data in UX Analysis

Quantitative UX data is invaluable because it helps you understand the measurable aspects of your user experience.

Metrics like Task Completion Rates and Time On Task provide clear indicators of how well a product is performing.

By analyzing these metrics, you can identify specific areas that need improvement, such as simplifying navigation or reducing the time it takes for users to find what they need.

Quantitative data also allows for benchmarking and comparison. By tracking these metrics over time, you can measure the impact of changes to the product and see how your user experience compares to industry standards.

This data-driven approach ensures that decisions are based on objective evidence rather than assumptions.

The Importance of Qualitative Data in UX Research

While quantitative UX data provides the “what,” qualitative data provides the “why.” 

This type of data is crucial for understanding the underlying reasons behind user behavior. Through methods like interviews, focus groups, and open-ended survey questions, UX researchers can uncover insights into the motivations of users, but also their frustrations.

Qualitative data is particularly valuable when trying to improve the emotional and psychological aspects of the user experience. For example, understanding why users feel frustrated with a particular feature can lead to a more empathetic product design.

Balancing Quantitative and Qualitative Data in UX Analysis

To have a balanced approach to UX analysis, consider combining both quantitative and qualitative data. This combination provides a more complete view of the user experience and will help you understand both the measurable outcomes and the reasons behind them.

For example, if quantitative data shows a high drop-off rate at a specific point in the user journey, qualitative data can help explain why users are leaving.

By analyzing these two types of UX data together, you can identify the root cause of the issue and develop targeted solutions to improve the user experience.

Let’s see how you can collect these different types of UX data.

Common Sources of UX Data

UX data can be collected from various sources, including:

• Usability testing : Observing users as they interact with a product.
• Surveys : Asking users specific questions about a product and gathering their feedback.
• Analytics tools : Softwares like Google Analytics generate data on user behaviors on a webpage or product.
• Customer feedback : Collecting insights from reviews and direct feedback.

You can use several of these UX data collection techniques. For instance, you might conduct usability tests to observe users navigating your website, and then use surveys to gather feedback on their experience.

Let’s cover each of these.

Using Usability Testing for In-Depth UX Analysis

Usability testing is a powerful method for collecting UX data because it involves observing real users as they interact with a product.

This method provides first hand insights into what challenges users encounter, and where they succeed. There are different types of usability testing, including moderated and unmoderated sessions (more on that later).

In moderated sessions, a moderator (usually a UX researcher) guides the user through tasks and observes their behavior in real-time.

In unmoderated sessions, users complete tasks independently, with their interactions recorded for later analysis.

Both methods provide valuable UX data that can be used to improve the user experience.

Using Surveys to Collect UX Data

Surveys are another effective method for gathering UX data. They allow you to reach a large number of users and collect feedback on specific aspects of a product.

Surveys can be used to gather both quantitative and qualitative data, making them a versatile tool for UX research.

When designing surveys, it’s important to ask clear, concise questions that will elicit useful responses. Open-ended questions can provide rich qualitative insights, while closed-ended questions can generate quantitative data.

The Role of Analytics Tools in UX Analysis

Analytics tools like Google Analytics are essential for collecting quantitative UX data. They track user behavior on a website or app, and provide you with metrics such as Page Views, Click-Through Rate, Time Spent On Page, and Conversion Rate.

This data helps you understand how users interact with your product and identify areas where the user experience can be improved.

For example, if analytics data shows that users are spending a lot of time on a particular page but not taking the desired action, this could indicate a problem with the page’s design or content.

By analyzing this UX data, you can make decisions backed by evidence rather than assumptions.

The Value of Customer Feedback in UX Research

Customer feedback is a valuable source of UX data because it comes directly from the users themselves.

Whether it’s through reviews, support tickets, or direct communication, customer feedback provides you with insights into how users perceive your product and what issues they encounter. This qualitative data is crucial for understanding the emotional and psychological aspects of the user experience.

By analyzing customer feedback, you can identify recurring themes and trends that indicate areas for improvement.

Key Concepts in UX Analysis

Before you start collecting UX data, it is important that you understand some of the key concepts in UX analysis. They will help you interpret it effectively. Some of the most important include:

• User personas : Representations of your target users based on demographics, behaviors, needs, and pain points. They provide the context needed to interpret the UX data you gather. Note that, in some cases, the insights derived from your UX research can help you draw out a persona within your audience further down the line. User personas aren’t necessarily established at the beginning of the research process.
• User journeys : The steps that users take to achieve a goal within your product. These journeys shed the light on pain points that users encounter as well as areas for improvement.

For example, in an e-commerce context, you might create a persona of a typical shopper and map their journey from browsing to purchasing, identifying where they might encounter issues.

Let’s dive deeper into each one of these.

The Role of User Personas in UX Analysis

User personas are fictional representations of your target users. They are created based on real data from UX research and represent the key demographics, behaviors, pain points, and needs of your users.

They provide a human face to the data, making it easier to interpret UX data and make informed decisions about the user experience.

By using user personas in UX analysis, you can better understand how different types of users interact with your product.

Mapping User Journeys for Better UX Analysis

User journeys represent the steps users take to achieve a goal within your product. By mapping out these journeys, you can see where they drop off or succeed.

For example, in an e-commerce business, a user journey might start with searching for a product, continue with adding the product to the cart, and end with completing the purchase. By analyzing the data associated with each step of this journey, you can identify where users face challenges and make data-driven decisions to optimize the user experience.

How User Journeys and Personas Interact in UX Analysis

By combining user personas and user journeys, you can get a more complete view of the user experience. User personas give context to the data by representing the target users, while user journeys map out the steps these users take within the product. Together, they provide a framework for interpreting UX data and making the best decisions about product design and development.

For example, by understanding that a particular user persona struggles with the checkout process, you can focus your UX analysis on that part of the user journey. This targeted approach enables you to make improvements where they will have the most impact on the overall user experience.

Now that you understand the foundational concepts of UX analysis, let’s go over the ways you can start collecting data.

Preparing Your Data for Analysis

Ux data collection methods.

Collecting data is the first step in drawing conclusions from it. To do so, you can employ several methods:

• Moderated testing : Conduct sessions with users to observe how they interact with the product. The feedback gathered is qualitative, even though certain findings can be supported by quantitative data.
• Unmoderated testing : It allows users to interact with a product independently. Data from these interactions is collected through automated tools.
• Surveys and polls : These are structured questions posed to a large number of users to gather both quantitative and qualitative data.

For instance, you could run a moderated test where users attempt to find a product on your e-commerce site, or send out surveys asking how easy it was to find what they were looking for.

Ensuring Your UX Data Is Reliable

Once you’ve decided on the methods for gathering data and the type of data you want, ensuring consistency should be your next priority. Using the same methods and tools across research sessions and user groups is crucial.

For example, if you’re collecting UX data through surveys, use the same questions and formats each time. If you’re conducting usability tests, use the same set of tasks and instructions for each participant. This consistency ensures that the UX data you collect is reliable.

Regularly reviewing the data is also important. Check for errors and anomalies. If answers differ significantly from one session to another, there might be an issue with your sample, the way you ask questions, or the instructions given to users. If they get stuck, it might not be solely because of the website’s usability, but rather due to how you’re instructing them to complete tasks.

Cleaning and Organizing Your UX Data

Once you have collected UX data, it might be necessary to clean and organize it in order to make the analysis smoother. Well-organized data makes it easier to identify patterns and trends. 

Here are some best practices:

• Remove outliers : Exclude data points that are significantly different from the rest. For instance, if some users sent you incomplete survey responses, remove them to ensure accuracy in your UX analysis.
• Categorize data : Group data into relevant categories. Use folders and subfolders based on different aspects of the user experience. For example, you might have separate folders for usability test results, survey responses, etc.
• Label and tag : Assign labels to your data points to make it easier to organize and retrieve when necessary. For example, you might label usability test files by date and participant ID.
• Document your process : Keep a record of how your UX data is organized, including the rationale behind your categorization and labeling choices. This documentation will be helpful if you need to revisit the data or if others need to access it.

Tools and Software for UX Analysis

Several robust tools can turn complex data into digestible information. If you would like to use one of them, here are some popular options:

• Userlytics : Userlytics is a leading platform for remote UX research and usability testing, offering features like Sentiment Analysis and First-Click Testing to make data more understandable. It comes with its proprietary panel of more than 2 million participants. Another popular alternative is UserTesting.
• Google Analytics: It tracks website traffic, user behavior, conversion rates, and more. It’s widely used by both small and large companies, although it is mostly limited to quantitative data.
• Hotjar: Excellent for generating heatmaps and session recordings, showing where and how users interact with your product.

How to Choose the Right UX Tool

When selecting a UX analysis tool, consider factors such as:

• Budget: Ensure the tool aligns with your budget constraints.
• Features and project requirements: Identify the specific features needed for your analysis. Do you require moderated testing? Should it be qualitative or quantitative? Answering these questions will help in making the right choice.

Small businesses might start with free or low-cost tools like Google Analytics before investing in more advanced solutions. Large companies with bigger UX research budgets may benefit more from comprehensive platforms such as Userlytics.

If you’re interested in investing in a comprehensive UX research and testing tool, check out our guide on the 6 best UX testing tools of 2024 . 

Analyzing Quantitative UX Data

Key metrics to track.

Quantitative UX research can generate a variety of insights. Some common metrics include Task Success Rate, Time on Task, and Error Rates. 

