scholarly journals Investigation of Rater Effects Using Social Network Analysis and Exponential Random Graph Models

2017 ◽  
Vol 78 (3) ◽  
pp. 430-459 ◽  
Author(s):  
Iasonas Lamprianou
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
High Stakes ◽  
Rating Data ◽  
New Approach ◽  
Exponential Random Graph ◽  
Rater Severity

It is common practice for assessment programs to organize qualifying sessions during which the raters (often known as “markers” or “judges”) demonstrate their consistency before operational rating commences. Because of the high-stakes nature of many rating activities, the research community tends to continuously explore new methods to analyze rating data. We used simulated and empirical data from two high-stakes language assessments, to propose a new approach, based on social network analysis and exponential graph models, to evaluate the readiness of a group of raters for operational rating. The results of this innovative approach are compared with the results of a Rasch analysis, which is a well-established approach for the analysis of such data. We also demonstrate how the new approach can be practically used to investigate important research questions such as whether rater severity is stable across rating tasks. The merits of the new approach, and the consequences for practice are discussed.

Μeasuring and Visualizing Coders’ Reliability: New Approaches and Guidelines From Experimental Data

2020 ◽  
pp. 004912412092619
Author(s):  
Iasonas Lamprianou
Keyword(s):  
Experimental Data ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
New Approach ◽  
Random Graph Models ◽  
Exponential Random Graph

This study investigates inter- and intracoder reliability, proposing a new approach based on social network analysis (SNA) and exponential random graph models (ERGM). During a recent exit poll, the responses of voters to two open-ended questions were recorded. A coding experiment was conducted where a group of coders coded a sample of text segments. Analyzing the data, we show that the proposed SNA/ERGM method extends significantly our analytical leverage, beyond what popular tools such as Krippendorff’s α and Fleiss’s κ have to offer. The reliability of coding for individual coders differed significantly for the two questions although they were very similar and the same codebook was used. We conclude that the main advantages of the proposed SNA/ERGM method are the intuitive visualizations and the nuanced measurements. Detailed guidelines are provided for practitioners who would like to use the proposed method in operational settings.

scholarly journals Social network analysis of lodging establishments in Tiradentes (MG): Profile and evidence of relationships genesis

2020 ◽  
Vol 22 (3) ◽  
pp. 424-445
Author(s):  
Geraldo Magela Rodrigues De Vasconcelos ◽  
Gustavo Melo-Silva ◽  
Velcimiro Inácio Maia
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

A análise de redes sociais (ARS) constitui um grande avanço na pesquisa em turismo ao revelar as características das relações estabelecidas, apresentando suas estruturas e propriedades. Este trabalho objetivou caracterizar e analisar a rede de cooperação formada entre proprietários de pousadas em Tiradentes-MG. Com o objetivo de explorar o contexto da pesquisa, foram realizadas entrevistas junto a sete proprietários de pousadas. Posteriormente, para a coleta dos dados, foi aplicado um questionário aos proprietários. Por meio da técnica da “bola de neve” foi gerada a rede de cooperação. A partir disso, utilizou-se das técnicas da ARS, com ênfase nas métricas descritivas e pela modelagem de grafos aleatórios da família exponencial (ERGM - Exponential Random Graph Models). Com isso, foi possível caracterizar a rede de cooperação e analisar suas propriedades. A rede possui 54 pousadas, com uma densidade geral baixa, pois há apenas 4,7% de relações possíveis. Foram identificadas 17 pousadas centrais e 37 periféricas. No subgrupo central, a densidade foi de 15,4% e no subgrupo periférico, de apenas 2,6%. A rede de cooperação observada apresentou homofilia por gênero e por procedência dos proprietários. Os resultados da modelagem ERGM permitiram explicações probabilísticas em termos de atributos endógenos dos proprietários, como gênero e procedência.

scholarly journals The performance of permutations and exponential random graph models when analyzing animal networks

2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk
Keyword(s):  
Social Network ◽  
Random Graph ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology

2018 ◽  
Vol 56 (1) ◽  
pp. 559-580 ◽  
Author(s):  
K.A. Garrett ◽  
R.I. Alcalá-Briseño ◽  
K.F. Andersen ◽  
C.E. Buddenhagen ◽  
R.A. Choudhury ◽  
...  
Keyword(s):  
Plant Pathology ◽  
Network Analysis ◽  
Disease Management ◽  
Agricultural Development ◽  
Research Priorities ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Exponential Random Graph ◽  
Plant Disease Management ◽  
Systems Framework

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.

