Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00993 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912563237748736 |
|---|---|
| author | Liu, Liu |
| author_facet | Liu, Liu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00993 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid APIM-CFGM Model for Longitudinal Non-Exchangeable Dyads: Demonstrating and Comparing Estimation Approaches Using Multilevel Modeling Liu, Liu Methodology 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. |
| title | A Hybrid APIM-CFGM Model for Longitudinal Non-Exchangeable Dyads: Demonstrating and Comparing Estimation Approaches Using Multilevel Modeling |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.00993 |