Navigating data-overload challenges during data-analysis: a qualitative researcher’s experience in accounting sciences

Abstract

Purpose: Data-overload is a generic, complicated issue, affecting data-analysis processes for many researchers. The challenge emanates from the researcher collecting volumes of data that hinder their ability to engage in higher-level data-analysis processes. Despite the existence of numerous frameworks towards understanding the world, novice qualitative researchers often become overwhelmed, disregard guidelines, and neglect to clearly explain their data-analysis processes. This paper discusses the author’s experience with data overload and how the three-phase coding process and collaborative decision-making with other stakeholders helped overcome challenges. The aim is to offer new accounting science researchers a set of examples, to serve as benchmarks when dealing with data-overload in their qualitative research projects. Methodology/Approach: The author employs narrative reflection, to capture the interpretive thematic data analysis of in-depth interviews and document-based datasets derived from a completed dissertation. This paper’s discussions are based on the key factors of data-overload and data-analysis concepts provided by the literature review. Charmaz’s constructivist grounded theory principles and Creswell’s spiral framework were used to present an open, axial and focused coding process, organising data around concepts, and forming categories and themes for theory development. Implications and Value: The three-phase coding method in qualitative data-analysis can effectively help navigate data-overload and provide reliable insights concerning the collected data. The developed frameworks can assist accounting science students and supervisors, as supplementary resources in addition to existing guidance, when they experience data-overload in their qualitative research projects.