Susanna Zaccarin

The use of different data sources in the analysis of co-authorship networks and scientific performance

Abstract Scientific collaboration is usually derived from archival co-authorship data. Several data sources may be examined, but they all have advantages and disadvantages, especially when a specific discipline or community is of interest. The aim of this paper is to explore the effect of the use of three data sources – Web of Science, Current Index to Statistics and nationally funded research projects – on the analysis of co-authorship networks among Italian academic statisticians. Results provide evidence of our hypotheses on distinct collaboration patterns among statisticians, as well as distinct effects of scientist network positions on scientific performance, by both Statistics subfield and data source.

Multilevel analysis in social research: An application of a cross-classified model

The multilevel approach can be a fruitful methodological framework in which to formulate the micro-macro relationships existing between individuals and their contexts. Usually, place of residence is taken as proxy for context. But individuals can be classified at the same level in more than one way. For example, not only may place of residence be relevant, but birthplace, household or working relations may also be taken into account. Contextual effects can be better identified if multiple classifications are simultaneously considered. in this sense, data do not have a purely hierarchical structure but a cross-classified one, and become very important to establish whether the resulting structure affects the covariance structure of data. In this paper, some critical issues arising from application of multilevel modelling are discussed, and multilevel cross-classified models are proposed as more flexible tools to study contextual effects. A multilevel cross-classified model is specified to evaluate simultaneously the effects of womens place of birth and womens current place of residence on the choice of bearing a second child by Italian women in the mid-1990s.

The science network in Italian population research : An analysis according to the social network perspective

The scientific community organises its relationships into network patterns, where the nodes are individuals (scientists) and the links are acquaintance and common work, usually presented at workshops and conferences and/or published in books and scientific journals. A references review on Population Studies by Italian scientists is delivered every two years by the Demography Section of the Italian Statistical Society; the review is exhaustive for academic demographers. In this paper, the properties of the demographers’ network in 1998–1999 are evaluated, with the aim of identifying factors which may influence collaborative relations among actors. The probability of cooperation between couples (dyads) of demographers is modelled, conditionally on observed characteristics of the dyad (sex, academic position, university affiliation). Main results suggest that “closeness”, defined in a wider sense and not simply as geographical proximity, plays a major role in determining actors’ relationships.

Modelling Multiple Interactions in Science and Technology Networks

Empirical studies have shown that the extent of innovation diffusion is greatly affected by the structure of the network in which innovation processes take place. This contribution aims to identify the complex structure of relationships at the basis of knowledge and innovation diffusion among actors from various organizations (firms, academic and research institutions) in a given territory. A multiplex approach is proposed to explain co-authorship and co-invention interaction among Author–Inventors community in a specific geographic area. To this end, we carry out a case study of the Trieste area (North-East Italy), characterized by a very high concentration of research organizations and by the emergence of a lively sector of small R&D firms.

Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians

The aim of the present contribution is to merge bibliographic data for members of a bounded scientific community in order to derive a complete unified archive, with top-international and nationally oriented production, as a new basis to carry out network analysis on a unified co-authorship network. A two-step procedure is used to deal with the identification of duplicate records and the author name disambiguation. Specifically, for the second step we strongly drew inspiration from a well-established unsupervised disambiguation method proposed in the literature following a network-based approach and requiring a restricted set of record attributes. Evidences from Italian academic statisticians were provided by merging data from three bibliographic archives. Non-negligible differences were observed in network results in the comparison of disambiguated and not disambiguated data sets, especially in network measures at individual level.

Modelling Network Data: An Introduction to Exponential Random Graph Models

A brief introduction to statistical models for complete network data is presented. An example is provided by the collaboration network of Italian scholars on Population Studies.

Social relations and life satisfaction: the role of friends

Social capital is defined as the individual’s pool of social resources found in his/her personal network. A recent study on Italians living as couples has shown that friendship relationships, beyond those within an individual’s family, are an important source of support. Here, we used data from Aspects of Daily Life, the Italian National Statistical Institute’s 2012 multipurpose survey, to analyze the relation between friendship ties and life satisfaction. Our results show that friendship, in terms of intensity (measured by the frequency with which individuals see their friends) and quality (measured by the satisfaction with friendship relationships), is positively associated to life satisfaction.

Improving Co-authorship Network Structures by Combining Heterogeneous Data Sources

The present paper aims at describing the scientific collaboration patterns of the Italian academic statisticians by merging bibliographic data from heterogenous sources–ISI-WoS, Current Index to Statistics, and the database of nationally funded research projects, PRIN. To obtain a unified database, containing both top international as well as nationally oriented production, information were combined by identifying and linking duplicate records, i.e. record linkage. The unique co-authorship network was then used as basis for network analysis.