Qualitative data analysis

Qualitative data analysis and quantitative data analysis differ significantly in their approach. Quantitative research is more structured, resembling an engineering challenge, where tools like regression analysis are applied after data collection to discern associations or causality. The process ideally follows a linear path from research topic to question formulation, research design, data collection, data processing, and finally, data interpretation.
In contrast, qualitative research is more fluid and iterative, likened to an artistic endeavor. It seeks to make sense of diverse data such as text, video, and photos, aiming to identify and explain underlying causal processes. The qualitative research process often involves a feedback loop where initial data collection informs analysis, which then guides further data collection. This cycle continues until theoretical saturation is achieved, where adding more cases or data doesn't provide new insights. Data analysis in qualitative research primarily involves qualitative coding, wherein large amounts of data are distilled into meaningful codes and themes. These themes are then further abstracted and theorized upon. Modern qualitative analysis is often supported by software to facilitate coding and track the evolution of the analysis.
Link to the slides: osf.io/9r6mv