手法を比較

	Repeat Victimization Analysis	Victimization Survey Method
分野	Criminology	Criminology
系統	Regression model	Process / pipeline
提唱年	1993	1973
提唱者	Ken Pease, Graham Farrell & colleagues	U.S. President's Commission on Law Enforcement / NCVS and CSEW programs
種類	Time-to-event analysis of elevated short-term re-victimization risk	Probability-sample survey measuring crime victimization including unreported offenses
原典	Tseloni, A., & Pease, K. (2003). Repeat personal victimization: 'Boosts' or 'flags'? British Journal of Criminology, 43(1), 196–212.	Lynch, J. P., & Addington, L. A. (Eds.) (2007). Understanding Crime Statistics: Revisiting the Divergence of the NCVS and UCR. Cambridge University Press. ISBN: 9780521862042
別名	Repeat Victimisation Analysis, Re-Victimization Risk Analysis, Multiple Victimization Analysis, Time-Course of Repeat Victimization	Crime Victimization Survey, Victimisation Survey Method, Crime Survey Methodology, Self-Report Victimization Survey
関連	4	3
概要	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.	The victimization survey method measures crime by asking a representative sample of households or individuals what they have actually experienced, rather than counting offenses recorded by police. Pioneered in the United States with the National Crime Victimization Survey (NCVS) and developed in Britain as the Crime Survey for England and Wales (CSEW), it captures the 'dark figure' of crime that never reaches the authorities, using a rotating-panel design with screening questions, detailed incident forms, bounding interviews, and weighted estimation.