Proximity and centrality in inter-organisational collaborations for innovation

2019 ◽  
Vol 58 (2) ◽  
pp. 239-254 ◽  
Author(s):  
Francesco Capone ◽  
Vincenzo Zampi
Keyword(s):  
Network Analysis ◽  
Inner Core ◽  
Preferential Attachment ◽  
Hierarchical Structures ◽  
Primary Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Content Type ◽  
Exponential Random Graph ◽  
The Impact

Purpose The purpose of this paper is to investigate the impact of the different drivers of the establishment of innovation relationships in an aerospace cluster. In particular, the work investigates the impact of the various forms of proximity in the formation of inter-organisational collaborations for innovation. Design/methodology/approach The analysis is based on primary data collected through interviews and questionnaires on innovation collaborations, administered to all the firms operating in the aerospace cluster in Tuscany. The work applies social network analysis and Exponential Random Graph Models to analyse the forces that drive inter-organisational collaborations for innovation. Findings Results confirm the importance of geographical proximity in the formation of ties in the cluster, but social proximity is one of the main drivers for tie formation. Reciprocity shows that companies develop innovations in a reciprocal way and that most relationships are bidirectional. Triadic closure is also relevant, where the role played by trust and previous relationships is evident. Finally, hierarchy network processes are underlined, where the most central actors of the network are the most popular confirming a processes of preferential attachment. The central organisations gradually are more important, whereas the marginal ones are left in the periphery. Originality/value The work presents some novelties. First, it measures the different impacts of the various forms of proximity together with more advanced measures of network analysis. It allows pointing out the relevance of a firm’s network position in clusters and the fact that clusters assume hierarchical structures similar to centre-periphery networks, where most relevant nodes are in the inner core and marginal organisations are relegated.

Research on interpersonal relationship network of pyramid scheme organization based on social network: A case study

2019 ◽  
Vol 30 (07) ◽  
pp. 1940016
Author(s):  
Pihu Feng ◽  
Zaiwu Gong ◽  
Duoyong Sun
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Organized Crime ◽  
Interpersonal Relationship ◽  
Hunan Province ◽  
Economic Losses ◽  
Exponential Random Graph Models ◽  
Pyramid Scheme ◽  
Relationship Network

As a form of organized crime, pyramid scheme has brought huge economic losses to many countries, especially China, and caused serious social problems. How to deeply analyze the structure of pyramid scheme organizations is a necessary topic to explore and combat pyramid scheme organizations. Social network analysis is an effective method to study organized crime. Among them, the motif and exponential random graph models are effective tools for studying organizational microstructure, endogenous process. For the first time, this paper uses the social network analysis to study the interpersonal relationship network of a specific pyramid scheme organization, the typical case of “5.03” in Hunan Province was taken as the research object, and the above model was used for modeling analysis. The results show that the interpersonal relationship network has a sparse density. The microstructure is not a pyramid structure that is generally considered, but presents more ternary closures. Cross-community links, cross-level links between core employees and surrounding employees are less. The pyramid scheme network of interpersonal relationships has obvious homogeneity characteristics.

scholarly journals A Case Study of Pyramid Scheme Finance Flow Network Based on Social Network Analysis

2019 ◽  
Vol 11 (16) ◽  
pp. 4370
Author(s):  
Feng ◽  
Sun ◽  
Gong
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Graph Model ◽  
Flow Network ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Pyramid Structure ◽  
Pyramid Scheme

(1) Background: The pyramid scheme has caused a large-scale plunder of finances due to the unsustainability of its operating model, which seriously jeopardizes economic development and seriously affects social stability. In various types of networks, the finance flow network plays an extremely important role in the pyramid scheme organization. Through the study of the finance network, the operational nature of pyramid scheme organizations can be effectively explored, and the understanding of pyramid scheme organizations can be deepened to provide a basis for dealing with them. (2) Methods: This paper uses the motifs analysis and exponential random graph model in social network analysis to study the micro-structure and the network construction process of the “5.03” pyramid scheme finance flow network in Hunan, China. (3) Results: The finance flow network is sparse, the microstructure shows a typical pyramid structure; finance flows within the community and eventually flows to the most critical personnel, there is no finance relationship between different communities, and there are few finance relationships between pyramid salesmen of the same level. The inductees are in a key position in the network, which may explain why they are transferred to prosecution.

scholarly journals Inferential Network Analysis with Exponential Random Graph Models

2011 ◽  
Vol 19 (1) ◽  
pp. 66-86 ◽  
Author(s):  
Skyler J. Cranmer ◽  
Bruce A. Desmarais
Keyword(s):  
Network Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Model ◽  
General Acceptance ◽  
Random Graph Models ◽  
Exponential Random Graph

Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network data remain fledgling by comparison. We introduce and evaluate a general model for inference with network data, the Exponential Random Graph Model (ERGM) and several of its recent extensions. The ERGM simultaneously allows both inference on covariates and for arbitrarily complex network structures to be modeled. Our contributions are three-fold: beyond introducing the ERGM and discussing its limitations, we discuss extensions to the model that allow for the analysis of non-binary and longitudinally observed networks and show through applications that network-based inference can improve our understanding of political phenomena.

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