Task Success Rate

Task Success Rate measures the percentage of users who successfully complete a specific task. For example, on an e-commerce site, a high success rate might indicate that users find it easy to complete a purchase.

How to Calculate the Task Success Rate: (Number of Successful Tasks / Total Task Attempts) x 100.

Time on Task

Time on Task measures the average time users take to complete a task. This metric can highlight where users struggle. For example, if users take a long time to complete a purchase, the checkout process might be too complex.

How to Calculate Time on Task: Total Search Time / Number of Searches.

Error Rates

Error Rates track the number of errors users make while using a product. High error rates suggest usability issues. For example, if users frequently enter incorrect information during checkout, it might indicate a need to simplify input fields or provide clearer instructions.

How to Calculate the Error Rate: (Total Number of Misinterpretations / Total Number of Attempts) x 100.

Analyzing Qualitative UX Data

Analyzing qualitative data can be more challenging than quantitative data analysis because each user’s feedback is unique. However, you can use techniques like Thematic Analysis to make the process smoother.

Thematic Analysis

Thematic Analysis identifies patterns and themes within your qualitative UX data. Those patterns can be categorized into groups to facilitate the analysis.

Here are the steps you can take to perform Thematic Analysis on your qualitative UX data:

• Familiarization : Review the UX data enough times to become familiar with its content.
• Labeling or coding : Once you’ve familiarized yourself with the data, assign labels or codes to segments based on their theme.
• Identify themes : Group these labels into broader themes representing common patterns.

To illustrate the above, you might code interview responses from users giving their feedback about their checkout experience, identifying themes like “confusing interface” or “missing payment options.”

Interpreting UX Data

Identifying patterns and trends.

After collecting and categorizing data, the next step is interpreting it. This means identifying patterns and trends that will help you understand the overall user experience.

To interpret your data effectively, here are some foundational steps you can follow:

• Aggregate your data : If you collected UX data from several sources, combine them to get a comprehensive understanding.
• Recognize patterns : Identify recurring themes and behaviors. For example, you might notice that users tend to abandon their carts after filling them up in the first place. Why? 
• Analyze trends : Monitor changes in user behavior over time to identify trends and potential areas for improvement. To follow-up on our previous example, you might notice that visitors abandon their carts specifically after seeing your shipping costs. This could suggest the need for more transparent pricing.

Drawing Insights from UX Analysis and Avoiding Common Mistakes

The next step is to turn the raw UX data you’ve gathered into practical recommendations. Start by pinpointing areas where improvements can enhance the user experience. You may come up with various ideas, such as reducing the steps in the checkout process or being more transparent about delivery costs. But which one should come first?

Deciding which changes will have the most impact can be tricky. By suggesting solutions and discussing them with your team, you can make a well-rounded decision.

It’s important to share your findings and recommendations with your team because they can help challenge any preconceived notions you might have. Confirmation bias is a common trap in UX research and design, and interpreting data to fit your assumptions can lead you in the wrong direction.

Be careful when applying your findings to a larger audience without solid proof. If your UX research was done with a small group, it may not be accurate to generalize the results. Make sure your conclusions are based on solid data and apply to all your users. For example, you can use a sample size calculator to find the right sample size that represents your audience.

Communicating Your Findings

In order to communicate your findings and recommendations to your team, you must ensure that your reports are structured and easy to understand.

Let’s explore some strategies to make sure your reports gain maximum buy-in from stakeholders.

Creating Effective Reports

The insights from UX research can be abundant, diverse, and sometimes quite complex. To communicate them effectively, you can use the following strategies:

• A clear structure : Organize your information logically by creating sections for key findings and recommendations. Earlier, we discussed labeling and categorizing your insights. Use these labels to structure your report, linking key findings like “Confusing checkout process” to the relevant UX data.
• Make it visual : Most people don’t enjoy reading through numbers. The more visually intuitive your report is, the more impactful it will be.
• Make it actionable : Offer solutions to your findings. Your report should go beyond just presenting the data; it should also include recommendations to address the issues and help improve the product.

A well-structured report helps stakeholders grasp the findings and make informed decisions. Beyond the recommendations mentioned earlier, one of the most powerful ways to communicate your findings is by crafting a compelling and engaging narrative. Connecting the data to the user experience through storytelling ensures that your team and stakeholders fully understand your recommendations.

Here are some of the best strategies to make your data more compelling:

• Use a persona: Step into the shoes of your target audience and guide your audience through the ideal user journey. Utilize this fictional persona throughout the report to illustrate each of your key points.
• Use real examples: Include real-life examples, supported by quotes or video snippets, to highlight the stages your persona experiences. These examples can reinforce your findings and make them more relatable.
• Illustrate your recommendations with an improved user journey: Show the potential impact of your recommendations on the user experience and the company’s business goals. Revisit the stages your fictional persona goes through and demonstrate how their experience would improve with the implementation of your recommendations.

Iterate And Improve Continuously

What happens after you’ve presented your points to your team and stakeholders? Don’t let your report gather dust! 

UX analysis should be an ongoing process. Keep revisiting your data and findings regularly. You might be surprised at how many new insights and improvement ideas you can uncover by consistently reviewing your data.

Understanding how users interact with your product is key to creating a great user experience. UX analysis helps you uncover important insights about the behavior of your users, their preferences, but also their frustrations. By analyzing both quantitative and qualitative data, you can make decisions that directly improve their experience.

When you combine UX research with data analysis, you gain a deeper understanding of your users. Whether you’re using usability tests, surveys, or any type of analytical tools, the insights you gather will guide your design and development processes.

By regularly collecting and analyzing UX data, you stay ahead of potential issues and find opportunities for improvement. This approach leads to happier users and, subsequently, a stronger brand reputation.

In short, UX analysis is about connecting with your users, understanding their needs, and creating a product that truly resonates with them. By focusing on this data-driven approach, you can build products that delight users and help your business thrive.

Consider using Userlytics for all your UX research needs – from conducting usability testing to analyzing its results. Our comprehensive suite of tools can help you take your user experience from good to truly great. Reach out to us .

What is UX analysis?

UX analysis is the process of examining user data to understand how they interact with a product. This analysis helps identify areas for improvement in the user experience.

Why is UX analysis important?

UX analysis is important because it provides insights into user behavior, helping businesses make informed decisions that improve the user experience and increase customer satisfaction.

What types of data are used in UX analysis?

UX analysis typically involves quantitative data (numerical data) and qualitative data (descriptive data). Both types offer valuable insights into user behavior and the overall user experience.

How can I ensure the quality of my UX data?

To ensure data quality, standardize data collection methods, regularly review data for errors, and maintain detailed documentation of your research process. Consistent practices lead to more reliable UX analysis.

About the Author: Mehdi El Taghdouini

Mehdi is the Head of Content and Communications at Userlytics. He is skilled at writing clear and engaging content on several topics, especially technology and SaaS products. Before joining Userlytics, Mehdi led the content marketing team of the largest Google Cloud and Google Workspace reseller in the Benelux market. He brings six years of experience in managing content for both startups and large companies. Mehdi holds a Bachelor\'s degree in E-Business and enjoys photography in his spare time. His photography projects have been exhibited in Brussels, Hong Kong, and Barcelona, where he now lives.

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Dive into our Resources Hub for a wealth of UX insights and tools, or jumpstart your journey with a free demo today. Discover how Userlytics can transform your user experience strategy!

• Open access
• Published: 03 September 2024

Developing community-based physical activity interventions and recreational programming for children in rural and smaller urban centres: a qualitative exploration of service provider and parent experiences

• Emma Ostermeier 1 ,
• Jason Gilliland 2 , 3 , 4 , 5 , 6 , 7 ,
• Jennifer D. Irwin 7 ,
• Jamie A. Seabrook 3 , 4 , 5 , 6 , 8 &
• Patricia Tucker 5 , 6 , 9  

BMC Health Services Research volume  24 , Article number:  1017 ( 2024 ) Cite this article

Metrics details

Children’s physical inactivity is a persisting international public health concern. While there is a large body of literature examining physical activity interventions for children, the unique physical activity context of low-density communities in rural areas and smaller urban centres remains largely underexplored. With an influx of families migrating to rural communities and small towns, evaluations of health promotion efforts that support physical activity are needed to ensure they are meeting the needs of the growing populations in these settings. The aim of this community-based research was to explore service providers’ and parents’ perspectives on physical activity opportunities available in their community and recommendations toward the development and implementation of efficacious physical activity programming for children in rural communities and smaller urban centres.

Three in-person community forums with recreation service providers ( n  = 37 participants) and 1 online community forum with the parents of school-aged children ( n  = 9 participants) were hosted. An online survey and Mentimeter activity were conducted prior to the community forums to gather participants’ views on the barriers and facilitators to physical activities and suggestions for activity-promoting programs. The service provider and parent discussions were audio-recorded, transcribed verbatim, and analyzed following a deductive approach guided by Hseih and Shannon’s (2005) procedure for direct content analysis. A code list developed from the responses to the pre-forum survey and Mentimeter activity was used to guide the analysis and category development.

Seven distinct categories related to the existing physical activity opportunities and recommendations for programs in rural communities and smaller urban centres were identified during the analysis: (1) Recovery from Pandemic-Related Measures, (2) Knowledge and Access to Programs, (3) Availability, (4) Personnel Support, (5) Quality of Programs and Facilities, (6) Expenses and Subsidies, and (7) Inclusivity and Preferences.

