Network analysis of 2-mode data

Network analysis is distinguished from traditional social science by the dyadic nature of the standard data set. Whereas in traditional social science we study monadic attributes of individuals, in network analysis we study dyadic attributes of pairs of individuals. These dyadic attributes (e.g. social relations) may be represented in matrix form by a square 1-mode matrix. In contrast, the data in traditional social science are represented as 2-mode matrices. However, network analysis is not completely divorced from traditional social science, and often has occasion to collect and analyze 2-mode matrices. Furthermore, some of the methods developed in network analysis have uses in analysing non-network data. This paper presents and discusses ways of applying and interpreting traditional network analytic techniques to 2-mode data, as well as developing new techniques. Three areas are covered in detail: displaying 2-mode data as networks, detecting clusters and measuring centrality.

Issues in second language proficiency

Benati provides clarity about the characteristics and notion of language proficiency in the field of second language acquisition. He looks at four areas of research paradigmatically related to the role of proficiency: theorizing and measuring second language proficiency; the dimensions of L2 proficiency; factors contributing to the attainment of L2 proficiency and attaining L2 proficiency in the classroom. It also contains a variety of research accounts about the specific factors which have an effect on proficiency together with a theorised measurement of proficiency in second language research. It will be required reading for researchers in applied linguistics and second language acquisition.