Vertaile menetelmiä

	Conflict Recurrence Analysis	Peace Duration Analysis	State Capacity Measurement	Selviytymisanalyysi
Tieteenala	International Relations	International Relations	International Relations	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Survival analysis	Survival analysis	Process / pipeline	Process / pipeline
Syntyvuosi	2004	2003	2010	1958
Kehittäjä	Civil-war recurrence literature (e.g., Barbara F. Walter)	Conflict-duration literature (e.g., Caroline Hartzell & Matthew Hoddie on post-civil-war peace)	State-capacity literature; measurement synthesis by Cullen Hendrix	Edward L. Kaplan and Paul Meier
Tyyppi	Survival/repeated-events analysis of renewed conflict	Time-to-event (survival) analysis of peace spells	Measurement of the state's ability to penetrate, extract, and enforce	Method
Alkuperäislähde	Walter, B. F. (2004). Does conflict beget conflict? Explaining recurring civil war. Journal of Peace Research, 41(3), 371–388.	Hartzell, C., & Hoddie, M. (2003). Institutionalizing peace: Power sharing and post-civil war conflict management. American Journal of Political Science, 47(2), 318–332.	Hendrix, C. S. (2010). Measuring state capacity: Theoretical and empirical implications for the study of civil conflict. Journal of Peace Research, 47(3), 273–285.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Rinnakkaisnimet	Recurring Civil War Analysis, Conflict Relapse Analysis, Repeated-Conflict Survival Analysis, Conflict Recidivism Analysis	Duration of Peace Analysis, Post-Conflict Peace Survival Analysis, Peace Spell Analysis, Time-to-Conflict-Recurrence Analysis	Measuring State Capacity, State Strength Measurement, Bureaucratic and Fiscal Capacity Measures, State Capacity Indicators	Kaplan-Meier analysis, Cox regression, TTE analysis
Liittyvät	3	3	3	3
Tiivistelmä	Conflict recurrence analysis studies why and when conflicts that have ended return, treating renewed war as a time-to-event outcome. Most civil wars in recent decades have occurred in countries with a prior war, making recurrence a central puzzle. Using survival and repeated-events models — as in Barbara Walter's (2004) analysis of recurring civil war — researchers model the hazard that a post-conflict country relapses into violence as a function of how the war ended and the underlying conditions, while accounting for the fact that the same country can experience multiple conflict spells.	Peace duration analysis applies survival (time-to-event) methods to study how long peace lasts after a conflict ends and what makes it endure or collapse. The unit is the post-conflict peace spell, observed from a settlement or cessation until conflict recurs or the observation is censored. Modeling the hazard that peace fails as a function of how the conflict ended and the structural conditions — as in Hartzell and Hoddie's (2003) study of power-sharing after civil war — reveals which arrangements, such as institutionalized power sharing or peacekeeping, lengthen the survival of peace.	State capacity measurement is the effort to quantify how able a state is to do the things states do — raise revenue, administer territory, and enforce its will — a variable central to explaining civil conflict, development, and governance. Because capacity is abstract, researchers operationalize it through observable indicators of fiscal, bureaucratic, and coercive strength. Hendrix (2010) systematically compared fifteen common operationalizations, using factor analysis to show that they reduce to a few underlying dimensions, and clarified which measures best capture the capacity relevant to conflict.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.