To improve the health and well-being of children who reside in low-density areas, the results of this study highlight service provider and parent recommendations when developing and implementing community-based physical activity programs and interventions in rural and smaller urban settings, including skill development programs, non-competitive activity options, maximizing existing spaces for activities, and financial support.

Peer Review reports

Physical activity is an important behaviour for children’s development, health, and well-being [ 1 ]. The World Health Organization’s guidelines for physical activity and sedentary behaviour recommend that children 5–17 years of age accumulate an average of 60 min of daily moderate-to-vigorous physical activity to attain physical, mental, and cognitive health benefits, including improved quality of life [ 2 ]; however, most children are not meeting the recommendations [ 3 , 4 ]. The high rates of physical inactivity have been further exacerbated by the COVID-19 pandemic, with the literature reporting considerable declines in children’s physical activity during stay-at-home orders [ 5 ] and activity levels remaining low following the reopening of recreational facilities [ 6 ]. In Canada, only 28% of children aged 5 to 17 years met the recommended amount of physical activity during the early years of the pandemic [ 7 ], an 11% decrease from the reported activity levels prior to the pandemic [ 8 ]. This is particularly troubling as sedentary lifestyles during childhood can cultivate unhealthy habits that will continue as they transition into adolescence [ 9 ] and persist into adulthood [ 10 ]. To help engage children in more physical activity as the public health precautions were lifted, parents highlighted the need for a variety of accessible, affordable programs that offered children the opportunity to be active outside of school [ 11 , 12 ]. Therefore, tailored and feasible health promotion interventions and initiatives are essential in preventing the persistent rise in physical inactivity.

Although there has been increasing support for interventions to promote physical activity in children, low-density areas – including dispersed rural communities (i.e., rural areas with a low population density and low population size), villages (i.e., small, semi-dense, rural settlements with a small population size), and smaller urban centres (i.e., semi-dense areas with a moderate population size) – have been underexplored [ 13 , 14 , 15 ], even though thinly populated communities have higher rates of obesity, chronic conditions (e.g., asthma and developmental delays) and mortality among children [ 16 , 17 ]. Due to the lower densities of development in rural and smaller urban settings, children in these areas commonly experience issues related to limited local resources and program options, reduced access to health-related services, and greater need for vehicular transportation to activities [ 18 , 19 ]. With the recent rise in migration of Canadians to rural areas [ 20 ], finding ways to help children from smaller communities overcome the barriers to physical activity participation is valuable. As Canada has the fastest-growing rural communities of the G7 countries [ 20 ], it can serve as an ideal location for additional research on children’s physical activity in less densely populated settings.

The Grade 5 ACT-i-Pass Program is a community-based physical activity intervention originally developed for London, Ontario, Canada that offers children in grade 5 free organized and drop-in activities at participating recreational facilities for the school year [ 21 ]. As previous evaluations of the program have indicated that the pass improved children’s physical activity [ 22 ], expanding the program to additional communities may be a promising approach to address children’s low physical activity levels; therefore, plans for offering the program in the neighbouring rural and smaller urban areas are underway.

Despite community-based interventions having the potential to foster much-needed population-level changes in physical activity [ 23 ], the effective implementation and intended outputs of these programs are vulnerable to the context and can be hindered by a variety of complex individual, social, and environmental conditions [ 24 ]. Durlak and Dupre [ 25 ] suggest that understanding the factors that influence program uptake and adoption by a specific community can help close the gap between an evidence-based intervention plan and its effectiveness in a real-world context. Thus, prior to investing the funds necessary to scale-up this program to rural and smaller urban settings, the extent to which community members would find programs like the ACT-i-Pass suitable needs to be determined to ensure a tailored version of the program that is most likely to be used by the target population is offered.

As an initial step of the program development phase, a needs assessment provides context into the factors associated with children’s engagement in physical activity and service providers’ capacity to offer recreation programs [ 26 ]. Specifically, a multisector approach to physical activity promotion can improve the quality and implementation of interventions in real-world settings by allowing families and community organizations to advise on the development and design of interventions based on their experiences and knowledge of the area [ 27 ]. Gaining input from the target audience during the planning stages of interventions can be used to highlight strategies to address the various social and environmental factors that influence physical activity participation, help align components of interventions with the needs and preferences of the target audience, generate buy-in from the community, and incentivize organizations to promote and adopt programs [ 28 , 29 ]. Notably, studies have shown that multi-disciplinary collaborations that integrate partners during the design stage of interventions can lead to more effective and sustainable health promotion initiatives [ 29 , 30 , 31 ].

The aim of this study was to host discussions with service providers and parents in Oxford, Elgin and Middlesex Counties to understand their experiences with the physical activity opportunities available in rural communities and smaller urban centres and gather their recommendations toward the development and implementation of efficacious physical activity programming for children in dispersed, resource-limited areas. To achieve this aim, this study explored factors which positively or negatively influence children’s physical activity participation in rural communities and smaller urban centres. Moreover, this study gathered parents’ and service providers’ perspectives about the design and/or implementation of health promotion initiatives in their community, specifically, the ACT-i-Pass Program and physical activity interventions targeting children.

Study design

This naturally-unfolding experiment is part of a larger study exploring the adaptation, implementation, and evaluation of the Grade 5 ACT-i-Pass Program expansion. As a case study, this research focuses on a predominantly rural region in Southwestern Ontario, Canada. Oxford, Elgin, and Middlesex counties are made up of farmland, outdoor attractions including conservation areas and beaches, and a variety of smaller urban centres (i.e., towns and small cities) and rural settlements (i.e., villages and dispersed communities) with populations of 22,015, 17,030, and 83,160 children ages 0 to 14 years, respectively [ 32 , 33 , 34 ]. To achieve the aim of this study, we hosted community forums, a group information collection technique that empowers members of the target area to use their knowledge and lived experiences to identify community-level impacts of interventions and provide locally derived strategies that can support beneficial behaviour changes while minimizing potential harms [ 35 ]. This study protocol was approved by Western University’s Non-Medical Research Ethics Board (REB #103954).

Participants and recruitment

Service providers and parents were recruited to participate in this study. Service providers were identified through an online search of recreational facilities, which was reviewed for missing organizations with program partners at the two health units and the municipal governments that attend to the residents of Oxford, Elgin, and Middlesex Counties in an effort to produce a comprehensive list of potential participants. Identified service providers were contacted via email and phone and provided details about the community forum, including an overview of the study. Potential parent participants were identified via the ACT-i-Pass registration form. For year 1 of the expanded program, information was distributed earlier than previous program years, including early access to the registration form, as part of a promotional effort to inform families that the program was now available to children in the counties. An extended pre-program promotion timeline also offered the project team time to recruit parents for the community forums and integrate their feedback into the program design for the upcoming year. Of those who consented to be contacted about research activities, parents were emailed an invitation to participate in the community forum, which included a brief overview of the study and the pre-forum survey.

Service providers were defined as any business, organization or community group that works with children and their families in the counties. To be eligible to participate in this study, service providers had to: (1) offer programs related to physical activity or have mandates that aimed to improve the health and well-being of children (i.e., physical activity program providers, municipal recreation representatives, small business owners who offered activities for children, government employees from family service branches, health unit representatives, and not-for-profit organizations); (2) provide services for families in Oxford County, Elgin County (including the City of St. Thomas), or Middlesex County; (3) speak and understand English; and (4) provided written and oral consent to participate in the study and to be audio-recorded.

Parents were eligible to participate in a community forum if they were the parent or guardian of a grade 5 child(ren) in Oxford, Elgin or Middlesex County who enrolled their child in the ACT-i-Pass during the early registration stage and consented to participate in the research study.

Data collection

Pre-forum survey.

As part of the invitation email for the community forum, service providers and parents were asked to complete an online (via Qualtrics) pre-forum survey. The service provider survey gathered details about their organization, key barriers and facilitators to physical activity opportunities, and the extent to which community members would find the ACT-i-Pass program appropriate for children in their area. Parents were posed similar survey questions as service providers except the parent survey asked to provide socio-demographic information instead of organization details.

Mentimeter activity

Before the start of the community forum conversations, service providers and parents were asked to engage in a brainwriting activity using Mentimeter interactive presentation software ( https://www.mentimeter.com/ ). Brainwriting is a form of idea generation where participants silently and independently record their ideas [ 36 ]. As an alternative to collaborative group-sharing sessions, brainwriting can be an effective way to gain a greater variety of unique ideas by engaging more participants in an activity while minimizing group conflicts, social pressure to conform to the group, and dominance of a few participants’ perspectives [ 37 , 38 ]. Participants could provide an unlimited number of responses to two questions: (1) What are the factors that influence children’s physical activity participation?; and (2) What program components or strategies can lead to successful physical activity programs and interventions in your community? Service provider and parent responses to the Mentimeter activity and the pre-forum survey, including their frequency counts, were amalgamated into a single list.

Community forum discussions

In total, 4 community forums were hosted for service providers ( n  = 3 forums) and parents ( n  = 1 forum) in Spring 2023. Community forums were organized and hosted separately for parents and service providers to acquire the perspective of those trying to access the activities as well as those trying to develop and run programs. In-person community forums with service providers were hosted at local community centres and libraries. Separate community forums were offered in Oxford, Elgin, and Middlesex Counties to improve geographic accessibility. The agenda of the community forums was organized in two parts. The first hour of the forum served as a promotional event for the health units to educate and recruit organizations to the ACT-i-Pass Program. Following a short break, the second hour was a research effort conducted by the research team to gather perspectives from community stakeholders about the physical activity opportunities that exist in the area.

