This chapter examines the scientific foundations and research methods that have shaped our understanding of serial homicide, tracing the shift from early descriptive case studies to more formalized, empirically grounded approaches. It reviews major qualitative methodologies (e.g., case studies, offender interviews, thematic and document analysis) and outlines key quantitative frameworks, including conceptually driven models within Investigative Psychology (e.g., facet theory, multidimensional scaling) and data-driven approaches informed by environmental criminology, spatial analysis, and predictive modeling. The chapter then considers the principal data sources used in serial homicide research, highlighting their respective strengths and limitations, and discusses enduring challenges such as small samples, solved-case bias, secondary data constraints, and difficulties in standardizing behavioral variables. Finally, it explores emerging trends, including applications of artificial intelligence and machine learning, cross-cultural and global comparative work, and interdisciplinary collaborations. The overarching goal is to evaluate how methodological advances continue to refine the study of serial homicide and inform more evidence-based investigative and policy practices.