Módszerek összehasonlítása

	State Capacity Measurement	Túlélemzési módszerek
Tudományterület	International Relations	Kutatási statisztika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2010	1958
Megalkotó	State-capacity literature; measurement synthesis by Cullen Hendrix	Edward L. Kaplan and Paul Meier
Típus	Measurement of the state's ability to penetrate, extract, and enforce	Method
Alapmű	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.
Alternatív nevek	Measuring State Capacity, State Strength Measurement, Bureaucratic and Fiscal Capacity Measures, State Capacity Indicators	Kaplan-Meier analysis, Cox regression, TTE analysis
Kapcsolódó	3	3
Összefoglaló	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.