Parent community forums were planned to be in-person, but the research team experienced issues with geographic accessibility, scheduling conflicts, and commitments impacting attendance; consequently, parent community forums were hosted online via Microsoft Teams. Differing from the service provider agenda, the first half hour consisted of an overview of the ACT-i-Pass and a question and answer session, following an hour of discussion guided by the research team about the physical activity opportunities for children in their community. The perspectives of children were not collected for this study as their input will be most valuable after completing a year of the program. By collecting children’s perspectives once they have used the pass, they can offer the research team insight into their experiences and propose adaptations to the ACT-i-Pass design that can improve the quality of the program.

The discussions lasted between 50 and 75 min ( \(\bar x\) = 61 min). Two members of the research team attended each community forum. One member acted as the moderator for all community forum discussions to ensure consistency. The second member took notes to capture all key ideas and thoughts from the participants. Prior to the questions, participants were provided an overview of the topics being discussed and asked if they still consented to be recorded.

The community forum conversations followed a semi-structured interview guide (Additional Files 1 & 2) developed by the research team. The guides for service providers and parents consisted of 7 and 6 questions respectively and a series of prompts. The questions were related to the recreational spaces and activity options available in their community (i.e., What organizations in your community provide physical activity programming for children?), the characteristics of the community that positively or negatively influence physical activity participation (i.e., What characteristics of Oxford/Elgin/Middlesex would you describe as factors that positively or negatively influence children’s physical activity participation?), and the adoption of the community-based programs into their communities (i.e., Do you have any recommendations for the ACT-i-Pass as we begin offering activities in Oxford/Elgin/Middlesex?). Conversations with service providers and parents were audio-recorded and transcribed verbatim via Microsoft Streams. A member of the research team de-identified and reviewed the transcripts for accuracy.

Data analysis

All transcripts were imported into QSR NVivo 12 and analyzed following the steps outlined in Hseih and Shannon’s [ 39 ] procedure for direct content analysis. A deductive approach to the content analysis was deemed appropriate for this study as the responses generated during the pre-forum survey and Mentimeter activity offered a participant-directed list of codes related to children’s physical activity participation, recreation programs, and health promotion interventions in the 3 counties [ 40 ].

The analysis started with the preparation of the coding list by developing the initial coding categories. A list of 119 codes was derived from the service provider- and parent-generated responses in the pre-forum survey and Mentimeter activity. As similar words and terms were used to describe the same phenomena, the responses were refined into a universal term, resulting in 102 unique codes. Subsequently, the codes were grouped into initial categories based on key concepts and a definition for each category was generated. The initial categories were developed by members of the research team who attended the community forums as they had more in-depth knowledge of the data and the nuances associated with statements made by the participants [ 41 ]. An audit trail with a detailed record of the research process was developed to add trustworthiness to the findings [ 42 , 43 ]. The list of pre-determined categories and their definitions were reviewed by an auditor to increase their accuracy and relevance to the responses provided by community forum participants [ 39 ].

Two reviewers analyzed the transcripts independently and collaborated to identify the final categories. Using multiple reviewers during coding can add reliability to the findings and improve the quality of the analysis by introducing various perspectives and lived experiences that can produce a deep, thorough exploration of the data [ 44 ]. The researchers first reviewed the transcripts to familiarize themselves with the data and note any initial patterns or thoughts on the discussions. To isolate the nuances in the topics discussed during the service provider and parent discussions, the data were organized by adding attribute codes to each transcript to identify the study population (i.e., parents or service providers) and location (i.e., Oxford, Elgin, or Middlesex) [ 45 ]. The reviewers then went through the transcript a second time and coded categories using the pre-determined code list. As some factors could be perceived as beneficial or a hindrance in different circumstances, reviewers included a second code, when applicable, to identify if the quote referred to a positive or negative experience. Statements that did not fit into one of the pre-determined codes were highlighted and reviewed to see if a new data-driven code was required.

Recommendations presented by Elo et al. [ 46 ] and Smith et al. [ 47 ] were integrated into the methodology of the study to add trustworthiness (i.e., credibility, transferability, dependability, and confirmability [ 48 ]) and rigour to the findings [ 49 ]. Transferability was introduced to the study by gathering direct testimony from service providers and parents in the counties and providing descriptions of the community and participant characteristics, which allows the reader to make a judgement if the findings are applicable to their settings [ 49 , 50 ]. To establish dependability to the analysis, reviewers engaged in memoing throughout the analysis process, which involved recording thoughts of the transcripts or possible answers to the research question to improve the transparency of the findings [ 45 ]. This process included a critical analysis of the transcripts to identify the potential influence of the focus group facilitators on participants’ responses and to identify potential leading or vague questions [ 46 ]. The reviewers met at various points throughout the analysis to discuss coding and to share notes. Following the categories being finalized by the two reviewers, the research team engaged in the process of “critical friends” to add credibility and conformability to the findings [ 47 ]. As an alternative to inter-rater reliability where the aim is to reach a consensus, this is a reflexive activity that encourages in-depth discussions amongst the research team, where the reviewers offer their interpretations of the data and others present critical feedback that can challenge the reviewers’ biases, pre-conceived ideas and knowledge of the subject matter that may have influenced the findings [ 47 ].

Participants

In total, 94 physical activity service providers and community organizations from across the counties of Oxford, Elgin and Middlesex were contacted. From the invited organizations, 42 representatives from 38 organizations attended one of the community forums, with 37 representatives (39.36%) consenting to participate in the research study (with time constraints noted as the primary reason for not staying for the community forum group discussion). Additionally, 79 parents consented to be contacted about ACT-i-Pass research projects. Of those who consented, 9 parents participated in the community forum (11.39%). Participants were dispersed across the counties, with most parents characterizing themselves as white ( n  = 8; 88.89%) and female ( n  = 9; 100%). See participant characteristics for both the service provider and parent community forums in Table  1 .

Category development

The positive and negative factors related to children’s physical activity participation and physical activity programs identified by service providers and parents during the Mentimeter activity and the pre-forum survey are visually represented in Fig.  1 A and B respectively.

Positive and negative factors related to children’s physical activity in rural and smaller urban centres. Positive factors are represented in blue ( A ) and negative factors are represented in red ( B ). The words represent service provider and parent responses to the pre-forum survey and Mentimeter questions related to children’s barriers and facilitators to physical activity participation, the design and implementation of physical activity programs, and recommendations for physical activity programs in their community

The synthesis of the service provider and parent responses to the Mentimeter activity and pre-forum survey resulted in 10 initial coding categories. Following the analysis of the transcripts and discussion amongst the research team, 1 new category was added and 4 categories were integrated into other existing categories due to similarities in content. This resulted in 7 unique categories. Further details on the categories and their definitions can be found in Fig.  2 .

Categories developed and adapted from the pre-forum survey, Mentimeter activity and community forum discussions. Yellow codes represent ideas discussed during service provider community forums, blue codes represent the ideas from the parent community forum, and green codes represent the ideas discussed by both groups

Recovery from pandemic-related measures

Conversations in all the community forums highlighted the long-term impacts of the COVID-19 pandemic on children’s physical activity. Specifically, service providers and parents believed the public health protections introduced to reduce transmission of the virus were associated with lower physical activity levels that have yet to return to pre-pandemic levels.

Despite both groups describing the barriers and challenges created by the pandemic, the focus of the discussions differed between parents and service providers. The community forum discussions with parents were directed toward their child’s quality of life. During the early stages of the COVID-19 pandemic in 2020 and 2021, all the parents agreed that children lacked access to activities, resulting in, “two years or so of limited access to everything and they didn’t even do it for an entire summer”. Without their regular opportunities during the closure of recreational facilities and gyms, some parents expressed concerns about the physical activity-related skills their children may lack, with one parent explaining, “they [gyms] had to modify a lot longer than other places due to the fact that they were known as potential super spreader locations”. As a result, some parents felt that “it’s unfortunate for our kids now who didn’t get that opportunity that you didn’t realize at the time was such a big developmental stage that they were in”. Without the opportunity for children to try different activities and develop their physical activity-related skills, parents worried about the long-term influence the early years of the pandemic may have had on their children’s physical activity participation.

Alternatively, service providers were focused on the influence of the pandemic-related protocols on program attendance and the consequential changes to the current program offerings and schedules. Following the re-opening of gyms and recreational spaces after the removal of COVID-19 protocols, many service providers felt that enrollment rates had not returned to pre-pandemic numbers. As one service provider mentioned, “getting kids to sign up for anything is difficult. Getting them to register for anything is impossible”. Another service provider expanded on this topic, discussing their experience recruiting children after they re-opened: “Pre-pandemic, all our programs were full. We were bursting at the seams March 2020. We are just slowly trying to figure out what people want right now. Our membership base is really changed and we’re not seeing the kids in the drop-in programs like we used to”. As a result, service providers had to adapt their programming options and scheduling. This includes “I would say at 6 out of our 10 branches we’ve changed our hours” and “trying to figure out what works and we’re hoping in the next session [Summer] to add a few more programs”.

Knowledge and access to programs

Both service providers and parents noted the concept of accessibility of activities for children in their communities; specifically, discussions were focused on the knowledge of and ability to partake in physical activity programs. One of the primary topics explored during the community forums was the unique aspects of the rural environment that influence children’s ability to get to the recreational facilities or small businesses offering activities. In addition to physical accessibility, service providers and parents discussed families’ awareness of the local physical activity opportunities.

