  In many scientific fields, the order of coauthors on a paper conveys
information about each individual's contribution to a piece of joint work. We
argue that in prior network analyses of coauthorship networks, the information
on ordering has been insufficiently considered because ties between authors are
typically symmetrized. This is basically the same as assuming that each
co-author has contributed equally to a paper. We introduce a solution to this
problem by adopting a coauthorship credit allocation model proposed by Kim and
Diesner (2014), which in its core conceptualizes co-authoring as a directed,
weighted, and self-looped network. We test and validate our application of the
adopted framework based on a sample data of 861 authors who have published in
the journal Psychometrika. Results suggest that this novel sociometric approach
can complement traditional measures based on undirected networks and expand
insights into coauthoring patterns such as the hierarchy of collaboration among
scholars. As another form of validation, we also show how our approach
accurately detects prominent scholars in the Psychometric Society affiliated
with the journal.
