In the context of formal leadership assessments, 360° ratings are widely used by organizations. In a
typical 360° assessment, ratings are gathered from multiple rater groups, including subordinates,
peers, superiors, and a self-rating of the target. Comparing one’s own perceptions to observers’
perceptions on key leadership dimensions is believed to highlight areas of agreement and
disagreement (“blind spots”), which can directly inform leadership training and development.
Because rater groups are assumed to differ in their perceptions of a leader’s behavior, including
each group’s unique perspective should create a more well-rounded analysis of the leader’s
strengths and weaknesses. However, due to the traditional treatment of multisource data, it is
impossible to separate consensus about the leader (i.e., Leadership Arena), unique selfperceptions
of the leader (i.e., Leader Identity), and impressions conveyed to others that are
distinct from self-perceptions (i.e., Reputation). The consequence is that, at present, it remains
unknown to what extent each rater perspective offers unique information about a leader. In the
current project, we propose the Leadership Arena-Reputation-Identity (LARI) model as a
theoretical framework that can be used to fully exploit the richness offered by multisource leader
assessments. To statistically disentangle the shared and unique perceptions on leadership, we
introduce bifactor modeling to the leadership literature.