Rural environments were described as low-density and dispersed spaces that, “if you live in a rural community, there’s no option if you don’t have a car” (Service Provider). The dispersed organization of these communities limits children’s ability to get to activities by themselves. Service providers and parents both described safety concerns with children travelling to activities by themselves, referring to “they’re [recreation facilities] a distance away and it’s the time of the year that’s dark” (Parent), and “there’s no bike paths leading to here [our facility], so those are barriers for that age” (Service Provider). Public transportation is non-existent in rural areas, placing pressure on parents to get their children to activities. As described by one parent, “I think it’s just access is a really big one, so like physically getting into the program and getting to London isn’t going to work for a lot of the community because there’s no public transportation between here and there.” This is a particularly large issue in small rural communities that lack resource availability and require families to travel to other municipalities or towns to access services, as mentioned by one parent: “I live in a town where we piggyback off the other town, so I have to travel only because my town doesn’t offer sports”. One challenge service providers can encounter is families’ unwillingness to travel to activities. Rural communities can cover a large area and it can be difficult to come up with programs that are accessible to all families within the region. As one service provider explained, “when we do county-wide scavenger hunts or something like that, if they live in the Far East they’re not going to [go]. Absolutely not. They might go to St. Thomas, but they’re not going from one end [of the county] to the other”.

In addition, many parents highlighted having difficulties finding programs for children, describing that it requires time and research on multiple platforms: “I think there’s programs all over the place. Some are private. Some are public. Some are invite only. Some of them are on Facebook and some of them are word of mouth.” As a result, one parent believed that they needed to be self-reliant to find their child after school activities and “sometimes we have to seek the questions and ask ourselves and not wait for the information to come to us”. One parent noted that access to information also differs among different socio-demographic groups in their region, with those from “the lower income side … [they] don’t have a lot of access to the information that gets sent out and be educated on things so there’s certainly a barrier of almost classism.”

One of the obstacles for service providers is figuring out how to best promote programs. While deliberating about effective ways to get information to parents, service providers indicated that the ultimate difficulty is that “there’s so much information out there that everything just gets bogged down, right? Gets lost in Facebook walls or Instagram or whatever”. Some service providers attributed promotion challenges to the popularity of different media platforms, specifically highlighting previously used modes of promotion now have limited effectiveness. Some examples provided by service providers included, “a newsletter every quarter of what’s going on and the newsprint in our area, people don’t read it anymore”, “FM radio is there and that’s supposed to be our local news for all that and most people don’t listen”, and “internet out in the rural areas is not always easy”.

Recommendations

To alleviate the issues associated with the physical accessibility of programs, parents and service providers recommended that interventions take the environment into greater consideration when developing programs for rural and smaller urban centres. Service providers encouraged more efforts to be focused on smaller communities that lack local recreational facilities and programs, including boosting the community’s use of outdoor spaces.

To better support parents’ understanding of the recreational opportunities available to their children, several parents spoke of the need for an online repository where the information for all physical activity programs can be found in one location, as emphasized by one who said, “it would be nice if there was a central spot where all of that [recreation programs] could be held and not necessarily relying on Facebook to find all that… ”.

Availability

A large portion of the community forum conversations centred around the availability of physical activity opportunities related to the programs, facilities, and resources in the community that can be used by children. Primarily, service providers and parents focused on the variety of activity options available to children.

In the counties, the activity options offered by municipalities can vary between communities, with some places not having programs, services and/or spaces for children to play. As one parent described:

They have the space, but they don’t have necessarily the programs. I’ll give you an example. We have a tennis court, but there’s nobody to run a tennis program. We don’t have the trained athlete or adult to run the programs. There’s badminton areas and volleyball areas, but there’s no one to run the program in our area again.

When trying to enrol in programs, some parents mentioned having difficulties getting a space for their child, with one parent highlighting, “show up two minutes late [to register] and now they can’t get in [the program]. Yeah, it really feels like if you already know then you’re good, but if it’s something new you’re trying to try out, good luck”. By not being able to enrol their child in local physical activity opportunities, parents struggle to get their children active outside of school.

In response to parents’ concerns about activities not being available or programs having insufficient spaces, service providers explained that limited activity offerings may be a consequence of previous attendance rates. As one service provider explained, “it gives you that justification to run the program that the numbers [participants] are there and it[s] driving revenue into your pocket, then you could say yeah let’s drive it forward”. Attendance is especially important in smaller, rural communities that have limited recreation budgets as underscored by one service provider who said, “[our municipality] does have a community center, but I know that they have been struggling to get people, so that’s affecting their offerings”. Consequently, local private organizations and small businesses are critical resources for physical activity in non-urban areas.

In addition to the activities, service providers referred to the available spaces for physical activity in rural and smaller urban centres. Predominantly, service providers focused on dispersed rural communities as they do not have local indoor recreational facilities. One service provider detailed, “again, it comes down to amenities and facilities. There aren’t really any there. It’s the rural part. There’s no facilities so there’s no programs”. While there may be a lack of indoor facilities for physical activity, a variety of outdoor spaces do exist in the counties; however, children can encounter challenges when trying to use these spaces. For instance, the definitions linked to specific places can limit children’s use of outdoor recreational facilities. One service provider referred to the definition of a space in terms of the associated activity: “Yeah, so if you have a big open park that is a soccer field, you can’t do anything else there but soccer. You can’t go and run around or do stuff because then they think you get kicked off”. In addition, service providers believed demographics, particularly age, influenced the places children felt they were allowed to use to play. For example, one service provider discussed older children’s experiences playing on the local playgrounds:

The facilities seem to be claimed by another group. It’s like your sense of belonging, like ‘well, I can’t go there’, and I hear it quite regularly by youth that are in that transitional age that they don’t feel like they could even go to the playground facility because it’s for younger kids and they’re deemed troublemakers if they’re there… so the facility might be there, but they’re not welcomed there.

Parents requested additional spaces in organized recreation programs to help alleviate their current frustrations. Conversely, based on the conversations with service providers, capacity can vary across community types and resource availability, as one service provider described, “if you look at what the capacity of the City of London compared to the capacity of the county and the capacity of each municipality is very different”. Service providers suggested that the development of seasonal programming should be influenced by the available spaces in the community, prioritizing activities that they can offer consistently and sustainably.

For service providers, particularly municipal recreation departments, to maximize the available spaces in the community and increase their capacity for additional programming, non-traditional locations for physical activity programs were suggested. This includes offering activities in any large, open room that is available such as a church, school, or library. The discussions also highlighted the large number of outdoor spaces in their communities. However, some parents noted that outdoor spaces were being underutilized, “you’re not just going to meet a bunch of kids at the park for a few hours. It’s rare that we just find random kids on the street that they can go play with… Yeah, my kids don’t have the internal appetite to just go outside and play”. Thus, parents believed additional outdoor organized activities, particularly during the summer, would be an advantageous way to increase the number of physical activity options and encourage more children to be active. Service providers did note that children may perceive certain outdoor locations as unwelcoming and unavailable and emphasized the importance of educating and redefining the way children view the spaces in their community.

Personnel support

There are multiple levels of support required for children to engage in physical activity. Service providers and parents highlighted four groups: friends and peers, parents/guardians, schools, and governments and municipalities.

Both service providers and parents discussed the difficulties parents/guardians face when trying to engage their children in physical activity. The discussions with service providers indicated that many families in rural communities “have to travel… My town is close enough to bigger centers, but, and as I hate to say, behind the times so there’s nothing”. Consequently, it can be difficult for parents who live in rural communities who drive longer distances to work. As one parent mentioned, “parents that work outside of their community have to drive all the way home at the end of the workday to pick up their child, and then to drive an hour back into [the city] is a lot of hours in a car. That is a lot of time consumed that is difficult for families and gas”. An additional issue service providers mentioned about parents’ ability to support active lifestyles was their knowledge of physical activity expectations for children. Some service providers felt, “the parents that I talked to in training have very little idea of physical activity guidelines, but they have an idea of what their child looks like. There are a lot of barriers and to kind of make sense of what’s out there and how it applies to raising a child”. As a result, service providers believed that low registration rates were potentially attributed to inadequate physical activity literacy.

While peers were primarily described as a positive influence on children’s physical activity, peer pressure was recognized by parents. If friends exhibit dislike for, or remove themselves from, an activity, this may discourage a child from participating. As one parent noted, “depending on who’s in their class, my daughter would definitely choose to sit on the sideline with her friend than try dodgeball”.

Governments and municipality officials were also highlighted by service providers as a group that has hindered children’s ability to be physically active. As one service provider describes, “a lot of policies in these small towns… I know that’s an issue in a lot of small communities, the liability issues”. Specifically, the safety protocols that need to be enforced at their facilities have led to inequities in activity access. As one service provider mentioned, “A lot of street hockey going on right now and the powers that be shutting it down… Hard getting their kids out to let them do anything because there’s always somebody watching saying ‘no, no, no you can’t’”. Similarly, another service provider talked about their skating programs and the new helmet regulations:

It was felt really hard this year with the new board policy for skating at the arenas. The school board implemented a policy of CSA-approved helmets, so children that only had a bicycle helmet could no longer participate in the school field trip for skating unless their families could pay to get them a hockey helmet or ice hockey helmet. Very limiting policy for those children to be able to participate.

