Improved lithospheric attenuation structure of the Arabian Peninsula through the use of national network data

2021 ◽  
Vol 14 (10) ◽  
Author(s):  
Michael E. Pasyanos ◽  
Yahya M. Tarabulsi ◽  
Salah Y. Al-Hadidy ◽  
Wael W. Raddadi ◽  
Abdullah D. Mousa ◽  
...  
Keyword(s):  
Arabian Peninsula ◽  
Network Data ◽  
National Network ◽  
Attenuation Structure

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Collection of Ego-Centered Network Data with Computer-Assisted Interviews

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Joachim Gerich ◽  
Roland Lehner
Keyword(s):  
Survey Methods ◽  
A Priori ◽  
Dynamic Structure ◽  
Computer Assisted ◽  
Network Data ◽  
Great Effort ◽  
Self Administration ◽  
Face To Face ◽  
Full Network

Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

Educating Ms. Fatima

1970 ◽  
pp. 56-63
Author(s):  
Tim Walters ◽  
Susan Swan ◽  
Ron Wolfe ◽  
John Whiteoak ◽  
Jack Barwind
Keyword(s):  
Federal Government ◽  
Free Trade ◽  
United Arab Emirates ◽  
Arabian Peninsula ◽  
High Technology ◽  
Emerging Technology ◽  
Abu Dhabi ◽  
Free Trade Zones ◽  
Knowledge Based ◽  
K 12

The United Arab Emirates is a smallish Arabic/Islamic country about the size of Maine located at the tip of the Arabian Peninsula. Though currently oil dependent, the country is moving rapidly from a petrocarbon to a people-based economy. As that economy modernizes and diversifies, the country’s underlying social ecology is being buffeted. The most significant of the winds of change that are blowing include a compulsory, free K-12 education system; an economy shifting from extractive to knowledge-based resources; and movement from the almost mythic Bedouin-inspired lifestyle to that of a sedentary highly urbanized society. Led by resource-rich Abu Dhabi and Dubai, the federal government has invested heavily in tourism, aviation, re-export commerce, free trade zones, and telecommunications. The Emirate of Dubai, in particular, also has invested billions of dirhams in high technology. The great dream is that educated and trained Emiratis will replace the thousands of foreign professionals now running the newly emerging technology and knowledge-driven economy.

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