📝 SummarXiv

Abstract

Understanding change over time within dyads, such as mentor-mentee or therapist-client pairs, poses unique challenges, particularly in studies with small samples and distinguishable roles. This paper introduces a flexible hybrid longitudinal modeling that integrates features of the Actor-Partner Interdependence Model (APIM) and the Common Fate Growth Model (CFGM) to simultaneously estimate individual-level and shared dyad-level effects. Using a hypothetical peer-mentoring example (novices paired with experts), the model addresses three key issues: (1) how the interpretation of model parameters when role is dummy-coded versus effect-coded; (2) how model performance is affected by small sample sizes; and (3) how results differ between Maximum Likelihood (ML) and Bayesian estimation. Simulated data for 50 dyads across five time points are analyzed, with subsampling at 30 and 5 dyads. Models are estimated in a multilevel modeling framework using R (lme4 for ML and brms for Bayesian inference). Results show that dummy and effect coding reflect distinct interpretations: dummy coding expresses effects relative to a reference group, whereas effect coding centers parameters on the grand mean across roles. Although model fit remains unchanged, the choice of coding impacts how group-level effects and interactions are interpreted. Very small samples (e.g., 5 dyads) lead to unstable estimates, whereas Bayesian credible intervals more accurately reflect uncertainty in such cases. In larger samples, ML and Bayesian estimates converge. This APIM-CFGM hybrid offers a practical tool for researchers analyzing longitudinal dyadic data with distinguishable roles and smaller sample sizes.