Abstract
One-mode projections of two-mode data are typically valued, and therefore, require dichotomization before they can be analyzed using many network analytic methods. The traditional dichotomization approach, in which a universal threshold is applied to all edge weights, can yield a binary one-mode projection with undesirable artifacts and requires the arbitrary selection of a threshold value. This paper proposes a method and associated Stata command, ONEMODE, for identifying statistically significant edges in one-mode projections, which can be used to construct both binary and signed projections. The method is demonstrated using two-mode data on southern women’s social event participation and US Supreme Court justices’ majority decision participation, and is compared to two alternative approaches for normalizing edge weights in one-mode projections.
Published Version
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