Manual Qualitative Data Coding Using MS Word for Students and Early Career Researchers in Resource-Constrained Settings

Abstract

In recent years, the field of qualitative data collection, management, and analysis has undergone significant evolution, with researchers worldwide increasingly leveraging technological advancements to streamline and simplify their practices. Despite the advantages of using qualitative data coding software, several challenges persist, particularly in resource-constrained settings. Issues such as the high cost of software licenses, the time required to learn and use these tools, and the complexity of advanced features often hinder researchers, especially students and early career researchers in low-income contexts, and contribute to the generation of poor-quality research evidence. This paper aims to provide a simplified, step-by-step guide to manual qualitative data coding using Microsoft Word, a widely accessible tool in low-income contexts. It is designed to support students and early-career researchers in low-income settings, helping them to conduct effective qualitative data management and analysis without the need for expensive software. We provide a five-step detailed guide for qualitative data coding using Microsoft Word, based on a practical example from previous research on maternal and child health in rural Tanzania. We also present a hypothetical example of a student research study focusing on the drivers behind pursuing a Bachelor of Science in Nursing degree. The process begins with examining the research objectives and questions to develop preliminary themes and sub-themes, identifying relevant data sources within study interview guides, and creating guided transcription and coding templates tailored to each interview guide, followed by data coding by integrating interview data into a structured template. Practical insights are provided on transforming coded data into meaningful interpretations and analyses. This systematic approach aims to facilitate effective qualitative data management and analysis using accessible tools like Microsoft Word, thereby supporting students and early career researchers in conducting small-scale studies effectively.