Developing an Analytical Framework for Multi-Level Data Analysis in Qualitative Inductive Studies

This paper proposes a multi-level framework for qualitative data analysis to address the challenges researchers face when integrating and synthesising findings from diverse data sources. While existing literature offers strategies for single-method qualitative analysis, few provide guidance on managing multiple data types within a unified framework. Drawing from an empirical example and relevant literature, the proposed framework presents a systematic approach that assists researchers in navigating complex data analysis, progressing from initial coding to theory development. The framework is structured into four levels: organising data, coding and generating themes, answering research questions, and theory construction. This stepwise process ensures both methodological rigour and flexibility, enabling researchers to iteratively explore relationships across datasets. Through an empirical example, we demonstrate how this framework facilitates effective triangulation, enhancing the credibility and coherence of findings. This approach is particularly beneficial for novice researchers, such as doctoral students, seeking to generate empirically grounded theories. The paper concludes with implications for future research and practical recommendations for applying the framework across various disciplines.