방법 비교

	Repeat Victimization Analysis	Recidivism Survival Analysis
분야	Criminology	Criminology
계열	Regression model	Survival analysis
기원 연도	1993	1988
창시자	Ken Pease, Graham Farrell & colleagues	David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application)
유형	Time-to-event analysis of elevated short-term re-victimization risk	Time-to-event regression for reoffending
원전	Tseloni, A., & Pease, K. (2003). Repeat personal victimization: 'Boosts' or 'flags'? British Journal of Criminology, 43(1), 196–212.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.
별칭	Repeat Victimisation Analysis, Re-Victimization Risk Analysis, Multiple Victimization Analysis, Time-Course of Repeat Victimization	Time-to-Recidivism Modeling, Recidivism Hazard Modeling, Failure-Time Analysis of Reoffending, Survival Analysis of Reoffending
관련	4	4
요약	Repeat victimization analysis studies the sharply elevated short-term risk that the same target — a household, person, or business — is victimized again soon after an initial offense. Established as a crime-prevention priority by Ken Pease, Graham Farrell, and colleagues in the early 1990s, it models the time-course of re-victimization, quantifies how the hazard of a repeat decays as time passes since the first event, and asks whether repeats arise because an event 'boosts' future risk or because stable target features 'flag' that risk.	Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods — Kaplan-Meier curves, Cox proportional-hazards regression, and split-population models — to answer not just whether someone recidivates but how quickly and what raises or lowers that risk over time.