Abstract

The goal of network analysis is to focus on relationships between social entities. It is used widely in the social and behavioral sciences, as well as in political science, economics, psychology andorganizational science. The social network approach has a long history and it has been developed over the last 60 years by researchers in psychology, sociology and anthropology. Nowadays, using high-speed computers, network analysis is mostly used for graphical presentation of relationships and dependencies; moreover, networks comprise graphical representations of the relationships (edges) between variables (nodes). Network analysis provides the capacity to estimate complex patterns of relationships and the network structure can be analyzed to reveal core features of the network. Recent developments however, stress the usefulness of network-based approaches for measurement models in social sciences, where CFA and IRT are are complemented by network approaches. In this paper we provide an overview of networks of European economic universities, and present and compare the results of classical Item Response Theory (Birnbaum model) and Latent Network Models (Ising and Residual Network Model) for measurement of networking ability among Polish economic universities. All calculations are conducted using R software.

The results of IRT model shows that the highest significant difficulty parameters (that can be regarded as network “attractiveness”) are characteristic for CEEMAN, CESEENET and MAGNACARTA networks, whereas the lowest but insignificant parameters are related to ATLAS, PRME and NICE. Because of lack of evidence of unidimenstionality of common factor IRT model and lot of insignificant parameters, the Latent Network Model and Residual Network Model were used. The application of LNM and RNM revealed three-dimensional structure of latent networks. First cluster consists with relatively attractive networks (EUA, CESEENET, MAGNACARTA, NICE, CEEMAN), second cluster represents the average attractiveness (CEMS, EDAMBA, PIM) and the third is based on networks with lowest difficulty parameters (EFMD, PRME, ATLAS).

摘要

网络分析的目标是关注社会实体之间的关系。它广泛应用于社会科学和行为科学，以及政治学、经济学、心理学和组织科学。社交网络方法有着悠久的历史，是由心理学、社会学和人类学研究人员在过去 60 年中开发出来的。如今，使用高速计算机，网络分析主要用于关系和依赖关系的图形表示；此外，网络包含变量（节点）之间关系（边）的图形表示。网络分析提供了估计复杂关系模式的能力，并且可以分析网络结构以揭示网络的核心特征。然而最近的事态发展，强调基于网络的方法对于社会科学测量模型的有用性，其中 CFA 和 IRT 得到网络方法的补充。在本文中，我们概述了欧洲经济大学的网络，并介绍和比较了经典项目响应理论（Birnbaum 模型）和潜在网络模型（Ising 和残差网络模型）的结果，以衡量波兰经济大学之间的网络能力。所有计算均使用 R 软件进行。展示并比较经典项目反应理论（Birnbaum 模型）和潜在网络模型（Ising 和残差网络模型）的结果，用于衡量波兰经济大学的网络能力。所有计算均使用 R 软件进行。展示并比较经典项目反应理论（Birnbaum 模型）和潜在网络模型（Ising 和残差网络模型）的结果，用于衡量波兰经济大学的网络能力。所有计算均使用 R 软件进行。

IRT模型的结果表明，最高的显着难度参数（可以被视为网络“吸引力”）是CEEMAN、CESEENET和MAGNACARTA网络的特征，而最低但不显着的参数与ATLAS、PRME和NICE相关。由于缺乏证明公因子IRT模型单维性的证据以及大量不显着的参数，因此使用了潜在网络模型和残差网络模型。LNM 和 RNM 的应用揭示了潜在网络的三维结构。第一个集群由相对有吸引力的网络（EUA、CESEENET、MAGNACARTA、NICE、CEEMAN）组成，第二个集群代表平均吸引力（CEMS、EDAMBA、PIM），第三个集群基于难度参数最低的网络（EFMD、PRME、ATLAS） 。