This chapter explores the use of Curriculum Vitae (CV) analysis in higher education research, particularly focusing on social network analysis and econometric models for processing academic CV data. As a mixed-methods quantitative research approach, CV-based social network analysis, CV-based regression analysis, and the combined application of both hold significant potential for integrating education research with fields such as demography, sociology, economics, geography, and the science of science. Drawing on recent empirical data, the chapter illustrates the promising applications of academic CV analysis in investigating phenomena such as academic inbreeding, the mobility of doctoral graduates, and the career development of university faculty. With careful attention to data collection, sample composition, variable selection, and model construction, the chapter offers a detailed account of decision-making behaviors and interaction mechanisms within the academic labor market. It highlights the objectivity, necessity, and feasibility of using large-scale, non-intrusive data for interdisciplinary research in higher education and beyond.