Dyadic analysis and the reciprocal one-with-many model: Extending the study of interpersonal processes with intensive longitudinal data.

Newly available data streams from experience sampling studies and social media are providing new opportunities to study individuals’ dyadic relations. The “one-with-many” (OWM) model (Kenny et al., 2006; Kenny & Winquist, 2001) was specifically constructed for and is used to examine features of multiple dyadic relationships that one set of focal persons (e.g., therapists, physicians) has with others (e.g., multiple clients, multiple patients). Originally, the OWM model was constructed for and applied to cross-sectional data. However, the model can be extended to accommodate and may be particularly useful for the analysis of intensive repeated measures data now being obtained through experience sampling and social media. This article (a) provides a practical tutorial on fitting the OWM model, (b) describes how the OWM model is extended for analysis of repeated measures data, and (c) illustrates application of the OWM model using reports about interpersonal behavior and benefits individuals experienced in 64,111 social interactions during 9 weeks of study (N = 150). Our presentation highlights the utility of the OWM model for examining interpersonal processes in everyday life. Translational Abstract—The study of interpersonal relationships and processes is of great interest to many researchers across psychology. New data collection methods, including experience sampling studies and social media observations, provide new opportunities to study individuals’ interpersonal relationships. The “one-with-many” (OWM) model is useful to researchers who want to examine features of multiple dyadic relationships that one set of focal persons (e.g., therapists, physicians) has with others (e.g., multiple clients, multiple patients). Originally, the OWM model was developed and applied to cross-sectional data. However, the model can be applied to examine interpersonal dynamics using intensive repeated measures data now being collected through experience sampling studies and social media observations. The goal of this article is to provide a practical primer on fitting the OWM model to cross-sectional and intensive longitudinal data to study interpersonal processes. We illustrate the OWM model using data from an experience sampling studying that gathered reports of social interactions individuals experienced during 9 weeks of study. We hope this article facilitates understanding and use of the OWM model, while also shedding light on potential methodological directions to advance dyadic analytic techniques.