QDA Miner is a software program designed to help researchers code, analyze, and visualize qualitative data. It is a versatile tool that can be used for a wide variety of research projects, from small studies to large-scale investigations.
Key Features of QDA Miner
- Import data from various sources: QDA Miner can import data from multiple sources, including text files, images, audio files, and video files, making it a versatile tool that can be used with various data types.
- Code text and images: QDA Miner allows researchers to code text and graphics, meaning researchers can assign labels or categories to different parts of their data. This can help pick out patterns and themes in the data.
- Perform statistical analysis:** QDA Miner can perform various statistical analyses of qualitative data, which can help explore relationships between different codes and identify patterns in the data.
- Well-suited for teamwork: QDA Miner has several features that make it well-suited for collaboration. For example, researchers can share their projects with others and collaborate on the coding and analysis of the data.
In addition to the features listed above, QDA Miner offers a user-friendly interface, making it a valuable tool for researchers.
List of recommended resources #
For a broad overview #
This essay by R. Barry Lewis and Steven M. Maas provides a review of the QDA software and the various changes that the different versions have added to its functionality.
This video tutorial on YouTube by Provalis Research – Text Analytics Software provides a short introduction to the qualitative data analysis software for mixed methods research.
For in depth understanding #
Published by Provalis Research, this user guide provides an in-depth understanding of how to install and start the software, the various features of the software, and how to manage projects using it.
Written by Christina Silver and Ann Lewins, this book provides an essential introduction to the practice and principles of Computer Assisted Qualitative Data Analysis (CAQDAS). Chapter 5 of this book focuses particularly on QDA Miner.
Case study #
This article by Antoine Derobertmasure and Jean E. Robertson carries out a study of training teachers in the French Community of Belgium. The theoretical foundations of the study are analyzed using two data analysis softwares: NVivo and QDA Miner.
This paper by Maidul Islam, Mincheol Kang, and Tegegne Tesfaye Haile examines how the choice of product type can influence the relationship between the level of review type and review helpfulness. The data collected is analyzed using the software QDA Miner.