## 4 Results
### Multi-Level Mixed Effects Regression  
A multi-level mixed effects regression model was employed to account for the nested data structure (individuals within teams, teams within groups). With 84 data entries spread across 28 teams, we opted to group our model by the variable group to categorize teams into treatment and control groups. Simultaneously, we modeled the individual teams as a random effect within our null model. This approach better reflects the nested structure of the data while accounting for inherent variability at the team level. The general structure of our approach involved creating a null model to establish a baseline, adding control variables to account for confounding factors, and then constructing a full model that integrates all relevant predictors, see Table 4. This approach aligns with the general structure of hierarchical linear models [81]. The null model (AIC = 252.94) revealed negligible variance at the group level (ICC = 0.0086). The Marginal R\({}^{2}\) and Conditional R\({}^{2}\) for the null model were 0.0099 and 0.0184, respectively, showing the low variance explained by the group-level random effects alone. The null model indicated that neither the baseline level of slope nor the variation across team numbers significantly contribute to the model's predictions, as shown by the non-significant intercept and the random effects of team numbers. There is a small variance component for the grouping variable, suggesting minimal variability in performance due to differences between the control and treatment groups. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Test Used** & \(p\)**-Value** & **Effect Size** \\ \hline Brightness (Slope) & Mann–Whitney U & 0.0046 & \(-\)0.17 *** \\ Happy (Std) & Mann–Whitney U & 0.0063 & 0.19 *** \\ Performance Slope & Mann–Whitney U & 0.0159 & 0.17 * \\ Abs. Utterances (Std) & \(-\)test & 0.0165 & \(-\)0.53 ** \\ Happy (Median) & Mann–Whitney U & 0.0198 & 0.16 * \\ Facial Arousal (Max) & Mann–Whitney U & 0.0634 & 0.13 * \\ Surprise (Slope) & Mann–Whitney U & 0.0788 & \(-\)0.10 * \\ Facial Valence (Max) & Mann–Whitney U & 0.0900 & 0.12 * \\ Abs. Interruptions (Min) & Mann–Whitney U & 0.0958 & 0.11 * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Differences Across Control and Treatment Groups. The inclusion of control variables (gender composition, experiment time, emotional intelligence, and prior acquaintance) improved the model's explanatory power (average AIC = 251.65, marginal R\({}^{2}\) = 0.1431, conditional R\({}^{2}\) = 0.1655), highlighting the importance of gender diversity (Gender Composition, \(p\) < 0.05) and pre-existing social ties (Acquaintance, \(p\) < 0.05) for team performance. The full model, incorporating all 31 features obtained after the VIF calculation, further enhanced the model's explanatory power (average marginal R\({}^{2}\) = 0.6483, conditional R\({}^{2}\) = 0.6604). The AIC also increased to 341.31, which, despite being higher than the control model, is not un-expected due to the introduction of a high number of parameters. Notably, the model revealed a nuanced relationship between interruptions and performance, where absolute interruptions negatively impacted performance (Abs. Interruptions (Mean), \(p\) < 0.01), but a positive trend in relative interruptions over time was associated with enhanced performance (Rel. Interruptions (Slope), \(p\) < 0.05). Additionally, increased communication volume (slope of absolute utterances) correlated with better performance