Comparing human vs. machine-assisted analysis to develop a new approach for Big Qualitative Data Analysis

Abstract

Machine-assisted analysis offers substantial efficiency gains with acceptable accuracy trade-offs for large-scale qualitative data analysis. However, human expertise remains essential for capturing nuanced meanings, identifying emergent categories, and providing domain-specific interpretation. This hybrid approach represents a viable methodology for Big Qual research, though current AI tools' constraints in accommodating emergent classification schemes remain a limitation. Our findings establish benchmarks for future development of more flexible AI systems adapted to qualitative research paradigms.

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