Applying MAXQDA in a Grounded Theory Study: Evaluation of an Enterprise Education Research Project

This paper explores how the analytic software MAXQDA (VERBI Software) can significantly facilitate a grounded theory (GT) study that utilizes qualitative data obtained through in-depth interviews. The research describes a recent study examining the impact of enterprise education on students pursuing Professional Higher Education (PHE) at an extensive vocational educational institution in the EU state of Malta. This paper aims to demonstrate how MAXQDA’s various tools can be effectively applied to the grounded theory methodology. The GT methodology encompasses systematic steps for data collection, coding and analysis, sampling, memo writing, and constant comparison, leading to conceptual categories and substantive theory building. The study consisted of 14 cases, combining the in-depth contributions of entrepreneurship students and academics accessed via convenience, purposive, and theoretical sampling. A sequence of coding paradigms was applied, atomizing the data into unique data incidents that were then analyzed through a flowing emergent process of reflexive memoing and utilizing constant comparison of data incidents to incidents, codes to codes, codes to categories, and categories to categories, until an early substantive theory emerged. Various MAXQDA tools facilitated this process, including creative and hierarchical coding, code matrix browser, MAXMaps, two-case and hierarchical case models, and summary grids. A parsimonious model emerged from the research, supported by a set of propositions explaining the entrepreneurial decision-making inclinations of the students towards pedagogical learning, enterprise learning, research acumen, self-efficacy, and employability. The study provided new insights into how students can successfully build upon their self-efficacy through integrating learning incidents, critical self-reflection, and proficient tutor support. MAXQDA allowed for an evidence-based research process that provided an audit trail, identifying how each data incident acquired within the research was analyzed, contributing to a final process-based substantive model.