Content Analysis as a Foundational Methodology in Music Research - Marsel Ridky Maulana; Regina Darmawanti; Raffa Arya Saputra (OSTINATO: Journal of Music Study and Research, 2025)
This study aims to examine a methodological framework based on content analysis for music research. Although content analysis has been widely applied across various scientific disciplines, its implementation within music studies remains sporadic, unsystematic, and lacks an operational framework that can be widely adopted. Accordingly, this research formulates a central inquiry: can content analysis be positioned as a foundational methodology in music research, and how can its methodological structure be scientifically constructed? The research employs a conceptual–qualitative approach using theoretical analysis and literature synthesis, drawing primarily on Krippendorff’s theoretical formulations on content analysis and Scopus-indexed empirical publications that apply this method within music-related contexts. The analytical processes were conducted through unitizing, sampling, coding, inferencing, and validation procedures, which were subsequently mapped onto music research settings. The findings indicate that content analysis possesses both epistemological flexibility and procedural structure, enabling its applicability in musicology, ethnomusicology, music education, music psychology, performance studies, as well as broader domains including the humanities and technology-based research. The novelty of this study lies in the formulation of a comprehensive methodological model that positions music as a representation of meaning rather than merely an acoustic phenomenon. In conclusion, content analysis warrants consideration as a primary methodological approach in music research, as it effectively bridges interpretive frameworks and data-driven approaches. Future research recommendations include the empirical testing of the proposed model across diverse genres and research contexts, as well as the exploration of its integration with computational methods such as AI based approaches and Music Information Retrieval.