Computer-assisted qualitative data analysis software


Computer-assisted 'qualitative data analysis software' offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, grounded theory methodology, etc.

Definition

CAQDAS is used in psychology, marketing research, ethnography, public health and other social sciences. The CAQDAS networking project lists the following tools a CAQDAS program should have:
  • Content searching tools
  • Code grouping tools
  • Linking tools
  • Mapping or networking tools
  • Query tools
  • Alternative visual representation tools
  • Writing and annotation tools

    Comparison of CAQDAS software

ApplicationTypeLicenseSourceLast ReleaseAnalysesOS SupportedTools
AquadClient2017-02Text, Audio, Video, GraphicsWindowsCoding, Sequence Analysis, Exploratory Data Analysis
Atlas.tiClient2022-07Text, Audio, Video, Graphic, Social NetworksWindows, macOS, iOS, Android, Cloud Coding, Aggregation, Query, Visualisation
CassandreWeb-based/server2018-10-09TextAll Coding
CLANClient2019-06-10TextWindows, macOS, LinuxCoding
Coding Analysis Toolkit Web-based2014-06-28TextAll Coding
CompendiumClient2014-02TextAll Coding
DovetailWeb-based2024-10-14Text, Audio, VideoAll Coding, Query, Visualisation
DedooseClient2022-06-07Text, Audio, VideoAll Coding, Query, Visualisation, Statistical Tools
ELANClient2018-12-12Audio, VideoWindows, macOS, LinuxCoding
KH CoderClient2015-12-29TextWindows, macOS, Linux
MAXQDAClient2019-02-05Text, audio, video, pictures, webpages, social networksWindows, macOSCoding, Aggregation, Query, Visualisation, Statistical Tools
NVivoClient2023-04Text, audio, video, pictures, webpagesWindows, macOSCoding, Aggregation, Query, Visualisation
QDAcityWeb-based2026-01-28Text, Audio, PDFWindows, macOS, iOS, Android, LinuxCoding
QDA MinerClient2016-11Windows
QDA Miner LiteClient2017-01-12TextWindowsCoding
QiqqaClient2016-09Windows, Android
Quantitative Discourse Analysis Package Client2019-01-02TextWindows, macOS, LinuxWord extracting, statistical analysis, visualization
QuirkosClient / Web-based2023-01TextWindows, macOS, Linux, All Coding, Query, Visualisation
RQDA Client2018-03TextWindows, macOS, LinuxCoding, Aggregation, Query, Visualisation
TaguetteClient / Web-based2025-11-10TextWindows, macOS, LinuxCoding
TransanaClient2017-11Text, Audio, VideoWindows, macOSCoding
XSightClient2006 Windows

Project Exchange Format

In 2019, the Rotterdam Exchange Format Initiative launched a new open exchange standard for qualitative data called QDA-XML, however, the Computer Assisted Qualitative Data Analysis Network Project had been formally established in 1994. The aim is to allow users to bring coded qualitative data from one software package to another. Support was initially adopted by Atlas.ti, QDA Miner, Quirkos and Transana, and has since been implemented into Dedoose, MAXQDA, NVivo and more. Although this was not the first standard to be proposed, it was the first to be implemented by more than one software package, and came as the result of a collaboration between vendors and community representatives from the research community. Previously there was very little capability to bring data in from other software packages.

Training

The CAQDAS Network Project hosts events on the use of CAQDAS packages for qualitative and mixed-methods analysis. They include:
  • fee-based in-person short courses
  • open-registration Webinars designed to raise awareness
  • open-registration Webinars that are methodological or pedagogical in nature
  • podcasts

    Pros and cons

Such software helps to organize, manage and analyse information. The advantages of using this software include saving time, managing huge amounts of qualitative data, having increased flexibility, having improved validity and auditability of qualitative research, and being freed from manual and clerical tasks. Concerns include increasingly deterministic and rigid processes, privileging of coding, and retrieval methods; reification of data, increased pressure on researchers to focus on volume and breadth rather than on depth and meaning, time and energy spent learning to use computer packages, increased commercialism, and distraction from the real work of analysis.