ATLAS.ti
ATLAS.ti is a qualitative data analysis software that helps you uncover actionable insights by organizing, coding, and analyzing unstructured data like text, audio, video, and survey responses.
Dedoose
Dedoose is a cross-platform software application for analyzing qualitative and mixed methods research data using integrated text, video, audio, image, and spreadsheet management tools to uncover deep data insights.
Quick Comparison
| Feature | ATLAS.ti | Dedoose |
|---|---|---|
| Website | atlasti.com | dedoose.com |
| Pricing Model | Subscription | Subscription |
| Starting Price | $10/month | $10.95/month |
| FREE Trial | ✓ 5 days free trial | ✓ 30 days free trial |
| Free Plan | ✘ No free plan | ✘ No free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 1993 | 2008 |
| Headquarters | Berlin, Germany | Manhattan Beach, USA |
Overview
ATLAS.ti
ATLAS.ti is a workbench for the qualitative analysis of large bodies of textual, graphical, audio, and video data. You can manage your entire research project in one place, whether you are conducting grounded theory, content analysis, or mixed methods research. The platform allows you to import data from diverse sources including social media, reference managers, and survey tools to begin your discovery process immediately.
You can utilize automated coding tools powered by AI to speed up your initial analysis and identify patterns across thousands of documents. The software is designed for researchers in academia, market research, and user experience who need to transform complex, unstructured information into structured findings. It supports collaborative work across Windows, Mac, and Web platforms, ensuring your team stays synchronized throughout the research lifecycle.
Dedoose
Dedoose is a collaborative environment designed for researchers who need to manage and analyze complex qualitative and mixed methods data. You can import various media types, including text, audio, video, and images, and then link them to quantitative descriptors for a holistic view of your project. It eliminates the need for expensive desktop installations by providing a secure, browser-based workspace where your entire team can code and analyze data simultaneously.
You can visualize your findings through interactive charts and plots that bridge the gap between qualitative stories and quantitative trends. Whether you are conducting academic research, market analysis, or medical evaluations, the platform helps you organize thousands of excerpts and codes efficiently. It simplifies the research process by offering flexible monthly access, ensuring you only pay for the months you actually use the software for your projects.
Overview
ATLAS.ti Features
- AI Coding Save hundreds of hours by letting AI automatically suggest codes and themes across your entire dataset in minutes.
- Multimedia Analysis Analyze text, images, audio, and video files directly within the platform without needing to transcribe every word first.
- Visual Networks Create digital mind maps and network diagrams to visualize the complex relationships between your codes and data segments.
- Sentiment Analysis Automatically detect the emotional tone of your documents to quickly categorize positive, negative, or neutral feedback.
- Survey Import Import open-ended survey responses from tools like SurveyMonkey and automatically turn them into coded data for immediate analysis.
- Team Collaboration Work together on the same project with your colleagues using integrated cloud-sharing and real-time synchronization features.
Dedoose Features
- Mixed Methods Integration. Link your qualitative excerpts directly to demographic or survey data to see how different groups respond to specific themes.
- Real-Time Collaboration. Work with your entire research team on the same project simultaneously with automatic syncing and version control.
- Interactive Data Visualizations. Generate dynamic charts and graphs that allow you to click through directly to the underlying qualitative source text.
- Blind Coding & Reliability. Test inter-rater reliability with built-in training modules to ensure your team is coding consistently across large datasets.
- Multimedia Support. Upload and analyze diverse media formats including video, audio, images, and PDF documents within a single interface.
- Secure Cloud Storage. Protect your sensitive research data with SOC 2 compliant encryption and secure access controls from any web browser.
Pricing Comparison
ATLAS.ti Pricing
- Browser-based access
- Fully functional coding
- AI-powered auto-coding
- Real-time collaboration
- Project export to desktop
- Secure cloud storage
- Everything in Web, plus:
- Windows and Mac versions
- Advanced network visualizations
- Full multimedia analysis
- Inter-coder agreement tools
- Offline functionality
Dedoose Pricing
- Full feature access
- First month free
- Pay only when you log in
- Unlimited projects
- Secure cloud storage
- Requires student verification
- Everything in Student, plus:
- Discounted rate for 2-5 users
- Collaborative workspace
- Project-level security
- Technical support access
- No long-term contracts
Pros & Cons
ATLAS.ti
Pros
- Superior handling of diverse file formats including video
- Powerful AI tools significantly speed up initial coding
- Excellent visual mapping tools for conceptualizing data
- Cross-platform compatibility between Windows and Mac systems
- Strong customer support and extensive training documentation
Cons
- Steep learning curve for the advanced desktop version
- Web version lacks some advanced features of desktop
- Pricing can be high for non-academic professionals
Dedoose
Pros
- Affordable monthly pricing for project-based research
- Excellent tools for mixed methods analysis
- Easy collaboration for remote research teams
- Web-based access requires no software installation
- Strong visual reporting and charting capabilities
Cons
- Interface feels dated compared to modern apps
- Occasional lag when processing very large datasets
- Learning curve for complex mixed methods features