1 / 11

Time Counts: A Roadmap for Understanding Duration Data in Politics and Everyday Life

Explore the impact of delay in politics and daily routines, with insights into applied Event History Analysis (EHA). Learn about key decisions, duration dependency, parametric models, Cox proportional hazards model, and more. Discover the nuances of interpreting duration data, time-varying covariates, censoring, and the positive aspects of duration analysis. Dive deeper into popular parametric models like the Weibull model and non-monotonic models. Understand the basics of EHA, duration dependence, and the s-curve in state innovation. Delve into models for unobserved heterogeneity, repeated events, and making sense of time-varying covariates. For a comprehensive guide, recommended readings include "Event History Modeling" by Janet M. Box-Steffensmeier and Bradford S. Jones, and "An Introduction to Survival Analysis Using Stata" by Mario A. Cleves, William W. Gould, and Roberto G. Gutierrez.

janice
Télécharger la présentation

Time Counts: A Roadmap for Understanding Duration Data in Politics and Everyday Life

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Time Counts…

  2. Delay in politics and everyday life… • reopening of Laurel Park • Iraqi elections

  3. Event history analysis... …a roadmap for applied users the use of EHA key decisions and how to make them interpretation

  4. Why not OLS? • the positive nature of duration data • censoring—Daschle v. McCain • time-varying covariates—Daschle as party leader

  5. The basics of EHA • the hazard rate—the rate at which units fail by t given that the unit had survived until t • absolutely continuous v. discrete treatments • given that Daschle has been in the Senate for 18 years, what are the chances he’ll be defeated in 2004?

  6. Parametric v. non-parametric models • what is duration dependence? • the s-curve in state innovation • consider parametric models when:  theory for time dependency  substantive interest in time dependency

  7. Some popular parametric models • Weibull model—monotonic baseline hazard • non-monotonic models—log-logistic and log-normal • theory and data in choosing parametric models

  8. The Cox proportional hazards model • duration dependency is left unspecified • so what does this mean in practical terms?

  9. Cox Model Variable Est.(s.e.) Constant n.a. Adcomm -1.00(.25) Pages .0008(.004) Indep -.33(.29) p n.a. N 170 Log-likel. -696.72 Weibull Model Variable Est.(s.e.) Constant 5.89(.30) Adcomm .80(.25) Pages -.0007(.004) Indep .27(.30) p 1.23(.15) N 170 Log-likel. -227.90 Time and rulemaking

  10. Topics for another day… • interpreting for substantive meaning • models for discrete duration data • time-varying covariates • repeated events—multiple and competing • models for unobserved heterogeneity • …just scratching the surface

  11. If you want more…  Janet M. Box-Steffensmeier and Bradford S. Jones, Event History Modeling: A Guide for Social Scientists Mario A. Cleves, William W. Gould, and Roberto G. Gutierrez, An Introduction to Survival Analysis Using Stata

More Related