| Submission rejected on 19 April 2024 by Qcne (talk). This topic is not sufficiently notable for inclusion in Wikipedia. Rejected by Qcne 7 months ago. Last edited by Caeid 37 days ago. |  |
| Submission declined on 12 April 2024 by CNMall41 (talk).CNMall41 7 months ago. |  |
| Submission declined on 11 April 2024 by Stuartyeates (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by Stuartyeates 7 months ago.
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| Submission declined on 25 May 2023 by Stuartyeates (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by Stuartyeates 17 months ago.
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| Submission declined on 8 May 2023 by KylieTastic (talk). This submission's references do not show that the subject qualifies for a Wikipedia article—that is, they do not show significant coverage (not just passing mentions) about the subject in published, reliable, secondary sources that are independent of the subject (see the guidelines on the notability of web content). Before any resubmission, additional references meeting these criteria should be added (see technical help and learn about mistakes to avoid when addressing this issue). If no additional references exist, the subject is not suitable for Wikipedia. Declined by KylieTastic 18 months ago. | |
Comment: Sorry, this simply does not seem notable and it reads like an advert. Qcne (talk) 20:27, 19 April 2024 (UTC)
- Comment: Please focus on the notability guideline at WP:GNG. While you did remove bad sources prior to resubmitting, you did not add reliable secondary sources which are needed to show notability. There is a lot of content that is not currently sourced. CNMall41 (talk) 00:23, 12 April 2024 (UTC)
- Comment: References need to be independent. Stuartyeates (talk) 06:34, 11 April 2024 (UTC)
- Comment: We need independent secondary sources. Stuartyeates (talk) 09:22, 25 May 2023 (UTC)
| Developer(s) | Professorship for Open-Source Software of Friedrich Alexander University. |
|---|---|
| Operating system | All |
| Type | Qualitative Data Analysis Qualitative Research |
| License | Proprietary |
| Website | qdacity |
QDAcity [1]is a web application for qualitative research developed by a group of scientists at University of Erlangen–Nuremberg (FAU). Its mission is to empower students and researchers by facilitating qualitative data analysis processes, thereby enhancing productivity and proficiency within their fields of study, and development of method competency through teaching.
Description and features
editQDAcity is a cloud based web application for qualitative data analysis with a particular focus on interview analysis and collaborative QDA.
Users can code text data and PDFs concurrently, and it offers an assisted interview transcription feature.
QDAcity is also used in teaching of qualitative data analysis[2]
QDAcity offers cross-platform compatibility, ensuring seamless utilization across various operating systems. This caters to the diverse needs of users, irrespective of their preferred operating system. Whether on Windows, macOS, Linux, or any other platform, QDAcity has a unified experience, fostering convenience and accessibility for individuals navigating across different systems. This adaptability underscores the commitment to inclusivity and user-centric design, enhancing the accessibility and usability for a broad spectrum of users.
Functionality
editQDA for students: QDAcity is a tool for qualitative data analysis, particularly for students completing their final thesis. With QDAcity, users and researchers can efficiently code their data and collaborate with each other and their supervisor. QDAcity offers a user-friendly interface tailored for beginners, emphasizing essential features without unnecessary complexity.
QDA for research groups: QDAcity facilitates rigorous analysis of scientific data, providing a professional and intuitive user experience. The platform offers extensive collaboration features, allowing seamless teamwork among researchers. Users can create and manage user groups, enabling concurrent collaborative coding of the same dataset. Additionally, QDAcity provides metrics for assessing Intercoder Agreement, ensuring accuracy and consistency among coders.
QDA for marketing: Market research shares similarities with qualitative theory-building research. Whether it's understanding brand perception, evaluating strategies, or testing new products, QDAcity offers a solution for analyzing data with enhanced methodological rigor, improved documentation, and ultimately, more dependable results.
Teaching QDA: Teaching QDA to students can be challenging due to its complexity, often resulting in limited course capacities and difficulty integrating it into degree programs. QDAcity addresses these challenges by enabling the instruction of larger student cohorts simultaneously, while also streamlining grading processes through automation.
History
editDevelopment on QDAcity started in 2016 during a research project at the Professorship for Open-Source Software at University of Erlangen-Nürnberg[3], on the application of qualitative data analysis for requirements engineering[4] in the software development process.
In 2019-2020 a team started forming at the research group developing the software, whose goal it was to start building a product out of the existing work.
The latest milestone in its journey was marked by the release of the product on 2024-04-05. This event reflects its dedication to continuous improvement and innovation, enabling real time collaboration.
See also
editReferences
edit- ^ "QDAcity website".
- ^ Kaufmann, Andreas; Riehle, Dirk; Krause, Julia; Harutyunyan, Nikolay (January 3, 2023). A Solution for Automated Grading of QDA Homework. Department of IT Management, Shidler College of Business, University of Hawaii. hdl:10125/102635. ISBN 978-0-9981331-6-4 – via scholarspace.manoa.hawaii.edu.
- ^ "Professorship for Open-Source Software".
- ^ Kaufmann, Andreas; Riehle, Dirk (March 1, 2019). "The QDAcity-RE method for structural domain modeling using qualitative data analysis". Requirements Engineering. 24 (1): 85–102. doi:10.1007/s00766-017-0284-8 – via Springer Link.
External links
edit| Open source software | |
|---|---|
| Proprietary software | |
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Make sure you add references that meet these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.