From the Guidelines
Statistical analysis for qualitative research should primarily involve thematic analysis, content analysis, discourse analysis, or grounded theory methods, rather than statistical tests, as recommended by recent studies 1. For qualitative data analysis, researchers should use these approaches to systematically code textual or observational data and identify patterns, themes, and relationships. Some key points to consider in qualitative research analysis include:
- Using software tools like NVivo, ATLAS.ti, or MAXQDA to help organize and analyze qualitative data 1.
- Enhancing rigor through triangulation, member checking, and maintaining an audit trail of analytical decisions 1.
- Incorporating descriptive statistics in mixed-methods approaches to summarize demographic information or quantify the frequency of themes, but keeping the core analysis interpretive 1. The goal of qualitative analysis is to develop a rich, contextual understanding of phenomena, focusing on meaning-making and interpretation rather than numerical measurement. Recent studies, such as the one published in 2022 in the Journal of Medical Internet Research 1, support the use of thematic analysis with an inductive approach for qualitative data. Additionally, a 2021 study published in Implementation Science 1 highlights the importance of using qualitative methods for data collection and analysis to identify barriers and facilitators. More recent studies, like the 2025 publication in the British Journal of Sports Medicine 1, recommend using qualitative or mixed-methods study designs when appropriate, ensuring potential biases are addressed. Overall, the most recent and highest quality studies support the use of thematic analysis and other qualitative methods for statistical analysis in qualitative research.
From the Research
Statistical Analysis for Qualitative Research
- Qualitative research methods are being used more regularly in various fields, including health professions education and pharmacy education 2
- Thematic analysis is a widely used method of qualitative data analysis, but it is often misunderstood and requires a rigorous approach to ensure systematic and thorough analysis 2, 3
- Directed qualitative content analysis is another approach that provides a stepwise method of data analysis, consisting of 16 steps to ensure a reliable, transparent, and comprehensive method for qualitative researchers 4
Data Analysis Process
- The data analysis phase in qualitative research often overlaps with the data collection phase, requiring the researcher to move back and forth between the two phases to create and explain the findings 5
- Data analysis in qualitative research involves a creative process, where the researcher explores and reflects on the meaning of the data 5
- Transparency in the data analysis process is integral to determining the evidence that is generated, and rigorous and systematic analysis of qualitative data is essential to produce high-quality research 6
Steps in Qualitative Data Analysis
- A model of qualitative data analysis can be outlined in five steps: compiling, disassembling, reassembling, interpreting, and concluding 2
- Directed qualitative content analysis involves a 16-step method of data analysis 4
- Thematic data analysis involves four steps: immersion, coding, categorising, and generation of themes 6
- A six-step framework for conducting thematic analysis is also commonly used, which includes defining a theme, coding, and interpreting the data 3