Inductive-Deductive Qualitative Data Analysis Logic in Health Sciences Research: A Framework for Analysing Qualitative Data

A true test of qualitative researchers’ competencies lies in their systematic data analysis, the ability to transform raw data into meaningful interpretations. This personal, creative, and rigorous activity requires both inductive and deductive logic in harmonising complex phrases to break ambiguity and clarify meanings for both amateurish and expert qualitative researchers. This integrative review used Whittemore and Knafl’s framework as a guide aimed to provide harmonised literature on qualitative data analysis for qualitative researchers to clarify the use of inductive and deductive analysis logic. The literature was searched from Scopus, CINAHL, and the Web of Science. Relevant studies were retrieved using qualitative data analysis, thematic analysis, qualitative content analysis, inductive analysis, deductive analysis, and coding as search terms and incorporated citation chaining with Google Scholar. The literature search explored dates ranging from 2000 to 2024, focusing on the English language to retrieve books, book chapters, and methodological papers that were applicable to the studies. The inductive dominant thematic analysis performed was guided by the question: What underlines the epistemological assumptions of inductive and deductive logic in qualitative data analysis? Twenty-six papers were included, with six themes emerging that include planning the research, coding fundamentals, inductive and deductive logic, content analysis, manifest and latent analysis, and thematic analysis. The review found a dual-logic approach to qualitative data analysis in health sciences research appropriate to provide valuable insight to further refine and improve the field of data analysis. Abductive or retroductive analysis is identified as the best fit for qualitative data analysis because researchers are either dominant in inductive and dormant in deductive or vice versa.