While the government’s efforts aim to create a safety measure that protects children, they have also led to greater inequities in physical activity participation.

Facilitators

Peers were characterized as key influencers in children’s lives, with parents and service providers describing how they can encourage each other to be active. For example, parents highlighted, “if you can bring a friend with you they’re more than likely to go with a buddy or two or a couple people instead of by themselves”, and “you both can kind of support each other on the [basketball] court and it’ll be great and they had a great time, but it was only because her friend was joining that she joined”. Some service providers have also seen the benefits of peers encouraging participation in recreation programs, explaining, “our badminton program almost didn’t run this past season because we had one kid signed up for the first month and then within probably a week or so of us cancelling the program, we had 15 kids sign up because one kid told his friends”. Overall, peers were viewed as an important driver of physical activity for children by acting as a key support system during activities.

Besides peers, parents and guardians have a pivotal role in their children’s health and are “key to their child’s physical activity” (Parent). Many parents felt that it was their responsibility to encourage their children to be active: “I guess it also at the grade 5 level, it’s really the parent that needs to push it [physical activity]. The parent is the one that has to drive them. The parent has to free time up in the afternoon, not to be cooking or cleaning or picking up from the week, but let’s pause and do physical activity”. Some of the service providers believed parents demonstrated they recognized the relationship between physical activity and their children’s health and well-being: “I have parents emailing me every day right now about stuff, so I think parents are starting to see what we are seeing, that their kids aren’t active enough”. Many parents described being happy to take their children to activities, stating, “it’s a choice, but you also see the joy in the kid, your kid’s eyes and you wanna keep going because they just love it so much”.

In addition, schools were described as key settings for physical activity, with staff playing an important role in physical activity promotion. Parents believed schools, specifically physical education classes, are responsible for introducing children to activities:

The other thing with sports is that you have to sign up for a period of time and we were just saying, if they’re not introduced to it in school, how would they know if they like it? And then why would a parent pay $300 for them to try something that they might absolutely hate? So, something like school can help introduce sports.

Similarly, many service providers viewed schools as advantageous places for physical activity, specifically for afterschool programs as “schools can provide space after hours and the kids are already there”. Schools were also labelled as a central location for program promotion, with one service provider stating, “schools are actually sending their papers home. They send their newsletter home once a week, electronically”. In terms of staff, teachers can be ambassadors and advocates for children’s participation in physical activity. As one parent explains, “if you get it to the right teachers, they interact with parents all the time. I know that they will send like a video or something”.

Based on the conversations with service providers and parents, creating partnerships is important for community-based interventions and recreation programs. Some service providers believed that talking with “established organizations that have the audience has been a driver of success for programs especially”. Teachers and administrative staff at schools were key collaborators identified during the community forums as they are constantly in contact with parents and can easily share information about recreation programs with their classes. Service providers have talked about the benefits of teacher advocates for physical activity interventions like the ACT-i-Pass Program, with one recommending, “put it in some of the teachers’ brains that ‘hey, guess what? We got this ACT-i-Pass thing’. They can physically talk to a parent instead of just a paper or something that gets missed”. Additionally, service providers recommended that parents be provided more education about the national movement guidelines to reinforce the amount of physical activity children should be acquiring.

Quality of programs and facilities

The quality of the physical activity offerings and facilities was discussed during the service provider community forums. By quality, service providers referred to the facilities being in good condition and programs being led by trained personnel who are skilled in the activity.

A few service providers noted changes to the composition of the counties over the last few years, including the growing population, changing demographics and redevelopment, as one of the underlying reasons for lower program quality. This has been particularly difficult in rural and smaller urban centres, with one service provider explaining, “everyone’s moving out of the city into the smaller towns so it makes sense to expand them now, establish them now, but [my community] hasn’t done anything”. As a result, service providers stressed that the internal migration “changes the dynamic of how you look at programming too because you could have a group you catered to for a while and then you have a line of families that are coming in from other places. They are expecting a lot of different standards of smaller areas which forces us to grow too”.

To offer a quality program, many service providers emphasized the demand for qualified staff that are knowledgeable about the activity and “skilled enough to be able to actually provide the program”. As mentioned by one of the service providers, “finding that instructor is definitely the hardest part when you’re trying to either start or restart a program, because if you don’t have that person to lead it or you don’t have the right person to lead it, your program doesn’t work no matter whether you had 1500 kids interested in that program if you don’t have someone excited and skilled to run it”. Due to the low population size of rural and smaller urban centres, finding community members who are proficient in an activity and willing to teach the skills to children is one of the service providers’ key obstacles in offering recreation programs.

When offering new programs, service providers stressed the time needed to gain community buy-in, as recreation programs are a “community service, it’s a service that you’re offering the community, so their interest is important”. The challenge highlighted by service providers is the time and effort required to gain awareness and secure regular enrollment in programs, which is necessary for their longevity:

It doesn’t happen overnight that people will come … It’s building the consistency, so families know that’s what’s gonna happen, whether they have 3 people show up for open basketball or whether there’s 20 people show up. If you don’t have the consistency, I think it’s really hard to be able to keep programming and families close within that area to participate in it.

To encourage community engagement, service providers have found that partnerships can help provide useful insight into the program models that work and the different approaches that have been unsuccessful. For instance, some service providers believed that sharing their experiences with other organizations can improve the quality of physical activity offerings across the community. One service provider referred to their experience meeting with the recreation programmers across their county:

I mentioned earlier how the municipalities who are in recreation are more than willing to talk to each other and share information with each other about what works and what doesn’t work. We started to try to open a membership option with some of our recreation programs and we reached out to a couple [of organizations], like, ‘hey, have you seen that this is a good thing or not?’

Consulting families was also viewed as vital for higher-quality programs. One service provider found that “a big piece, if you wanted to utilize those spaces, would be to engage with the youth to understand, like, if we open the gym or do we have a structured basketball tournament or badminton tournament or whatever that be”. By talking with potential users, this provides “validation that if they are going to pay staffing to be there and that people are going to show up”.

To account for the rising population, a service provider suggested that municipalities need to account for physical activity-related facilities and staffing during the development of rural communities and smaller urban centres: “we need to be able to provide the programs and amenities that come with that [the county growing], but until other things grow, whether it’s facilities or staffing or availability or whatever it is, you won’t grow with the population”. Service providers from rural communities also noted that it takes time to gain awareness among families when they introduce new program offerings, recommending that fellow program coordinators “… keep in mind with timing, it’ll take time. The population is lower, but we find things take longer and you have to build over time. Be patient”.

Expenses and subsidies

The expenses related to physical activity programming were a predominant topic among all community forums; however, the focus of expenses for parents was related to the cost of attending activities, while service providers were associated with the cost of managing programs.

For parents, the topic of expenses was related to the cost of their child attending and participating in activities. Ultimately, many parents felt that the price of organized physical activity is too high, with some describing sports as unfeasible opportunities for their children. As one parent described her son’s hockey season, “we’ll be in at $5000 by the time the season’s done and that’s just local league. That is cheap hockey. Now, if he wants to go competitive, some of my friends are saying they’re spending $7,000 to $10,000 for them to play competitive”. Families attributed the challenges associated with expenses to the cost of living “getting worse. We had a conversation at our dinner table about the cost of living. Everyone’s talking about it increasing”. Due to the high prices, parents felt that it can be difficult for children to try a variety of activities and find what they enjoy as one parent reported, “we’d be more than willing to sign our kids up for a bunch of programs if they had them, if we could… I can maybe pick one and then that’s all you can get this year because it’s all financially I can do”, meaning that “the cost of certain programs are just not attainable for some people… there’s a much larger cost to getting into the programs, so that negates it for some people”.

In addition to the registration fee, parents attributed transportation and unplanned expenses as challenging supplemental costs. Parents described the cost of gas accumulating quickly throughout the season, “now I’m driving him every day, not every day, but to his practices and his games. Well, that’s gas money, that’s another thousand dollars”. There are also team events that can lead to activities being more expensive than planned. For instance, one parent discussed the extra costs they noticed as their child engaged in more team sports:

It’s not only just the cost of equipment, but people go out for dinner after or they go out for ice cream. It’s all those things that if you can’t afford to bring your child, pay for it, the child might just decide ‘I don’t wanna be the one who’s going and I can’t go out for a meal after or get that ice cream cone with the group because I don’t have the $4’, so it’s a lot.

In contrast, service providers were focused on the expenses of managing physical activity programs. Service providers described having to limit the types of activities they can offer due to their available funds. Service providers supporting rural communities believed that it “might be easier for cities and towns to run them [recreation programs] because maybe they have that built into their budget that they can have money to give a program. We don’t, unfortunately”. Also, due to limited funds, they may not be able to offer some free and low-cost programs, with one service provider explaining, “there’s pickleball nets and they get so many people out of that but it’s free and that’s not something that I can do with our programs”.

Service providers also discussed the available resources in their communities. Due to budgets, service providers reported issues getting access to the necessary equipment and the need to borrow supplies from partners or schools. For those who have the equipment, service providers experienced time and cost challenges of transporting their equipment to facilities: “We have our equipment because we have our own space… we can bring it there [to the school] but we can’t store it there, which means there’s an extra amount of time and money that goes into that transportation every week for each day”.

Finally, a lack of funds influences the type of staff working at service providers. As one service provider expressed, “getting actual programmers for us, ‘cause we don’t have the Y budget that would provide a programmer to us, so that is a challenge”. In order to recruit the necessary staff, many service providers have to counter the extra costs by increasing the price of their activities: “So then you start paying that that main instructor that price needs to go up in order for us to continue”. Either the price goes up or you don’t run the program”.

