The Limits of Studying Networks with Event Data: Evidence from the ICEWS Dataset

2018 ◽  
Author(s):  
Kai Jäger
Keyword(s):  
2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Emerson Bodevan ◽  
Luiz Duczmal ◽  
Gladston Prates Moreira ◽  
Anderson Duarte ◽  
Flávia Oliveira Magalhães
Keyword(s):  
Low Risk ◽  

2021 ◽  
pp. 089331892110239
Author(s):  
Michael W. Kramer ◽  
Jasmine T. Austin ◽  
Glenn J. Hansen

Single-event volunteering, one form of episodic volunteering, is increasingly common. To gain a deeper understanding of this phenomenon, this study used self-determination theory to explore the motivations and communication experiences of volunteers for a 1-day volunteer event. Data were collected from 294 volunteers on a questionnaire containing open-ended questions (qualitative) and scaled items (quantitative). Results from the analysis indicated increased feelings of autonomy, competence, relatedness, and purpose, along with reduced feelings of pressure to participate, were associated with increased motivation to volunteer during the recruitment process. Higher levels of motivation, along with positive communication with leaders and peers, resulted in higher levels of satisfaction and likelihood of volunteering again. These results provide evidence for expanding SDT to develop a model of volunteering that includes additional motivations and communication, and provide practical advice for leaders of volunteers.


2021 ◽  
pp. 096228022110028
Author(s):  
T Baghfalaki ◽  
M Ganjali

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.


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