Engineering interaction: Structural change,locus of identification,and the formation and maintenance of cross-unit ties

Cross-unit ties–relationships that facilitate discretionary information sharing between individuals from different business units–offer a range of organizational benefits. Scholars argue that organizations can promote cross-unit ties by: (a) formally bringing together individuals from different business units into structural links (e.g., cross-unit strategic committees) to encourage the formation of new cross unit ties and, (b) transferring individuals across units, which can increase cross-unit interaction when ties to the prior unit are maintained. This study considers the notion that the success of these formal interventions in fostering cross-unit interaction is contingent on identification with the local unit relative to identification with the broader organization. Specifically, we propose that structural links are more likely to foster cross-unit ties when organizational identification is high and unit identification is low. In contrast, lateral transfers are more likely to result in cross-unit ties when both organizational identification and unit identification are high. We find general support for these propositions in data obtained from a sample of senior leaders of a Fortune 200 agribusiness company before and after a restructuring designed to stimulate cross-unit information sharing. Our model and results make important contributions to our understanding of the relationship between formal and informal structure and reconcile conflicting views regarding the moderating effect of unit identification on intergroup relations.     

A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis’ formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis’ formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis’ formula with previously existing methods. Results from these presentations suggest that Louis’ formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.     

Correction to: Suicidal Mental Imagery in Psychiatrically Hospitalized Adolescents

Research on Child and Adolescent Psychopathology - A correction to this paper has been published: https://doi.org/10.1007/s10802-021-00827-8