dyadic data analysis – Andrew Knight

Campagna, R. L., Dirks, K. T., Knight, A. P., Crossley, C., & Robinson, S. L. (2020). On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy. Journal of Applied Psychology, 105, 994-1012.

Abstract. Research has long emphasized that being trusted is a central concern for leaders (Dirks & Ferrin, 2002), but an interesting and important question left unexplored is whether leaders feel trusted by each employee, and whether their felt trust is accurate. Across two field studies, we examined the factors that shape the accuracy of leaders’ felt trust—or, their trust meta-accuracy—and the implications of trust meta- accuracy for the degree of relationship conflict between leaders and their employees. By integrating research on trust and interpersonal perception, we developed and tested hypotheses based on two theoretical mechanisms—an external signaling mechanism and an internal presumed reciprocity mechanism—that theory suggests shape leaders’ trust meta-accuracy. In contrast to the existing literature on felt trust, our results reveal that leader trust meta-accuracy is shaped by an internal mechanism and the presumed reciprocity of trust relationships. We further find that whether trust meta-accuracy is associated with positive relational outcomes for leaders depends upon the level of an employee’s actual trust in the leader. Our research contributes to burgeoning interest in felt trust by elucidating the mechanisms underlying trust meta-accuracy and suggesting practical directions for leaders who seek to accurately understand how much their employees trust them.

Knight, A. P., & Humphrey, S. E. (2019). Dyadic data analysis. In S. E. Humphrey and J. M. LeBreton (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis, pp. 423-447. Washington, DC: American Psychological Association.

Accompanying R functions for the social relations model: http://apknight.org/pdSRM.R

Abstract. Many foundational theories in the social sciences rely upon assumptions about dyadic interpersonal perceptions, behaviors, and relationships. This chapter provides a broad introduction to foundational concepts and techniques in analyzing dyadic data. The authors describe in detail one specific approach to dyadic data analysis—the social relations model—and provide software functions for conducting the analysis using multilevel modeling in R. The value of dyadic data analysis is illustrated through a discussion of prior publications that have used this approach. The authors also provide a step-by-step empirical example of how to use the social relations model with multilevel modeling in R, focused on dyadic trust in workgroups. The chapter concludes with a discussion of alternative approaches, beyond the social relations model, for analyzing dyadic data.