Item response theory network analysis of European universities

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).