Staying True to the Data A Visual and Quantitative Approach to Showcase Coding Rigour and Theoretical Saturation

Main Article Content

Ilse Doyer
Michael K. Ayomoh

Abstract

This paper describes a data-based and transparent approach that was followed during classic grounded theory data analysis to ensure rigor and theoretical saturation. Firstly, a visual approach was followed to ensure that the theory discovery stayed as close to the story told by the data set as possible, thus ensuring both rigor and accuracy. Secondly, process data in the form of a coding rate time study and theoretical contribution rate, were tracked to monitor theoretical saturation objectively. The presented approach offers a novel approach to showcasing classic grounded theory rigor during the data coding process, as well as a way to prove that theoretical saturation was reached. The approach presented in this paper thus provides grounded theory researchers with a way in which to both ensure and defend the rigor and accuracy of their research. 

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How to Cite
Doyer, I., & Ayomoh, M. K. (2025). Staying True to the Data: A Visual and Quantitative Approach to Showcase Coding Rigour and Theoretical Saturation. Grounded Theory Review, 24(1), 80–98. Retrieved from https://groundedtheoryreview.org/index.php/gtr/article/view/295
Section
Research Articles
Author Biographies

Ilse Doyer, Department of Industrial and Systems Engineering, University of Pretoria

Ilse Doyer is a lecturer and PhD student in the Department of Industrial and Systems Engineering at the University of Pretoria. She has more than 20 years of industry experience focusing on operations excellence in the manufacturing, mining, agricultural and services sectors – in South Africa and Europe. Her research focuses on the cross-section between operations excellence and organisational behaviour.

Michael K. Ayomoh, Department of Industrial and Systems Engineering, University of Pretoria

Professor Michael K. Ayomoh is an Associate Professor with the Department of Industrial and Systems Engineering at the University of Pretoria. His research interests spread across: Management Systems covering: Systems Engineering Design, Systems Complexity Analysis and Evaluation, Multi-objective Decision Support Systems, Optimization and Reliability of Systems; Artificial Intelligent Systems including- Robotics research, Machine Learning, Artificial Neural Networks, Manufacturing Systems Automation, Haptics and Smart Control Systems amongst others.

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