To improve access to resources, one suggestion offered by service providers involved partnering with other publicly-funded organizations, such as community centres or libraries, to supply children with equipment that they can borrow and bring home: “Through the Y[MCA] or a program like that where you could come and get sports equipment or things so they can try a sport whether it be a hockey stick or a baseball glove or a soccer ball or a basketball. To have a sports lending library there”.

To help fund activities, a few service providers found that gaining sponsorships from organizations was a beneficial way to acquire additional funds. As described by one service provider, “maybe there would be another business that might be willing to provide funding so if a child wanted to sign up or to be able to help out businesses that are keen to help but maybe just can’t afford it financially”. External funding partners can also subsidize activity fees for children by acting as a “sponsor a dance class or a Taekwondo class or a something like that”. As offering free programming was deemed difficult or impractical for service providers, it was suggested that grants and subsidy programs be used to help improve families’ access to recreation programs. Funding support offerings can provide opportunities related to “their income level and if they were under a certain level then they received 50% funding for all the registration fees”, or “a necessity program so money is just for low-income families to help cover the cost of activities”.

Inclusion and preferences

Offering a variety of activity types and levels to make service providers more welcoming to all children was another frequently discussed topic during the community forums. As stated by one service provider, “inclusivity is crucial to youth right now, right? So, if you’re not inclusive you’re not being positive and allowing everyone to participate and then you’re not gonna be successful and kids aren’t gonna participate”. The discussions concentrated on service providers having a diverse number of activity types and levels within each activity to consider children’s abilities and preferences.

Some service providers and parents credited children’s low engagement in physical activity to the confidence or skillset to participate in a specific activity. As one service provider discussed, “I have noticed a huge confidence issue. Not picking things up that they aren’t fantastic at right off the bat… ‘I’m not good, I’m outta here, everyone’s better than me’”. A few parents reported seeing confidence issues in their children, with one parent describing, “it’s so tricky, especially when you think about that confidence. The ability to do sport, especially hitting that grade 7, that 13-year-old where you’re very self-conscious.” An explanation for confidence issues is the pressure they feel from their peers when they “size themselves up. It’s a natural thing people do. The ‘am I better than you? Are you better than me?’ mentality” (Parent). To help grow children’s confidence, children are looking for “proper skills and drills, it’s very popular” (Service Provider).

The appropriateness of the available activities may also be lacking with the current program options. Specifically, children have different needs and a greater variety of activities will help offer programs suitable to the different skill sets and ages of children. One characteristic highlighted throughout the conversations was the competitive spirit of children. Some of the parents attributed the lack of participation in organized programs to the absence of non-competitive options for sports. As one parent mentioned, “I find that there’s kind of a gap between like rec hockey players and just base recreation players… They don’t like high levels.” Parents felt that many activities were “the team sport atmosphere. My child’s not competitive, so knowing that she wants to learn, she wants to be better, but she has her own internal competitiveness, not external”. A problem many parents encountered was trying to find programs for their children to try and learn activities, as underscored by one parent while discussing an introductory hockey program in their community:

Now, one thing I don’t know is having those same kids on the ice at the same time as those who have been playing the sport for years because if that’s the case, that’s gonna fail immediately. They almost need their own ice time or their own space that they’re learning at their level.

In addition to the activity options for non-competitive children, the activities need to be age-appropriate. For instance, children can be embarrassed when “my child who is 10 is doing say beginner hockey, but then there’s also 5-year-olds in that group. Even if she’s at the same level as them, she is not going back. She’s like ‘I’m at the same level as a 5-year-old. No, thank you’” (Parent). The financial and personnel constraints service providers experience have also affected the program offerings by prompting more co-ed activities that combine both boys and girls; however, one parent said this has negatively impacted her daughter’s participation in team sports as, “at her age, they’re often both male and female combined, so co-ed. What I’m seeing as a parent is that the boys are becoming bigger and more aggressive as in they’re competitive and she is not, so therefore, she gets intimidated”.

Offering children activities they want to participate in and are passionate about was described as critical for continued physical activity participation. Ultimately, parents cannot force their child to want to take part in an activity. As one service provider highlighted, “you know we have parents bringing kids 3 or 4 years old to take martial arts. The parents are making them do something that doesn’t really draw [their] interest, but after 11 years old they seem to make their own choices”. As one parent noted, providing children with the opportunity to try various activities can be beneficial “if you want them to stay active in the long run, they need to find something they enjoy”.

Moving forward, it will be important to offer activities for various skill levels. As noted by one service provider, “building people’s confidence up, giving them an opportunity—a safe space to try a sport or try an activity with people with the same skill level as them”. In order to develop children’s self-efficacy and increase program uptake, there needs to be a variety of program offerings to account for “the diversity in who the kids are, the ages of the kids and interests” (Service Provider). This can also be done by offering flexible activities where the programs are “something more that evolves and keeps them interested” and they can be adapted by “asking them if they feel good and you’re teaching them to help structure play” (Service Provider). In addition, offering non-competitive and entry-level programs can encourage children to join activities where “everybody that joined it was just kind of trying it. Nothing serious and it made it easier to attend those things as opposed to going with a group of kids who have been playing that sport for 7 years and you’re trying it for the first time” (Parent).

One strategy to alleviate the issue of activity options for all children is offering non-traditional activities. For instance, service providers reported, “people get bogged down with the traditional programming like soccer and basketball. There’s so many other programs that are out there” and “dodgeball’s huge right now. Just those off the cuff programs that aren’t traditional… just doing something that they don’t have the opportunity to do and just being creative with that”. Similarly, service providers suggested that program offerings should integrate trending activities among youth: Working on some trends in certain sports. Like, who would’ve thought pickleball? Cornholes replaced horseshoes. You know what I mean? You gotta kind of recognize it’s replacing something in a more modernizing way.

Through a series of community forums with service providers and parents, this study aimed to explore the physical activity opportunities in rural communities and smaller urban centres and to understand how to develop and implement community-based physical activity programs for children in areas with low resource availability. The discussions with service providers and parents highlighted a variety of barriers and facilitators to physical activity participation. Some examples of barriers included the distance to activities, the expenses related to physical activity programs, and limited resources to meet the population growth. In contrast, flexible activities, promoting programs through schools, and outdoor spaces were described as facilitators. In addition, recommendations for the development and implementation of physical activity programs for children in low-density and minimally resourced areas were noted. Recommendations covered a range of topics such as developing physical activity-related skills, utilizing non-traditional physical activity spaces, and centring program offerings around equipment and personnel capacities.

When asked about the factors that influence children’s physical activity, service providers and parents believed that the loss of organized programs and the closure of recreational facilities due to the government-regulated COVID-19 public health protections had a negative effect on their child(ren)’s physical activity. Children’s preference for organized recreational opportunities and limited involvement in active play is consistent with the evaluations of Canadian children’s physical activity participation [ 7 , 8 ], For instance, Sharp et al. [ 52 ] found that most rural children were looking for structured after school or weekend activities and would enrol in a wide variety of organized programs, such as physical activities, music, clubs, and tutoring. However, children’s desire to engage in organized activities conflicts with the body of literature asserting that there is a lack of resources in non-urban communities [ 53 , 54 ]. In a comparison of rural and urban Canadians, participants from rural communities are more likely to report barriers to accessing recreational facilities [ 55 ]. Due to the interest in more structured activities, implementing community-wide programs and finding strategies to improve recreation offerings can be a beneficial way to promote physical activity participation in resource-limited communities.

Accessibility was noted as a common barrier throughout the community forums, consistent with the literature on rural physical activity [ 56 ]. Poor accessibility was associated with the community structure and resources varying between communities. For instance, Gilbert et al. [ 19 ] found smaller rural communities with a population size of less than 6,000 residents had fewer resources and less infrastructure than larger communities, which may require a tailored intervention plan. Due to the longer distances between home and program offerings, transportation is one of the main barriers to physical activity in rural and smaller urban centres. In non-urban communities, public transportation is non-existent or unreliable, and active transportation is not available to children as parents may be concerned about the lack of bicycle lanes and sidewalks, their children travelling on underutilized routes, and wild animals [ 57 ]. Consequently, children cannot attend programs without a parent or family member acting as a driver. As a result, researchers and program coordinators need to understand the unique characteristics of the different communities in their jurisdiction when developing community-based programs and create an implementation plan that best meets the needs of the whole target population.

Outdoor spaces were also identified as a beneficial method for improving children’s physical activity. Both parents and service providers highlighted the variety of outdoor spaces that are unused by children without organized activities. In addition to engaging children in more physical activity, outdoor spaces have been found to provide various other health-related benefits, including increased self-esteem, problem-solving abilities, social behaviours, and motor skills [ 58 ]. While outdoor spaces can provide additional recreational opportunities when programs and facilities are limited, they may target those who are sufficiently active. For instance, children from rural and remote communities who reported being involved in a higher number of organized activities also reported greater involvement in unstructured leisure activities; this refutes the ‘over-scheduling hypothesis’ that proposes those who participate in more organized activities face time constraints that inhibit participation in unstructured forms of physical activity such as outdoor play [ 52 ]. As the outdoors can provide an open space for imagination and creative activities, offering non-traditional activities in these settings can help engage children who are not interested in sport-focused activity offerings.

