Abstract
As an effective approach to utilizing existing data resources, secondary analysis has gained increasing attention in fields such as social sciences, education, and public policy in recent years. This paper systematically outlines four key stages of secondary analysis: identifying and accessing data sources, evaluating and understanding data, reconstructing and preparing data, and conducting new analyses and interpretations. It emphasizes the theoretical judgment and methodological rigor required at each stage, particularly highlighting the importance of data quality assessment, variable reconceptualization, cross-dataset integration, and theoretical reinterpretation for ensuring research validity. Methodologically, the study integrates techniques from statistics, econometrics, and data mining, demonstrating the dual potential of secondary analysis in both theory verification and academic innovation. The purpose of this paper is to provide a systematic methodological reference for conducting high-quality secondary research and to promote a transition from mere “reuse” to meaningful “rediscovery” in data-driven knowledge production.
