The contributions of this project can be categorized according to the three main objectives of the project:
I. An advanced relational event modeling (REM) network framework has been developed to study complex social interaction behavior using relational event history data.
II. The new framework has been applied to better understand social interaction dynamics in organizational science, education, and criminology, and other fields of science.
III. Free, open-source, and user-friendly statistical software packages (R and JASP) have been developed that allow researchers to apply the framework in their own research.
I. The REM framework encompasses the following novel methodologies:
* Exploratory statistical methods for time-sensitive social processes
...to study how social interaction behavior changes using moving window techniques (Mulder & Leenders, 2019);
...to study memory decay functions using relational event sequences (Arena et al., 2022);
...to identify key drivers of social interaction processes using Bayesian shrinkage priors (Karimova et al., 2023);
...to assess model (mis)fit of REMs using posterior predictive checks (Mulder & Hoff, 2024);
...for events having different types, sentiments, and duration (Meijerink et al., 2023; Meijerink et al., 2024; Arena et al., 2024a).
* Confirmatory statistical methods for testing theories on social network interactions using Bayes factors
...for testing which drivers dominate social interaction processes (Heck et al., 2023, Mulder & Leenders, 2019);
...for testing memory decay parameters in REM (Arena et al., 2023);
...for testing the heterogeneity of social interaction styles across social networks (Viera et al., 2024);
...for making predictions about social interactions in the (near) future (Arena et al., 2022, Lakdawala et al., 2024);
...for testing the factors that affect the formation of ties (Mulder et al., 2024).
* Advanced statistical methods
...to study abrupt changes of social interaction behavior (Shafiee-Kamalabad et al., 2023).
...to study complex social processes caused by latent social structures (Mulder & Hoff, 2024);
...to analyze multilevel relational event models (Vieira et al., 2024a).
* Statistical algorithms have been developed to ensure general utilization of the methodologies including
...Markov chain Monte Carlo algorithms (Mulder & Hoff, 2024) and Hamiltonian Monte Carlo algorithms for learning of Bayesian posteriors of REM parameters (Arena et al., 2024c);
...Meta-analytic approximation techniques for “Big” relational event sequences and sequential fitting based on streams of relational events that are observed in real time (Vieira et al., 2024b).
II. We developed free and user-friendly software that allow researchers to apply the novel framework in an accessible manner:
* The following R packages have been developed (which also include tutorials)
- ‘remify’ for processing, arranging and transforming relational event data (Arena et al., 2024c).
- ‘remstats’ for computes statistics using relational event history data (Meijerink et al., 2023).
- ‘remstimate’ for optimization algorithms for tie-oriented and actor-oriented relational event models (Arena et al., 2024c).
- ‘remx’ for fast meta-analytic approximations for relational event models (Viera et al., 2024).
- ‘bremory’ for modeling memory retention in relational event data (Arena et al., 2024d).
- ‘remulate’ for generating dynamic temporal network data based on relational event models (Lakdawala et al., 2024).
* A REM module (Pfadt & Mulder, 2023) has been developed for the popular software program JASP with an intuitive graphical user-interface (such as SPSS). The module will be part of the next large update of JASP.
III. The application of the novel framework has resulted in a better understanding of social interaction dynamics in various fields of research:
* In organization studies (WP1), we obtained new insights about integration processes of new workers (Mulder & Leenders, 2019), unobserved social structures (Mulder & Hoff, 2024), and key drivers of face-to-face interactions (Karimova et al., 2023).
* In sociological applications (WP2), we learn how social interaction dynamics change in classrooms (Vieira et al., 2024) and among freshmen (Meijerink et al., 2023).
* In criminology (WP3), we learned how complex relations based on rivalries (e.g. enemy of a friend) and retaliations affect can predict when violent interactions will occur and who will be involved (Gravel et al., 2023).
* In communication science, we identified key drivers of communication patterns (Karimova et al., 2023) and how they can abruptly change when emergencies occur (Shafiee-Kamalabad et al., 2023).
* In political networks, we learned how the relations affects future relations among socio-political actors (Arena et al., 2023a).
