Abstract
Dynamic models are becoming increasingly popular to study the dynamic processes of dyadic interactions. In
this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data
generation as a tool to inspire and guide theory development and elaborate on how to connect substantive ideas
to specific features of these models. We begin by discussing the basics of dynamic models with dyadic interactions. Then we present several models and illustrate model-implied behavior through generated data, accompanied by the DID Shiny app which allows researchers to generate and visualize their own data. Specifically, we
consider: (a) the first-order vector autoregressive (VAR(1)) model; (b) the latent VAR(1) model; (c) the timevarying VAR(1) model; (d) the threshold VAR(1) model; (e) the hidden Markov model; and (f) the Markovswitching VAR(1) model. Finally, we demonstrate these models using empirical examples. We aim to give
researchers more insight into what dynamic modeling approach fits their research question and data best.
this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data
generation as a tool to inspire and guide theory development and elaborate on how to connect substantive ideas
to specific features of these models. We begin by discussing the basics of dynamic models with dyadic interactions. Then we present several models and illustrate model-implied behavior through generated data, accompanied by the DID Shiny app which allows researchers to generate and visualize their own data. Specifically, we
consider: (a) the first-order vector autoregressive (VAR(1)) model; (b) the latent VAR(1) model; (c) the timevarying VAR(1) model; (d) the threshold VAR(1) model; (e) the hidden Markov model; and (f) the Markovswitching VAR(1) model. Finally, we demonstrate these models using empirical examples. We aim to give
researchers more insight into what dynamic modeling approach fits their research question and data best.
| Original language | English |
|---|---|
| Journal | Psychological Methods |
| DOIs | |
| Publication status | E-pub ahead of print - 25 May 2023 |