In addition, parents and service providers described select individual-level factors as barriers to physical activity participation. Consistent across evaluations of urban and non-urban communities, children are potentially not participating in any programs due to their lack of interest in physical activity options [ 59 ]. Parents and service providers presented conflicting accounts for why there are issues with the current program offerings. Consequently, it is difficult to conclude if service providers’ limited capacity or families’ low uptake has led to a reduced variety of activity options, but they both likely play a role in children’s physical activity opportunities. With the rising internal migration to rural communities on account of the transition to virtual and hybrid work options available during the COVID-19 pandemic [ 20 ], there is an increasing demand for resources and services in these areas. As there are difficulties associated with recruiting staff and the capacity for communities to build more recreational facilities, program offerings should prioritize the resources that currently exist in the community, including integrating the land use and development plans for the municipality to account for the growing population [ 60 ].

One finding highlighted in the current study by both service providers and parents was the cost of recreation programs. Due to the high cost of extracurricular activities, family income is an important factor in physical activity participation for children [ 61 ]. For example, Kellstedt and colleagues [ 62 ] found that children’s chances of partaking in sports were 4 times more likely when they lived in a higher-income household. This aligns with the idea that socioeconomic-based health inequalities increase across the life course because of the cumulative advantage or disadvantage associated with differential access to health-promoting resources, much of which is rooted in early life exposures [ 63 ]. While many recommendations for reducing the economic accessibility of physical activity surround affordable programs, one frequently reported barrier among rural populations is the shortage of free and low-cost physical activity opportunities [ 55 ]. The high cost of activities was also noted as a challenge for service providers. Local governments in smaller communities tend to face financial challenges with limited revenue, minimal financial capacity, and a high cost of living [ 15 ]. As a result, service providers have difficulties maintaining their facilities and creating environments that better support physical activity, which means regular free activity offerings are not a viable solution in many communities.

Recommendations for physical activity interventions and recreation programs

In response to the identified facilitators and barriers related to recreation programs, service providers and parents offered recommendations to integrate into the expansion of the ACT-i-Pass Program and future physical activity interventions. Recognizing that the number of physical activity providers declines as the ACT-i-Pass shifts from a densely-populated city to more dispersed, resource-limited settings, the recommendations provide valuable adaptations to the intervention’s design and implementation that can offer physical activity opportunities tailored to the needs of families in rural and smaller urban communities. For instance, due to the range of conditions that exist in non-urban areas (e.g., population size, resources), the unique characteristics of the different communities and available resources need to be incorporated into community-based programs to ensure activities are accessible to all children, particularly those in low-density rural areas [ 64 ]. For example, the transportation options in dispersed communities differ from urban environments; therefore, additional attention needs to be placed on creating more programs in a variety of neighbourhoods or reducing transportation barriers by offering busing from schools to service providers or encouraging carpooling with other families.

Primarily, creating additional structured activity options for children was deemed a beneficial strategy for engaging children in greater amounts of physical activity. One suggestion included utilizing the abundance of outdoor spaces available in the area. Encouraging outdoor play and creating more outdoor programs in a variety of communities can help children be more active [ 65 ]. In addition, increasing the program offerings to service a greater variety of activity preferences and skill levels can allow programs and interventions to have a greater impact on the health behaviours of children. Traditional activity offerings are not reaching all children, particularly those not interested in sports or competitive environments; therefore, providing unique and fluid programs may help gain their interest in activities and engage them in more physical activity. Program coordinators were encouraged to integrate trending activities (e.g., pickleball) and flexible programs into their offerings. Flexible programs, alternatively termed scaffold play, are child-directed activities that are guided by an adult [ 66 ]. The objective of these activities is to foster children’s development and creativity as they work towards a specified objective outlined by the adult [ 67 ]. While this strategy is primarily used in a preschool context [ 68 ], it may continue to have benefits among older children.

Additionally, partnerships were a key recommendation from service providers, reinforcing the importance of collaborations in successful community-based interventions [ 69 ]. Specifically, it was stressed that community organizations and families are valuable sources of information and support when creating programs for children. Community organizations, such as government agencies and businesses, can assist in the administration of programs and interventions by offering financial support via subsidies or grants that reduce the financial strain of registration fees for families or facility management costs for service providers [ 70 ]. Other partners, such as schools, can also improve awareness of programs and interventions by acting as promoters [ 71 ]. Alternatively, engaging with families can give greater context to the community and help set priorities for interventions based on the interests and the supports needed by the target population [ 72 ].

As COVID-19 continues to influence the physical activity context, there are additional recommendations that need to be integrated into health promotion efforts. For instance, children missed pivotal years of physical education due to the closure of schools and recreational facilities. Perceptions of athletic ability, self-efficacy, and motivation to be active are all factors that can have a significant influence on physical activity behaviours [ 73 ]. Thus, interventions should integrate programs with a greater focus directed toward building children’s physical activity confidence by teaching skill sets and movement competence [ 74 ]. In addition, with many small businesses closing during the pandemic, redefining what qualifies as a setting for physical activity is important. In rural communities, children do take advantage of existing afterschool program opportunities (e.g., church youth groups) when school athletics programs, sports leagues, and recreation activities are limited or unavailable [ 52 ]. As the findings indicate that children are hesitant to use spaces without the guidance of an adult, creating structured programs will make non-conventional physical activity spaces more accessible for children. A full list of the recommendations provided by service provider and parent community forum participants is provided in Fig.  3 .

Service provider and parent-derived recommendations for physical activity programs and interventions in rural and smaller urban centres

Limitations

While this study provides valuable insights into rural and smaller urban centres and physical activity programs, there are limitations that must be considered. The parent community forums exclusively involved responses from mothers. While it is common that parental perspectives on their children’s health behaviours tend to come from mothers [ 75 ], we are missing the paternal perspective that may offer different experiences with their child(ren)’s physical activity. Additionally, our study consisted of families and service providers from Elgin (including the City of St. Thomas), Oxford, and Middlesex Counties. Based on responses to the Census Profile, the populations of these three communities consist primarily of English speakers and non-immigrants and have a lack of racial and ethnic diversity [ 32 , 33 , 34 ]. Due to the similarities between participants, we are unable to make conclusions about the influence of demographic characteristics on the experiences of families from our study area. While efforts were made to produce a thorough list of service providers, the perspectives of some organizations may have been missed if they did not have an online presence or if our community partners were unaware of their existence. Finally, rural communities and smaller urban centres are contextually diverse based on population size and physical activity-specific resources [ 19 ]. There are multiple definitions used to differentiate between urban, suburban, and rural areas that vary based on one or more community characteristic(s), such as population density, population size, distance from an urban area or distance to an essential service [ 76 ]. As a result, the applicability of findings to other non-urban spaces can be challenging and may only relate to the experiences of service providers and families who reside in rural communities, villages and small urban centres that are within an hour’s drive of a large urban centre.

To counter the rise in physical inactivity associated with the COVID-19 pandemic, developing and implementing interventions that can encourage children to live more active lifestyles are critical. To improve the quality and effectiveness of community-based interventions, researchers and program developers should collaborate with community members and organizations to adapt interventions to meet the needs of their target community. This is particularly important for small, dispersed communities that have unique characteristics based on their population size, number of recreational facilities, and activity options. Service providers and parents emphasized the need for interventions and programs that offer accessible, diverse, high-quality program options that are inclusive and meet the needs of all children in the community. To account for the impacts of the COVID-19 pandemic, interventions need to integrate additional opportunities for children to develop their confidence and physical activity-related skills and find resources that can reduce the economic strain associated with recreation programs. While a variety of suggestions from parents and strategies used by service providers were noted, further studies are needed to evaluate the impact of the recommendations on the effectiveness of interventions and recreation programs in rural and smaller urban centres with a focus on fidelity, uptake, use and changes to physical activity levels.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to research ethics board requirements but are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank Southwestern Public Health for their support in administering and organizing the ACT-i-Pass community forums. We also thank program service providers and local school boards (the London District Catholic School Board, Thames Valley District School Board, Conseil Scolaire Viamonde and Conseil Scolaire Catholique Providence) for their continued support of the ACT-i-Pass Program. We also thank the parents and organization representatives who took the time to attend the community forum and participate in a community forum discussion. Finally, we thank our research assistant, Samantha Lotzkar, who reviewed the transcripts for accuracy and acted as a secondary analyst.

This research was funded by the Lawson Foundation Miggsie Fund’s Community Grants (GRT 2022-49).

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E.O., J.G., J.I., J.S. and P.T. conceptualized the study. E.O. and P.T. developed the community forum guides. E.O. recruited study participants, moderated the community forums, conducted the analysis, and wrote the original manuscript draft. J.G., J.I., J.S. and P.T. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jason Gilliland .

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The study was approved by Western University’s Non-Medical Research Ethics Board (REB #103954). Written and oral informed consent was obtained from all service providers and parents who participated in this study. All methods were carried out in accordance with relevant guidelines and regulations.

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Ostermeier, E., Gilliland, J., Irwin, J.D. et al. Developing community-based physical activity interventions and recreational programming for children in rural and smaller urban centres: a qualitative exploration of service provider and parent experiences. BMC Health Serv Res 24 , 1017 (2024). https://doi.org/10.1186/s12913-024-11418-w

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Received : 22 April 2024

Accepted : 09 August 2024

Published : 03 September 2024

DOI : https://doi.org/10.1186/s12913-024-11418-w

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