Abstract. Many topics in organizational research involve examining the interpersonal perceptions and behaviors of group members. The resulting data can be analyzed using the Social Relations Model (SRM). This model enables researchers to address several important questions regarding relational phenomena. In the model, variance can be partitioned into group, actor, partner, and relationship; reciprocity can be assessed in terms of individuals and dyads; and, predictors at each of these levels can be analyzed. However, analyzing data using the currently available SRM software can be challenging and can deter organizational researchers from using the model. In this article, we provide a “go-to” introduction to SRM analyses and propose SRM_R (https://davidakenny.shinyapps.io/SRM_R/), an accessible and user-friendly, web-based application for SRM analyses. The basic steps of conducting SRM analyses in the app are illustrated with a sample dataset of 47 teams, 228 members, and 884 dyadic observations, using the participants’ ratings of the advice-seeking behavior of their fellow employees.
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.
Abstract. We develop and test predictions about how demographic differences influence dyadic deference in multidisciplinary research teams, and how differential patterns of dyadic deference emerge to shape team-level effectiveness. We present a dual pathway model that recognizes that two distinct mechanisms—task contributions and social affinity— account for how team members’ demographic attributes contribute to deference. Furthermore, we propose that the extent to which these different mechanisms are prevalent in a team has implications for the team’s research productivity, with deference based on social affinity detracting from it and deference based on task contributions enhancing it. Using longitudinal data from a sample of 55 multidisciplinary research teams comprising 619 scientists, we found general support for our conceptual model. Our findings underscore the importance of accounting for multiple interpersonal mechanisms to understand the complex, multilevel nature of deference in teams.