How to conduct data analysis using the case study method?

Data analysis using the case study method involves an in-depth, interpretative examination of a bounded instance or phenomenon (the 'case'), leveraging diverse qualitative and often quantitative data sources. Its feasibility rests on triangulating evidence to build a rich, contextual understanding rather than seeking statistical generalizability. This approach necessitates rigorously defining the case boundaries and context. It relies heavily on gathering multiple evidence types like interviews, observations, documents, and artifacts, analysed iteratively to identify patterns and themes. Key principles include constant comparison, maintaining attention to the specific case context, and permitting themes to emerge from the data. It is particularly suited for exploring complex, contemporary events or phenomena where contextual factors are paramount and "how" or "why" questions predominate. Transparency in analytical procedures and reflexivity are critical for trustworthiness. Implementation begins by formulating specific research questions guiding data collection and analysis. Researchers typically organize collected data systematically, then engage in coding (identifying key concepts) and thematic analysis within the singular case context. Software like NVivo or Atlas.ti often aids organization and coding. The process culminates in interpreting patterns and themes to construct a coherent narrative explaining the case phenomenon, focusing on its uniqueness and its contribution to theoretical understanding or practical insight. Findings derive meaning from the case's specific context.