Qualitative data analysis (QDA) using software tools like ATLAS.ti, Dedoose, HyperRESEARCH, MAXQDA, etc., involves systematically examining and interpreting non-numerical data to identify patterns, themes, and insights. Qualitative data is often rich and complex, consisting of textual, visual, or audio information gathered through interviews, focus groups, observations, or content analysis.
Steps for qualitative data analysis using software:
- Data importation: The first step is importing qualitative data into the software. This can include text documents, images, audio files, or videos.
- Coding: Coding is a fundamental process in qualitative analysis. It involves labeling or categorizing data segments based on themes, concepts, or patterns.
- Data exploration: Researchers explore the data by navigating coded segments to understand the content and context. Visualization tools within the software may help in identifying patterns or relationships.
- Memoing: Researchers often make notes or memos to record thoughts, ideas, or insights during the analysis process. Some software tools have dedicated features for creating and linking memos to coded segments.
- Querying and searching: QDA tools provide features to search for specific codes, words, or phrases within the data, which helps retrieve relevant information for deeper analysis.
- Theme development: As coding progresses, themes and patterns start to emerge. Researchers can group related codes into themes, allowing for higher abstraction and interpretation.
- Visualizations: Some QDA tools offer visualization options, such as word clouds, charts, or network diagrams, to help researchers better understand the relationships and frequencies within the data.
- Report writing: Researchers can use the findings from the analysis to generate reports. Some software tools offer features to export data, visualizations, and interpretations for inclusion in research documents.
Different QDA software tools may have unique features and interfaces, but the general process involves these key steps. The choice of software often depends on the researcher’s preferences, the nature of the data, and the specific requirements of the research project.
List of recommended resources #
For a broad overview #
This resource guide by NYU libraries provides comparison charts, video tutorials, and other data services tutorials and guidance for researchers interested in conducting qualitative data analysis using software.
This YouTube tutorial by Quirkos – Simple Qualitative Analysis Software gives a broad overview into qualitative methodologies of data analysis softwares, their various uses as well as criticisms.
For in depth understanding #
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). Some of the major software programs discussed by Silver and Lewins include ATLAS.ti, DEDOOSE, HyperRESEARCH, MAXQDA, NVivo etc.
This paper by M. L. Jones addresses the use of software for the purpose of qualitative analysis that can provide tangible benefits. Jones writes that appropriate software can shorten analysis timeframes, can provide more thorough and rigorous coding and interpretation, and provide researchers with enhanced data management. Jones particularly focuses on the data analysis software QSR NVivo.
Case study #
This study by Senel Elaidi and Nazli Sila Yerliyurt attempts to evaluate the views of senior preservice preschool teachers on the efficacy of drama activities in their field experience in terms of the effect of students’ learning, socialization, individual or group work skills and school connectedness. The data collected was analyzed using MAXQDA-11, qualitative data analysis software, and descriptive analysis technique.
This article by Susanne Friese aims to find out whether the use of software influences the ways we analyze data and if so how. Friese’s argument is that software has a greater influence when users lack methodological know-how. She illustrates her argument by analyzing the financial crisis data with ATLAS.ti.