Table 4.
Results of Hierarchical Linear Mixed Models Predicting Interaction Evaluation (N = 2,430 contact reports from 104 participants and 508 participant-specific partners)
| Predictor | Null Model | Baseline Model | Full Model |
|---|---|---|---|
| Intercept | 5.74 (0.05) *** | 5.89 (0.07) *** | 5.89 (0.07) *** |
| Level-2 Covariates | |||
| Age | 0.02 (0.01) ** | 0.02 (0.01) ** | |
| Gender: Male | −0.25 (0.13) | −0.25 (0.13) | |
| Gender: Other | −0.29 (0.38) | −0.29 (0.38) | |
| Level-1 Covariates | |||
| Interaction type: Virtual | −0.53 (0.05) *** | −0.53 (0.05) *** | |
| Strategy: Encouragement | 0.15 (0.08) | 0.15 (0.08) | |
| Strategy: Reappraisal | −0.05 (0.07) | −0.05 (0.07) | |
| Strategy: Humor | 0.21 (0.06) *** | 0.21 (0.06) *** | |
| Strategy: Distraction | −0.03 (0.06) | −0.03 (0.06) | |
| Strategy: Touch | −0.20 (0.12) | −0.19 (0.12) | |
| Main Predictor | |||
| Partner–motive residual (leave-one-out) | −0.02 (0.02) | ||
| Random Effects | |||
| Variance: Participant | 0.22 | 0.20 | 0.20 |
| Variance: Partner (within participant) | 0.17 | 0.17 | 0.17 |
| Variance: Residual | 0.69 | 0.64 | 0.64 |
| ICC (participant / partner) | 0.20 / 0.16 | - | - |
| Model Fit | |||
| Variance explained | |||
| Marginal R² | - | 0.08 | 0.08 |
| Conditional R² | - | 0.42 | 0.42 |
| Fit Statistics | |||
| AIC | 6499.12 | 6325.02 | 6325.97 |
| –2LL | 6491.12 | 6299.02 | 6297.97 |
| Comparison | |||
 Δ − 2LL (χ²) |
- | 192.10*** (df = 9) | 1.05 (df = 1) |
Note. Unstandardized estimates (b) are reported with standard errors in parentheses. All three models include random intercepts for participants and for partners nested within participants. The focal predictor is the leave-one-out partner–motive residual (see Measures); models are estimated on the 2,430 contact reports for which it could be computed. Reference categories: Gender = Female; Interaction Type = Physical; Regulatory Strategy = Problem Solving. ICC = proportion of the total variance located at the respective level in the null model (participant / partner). AIC = Akaike Information Criterion (lower values indicate better fit); Δ − 2LL tests the significance of model improvement over the preceding model. Parameter estimates and R² values refer to the REML fits; AIC, −2LL and all model comparisons refer to the corresponding ML fits. Models with a participant random intercept only and the original Specialization Score are reported in Additional file 1, Table S9.2; the corresponding estimate for the leave-one-out residual is given in Table S9.1. * p < .05. ** p < .01. *** p < .001