Additionally, a two-way interaction between ownership and valence (stock outcome) approached significance, F (1, 21) = 13.68, p = 0.001, η 2 partial = 0.394, suggesting that the gain–loss effect was inconsistent between the two different owners (self, other).
Differential associations of support system profiles with resilience One-way ANOVA results indicated highly significant differences in total resilience and all dimension scores across the support system profiles ( p < 0.001), with effect sizes (η 2 ) ranging from 0.100 to 0.107, indicating moderate to large effects (see Table 6 ).
A Linear Mixed Effect Model (with participant, scene and ordinal trial number entered as random effects) revealed that there was a highly significant effect of scene organization on the distance from the center of each quadrant ( t = 35.94, p < 0.001), with scenes that were scrambled having fixations that were 14.67% closer to the quadrant center (Figure 3 ).
Both unadjusted and adjusted analyses produced a highly significant main effect of time ( F = 66.17, p < 0.001), accompanied by a large effect size (partial η 2 = 0.68).
Results indicate that together MSR and TTR at T1 explain 41% of the variance within CMAS at T2 ( R 2 = 0.47, F (2, 343) = 151.5, p < 0.001) with highly significant contributions from both indicators (β TTR = 0.47, p < 0.001 and β NSR = 0.30, p < 0.001).
The adjusted mean difference was 25.0 points (95% CI [21.5, 28.5]), with a highly significant group effect, F (1, 64) = 50.12, p < 0.001, and a large effect size (partial η 2 = 0.439).
The ANOVA result remained highly significant ( p < 0.001) when considering the valence dimension as well.
PERMANOVA on ‘dynamisms’ (main effects and pairwise tests) Main effect and pairwise test The three-way PERMANOVA (excluding ‘shame’ and ‘guilt’) revealed highly significant main effects for ‘dynamism’ (Pseudo- F = 15.53, p = 0.001), ‘Location’ (Pseudo- F = 4.52, p = 0.001), and ‘Time’ (Pseudo- F = 18.07, p = 0.001) ( Supplementary Table S6a ).
The model yields a highly significant F -value [ F (2, 1,285] = 123.46, p < 0.001), confirming that the set of predictor variables collectively makes a statistically significant contribution to explaining variance in the outcome variable.
Crucially, the effect of latent profiles remained highly significant after covariate adjustment, F (3,221) = 31.02, p < 0.001, partial η 2 = 0.296.
In the within-group comparisons, both the inpatient group and outpatient group showed highly significant reductions in pain interference at T2–T4 compared to T1 (inpatient p ≤ 0.001, 0.001, 0.001 vs outpatient, all p ≤ 0.001).
The contribution of the 3 variables used is highly significant ( p < 0.001) in all models.
There was no significant difference between being illiterate or up to grade 5 and the other categories, but a significant trend ( p < 0.001) toward a graded association between the level of education and the total IES-R scores.
The estimated difference between the “Low Cognitive Reserve” and “Healthy” clusters decreased from −0.222 to −0.175 but remained highly significant ( p < 0.001).
Notably, a highly significant positive correlation is observed between the AUC (Area Under the Curve) of the Delay and Social discounting tasks ( p < 0.001).
The result showed a highly significant logistic component [ t (48) = 44.31, p > 0.001] consistent with the presence of a category boundary (Harnad, 1987 ) (see Figure 2 ).
For model 3 predicting the behavioral factor II as a response, which was mainly comprised of the proportion of time tail wagging, licking the trainer’s hand or face, and the frequency of lips licking, we found a clearly significant full-null model comparison (c2 = 27.44, df = 6, p < 0.001).
Due to skewness of positive mutuality, we subsequently used the Kruskal–Wallis non-parametric test, which revealed that these were highly significant differences (sensitive responding: χ 2 (2) = 23.23, p < 0.001; positive mutuality: χ 2 (2) = 102.44, p < 0.001).
The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy showed the data for Section 1 to be factorable (KMO = 0.856), Bartlett’s test of sphericity was also highly significant (χ 2 = 2868, df = 36, p < 0.001).
Not surprisingly, the main effect of serial position was highly significant in all three ANOVAs ( p < 0.001, η p 2 > 0.48).
There was no significant effect when the target was a no-onset item, F < 1, but a highly significant effect when the target was an onset, F (4, 84) = 16.18, p < 0.001: RTs increased on average by 103 ms from one onset to six onsets.
We computed a 3 × 2 repeated-measures ANOVA with factors ROUND(third-to-last, second-to-last, last) and AHA(aha, no aha) to compare mean FoW ratings, and found a highly significant main effect for ROUND [ F (2,52) = 132.27, p < 0.001, ε G-G =0.845, η p 2 =0.836, power G-G =1.0], no main effect of AHA [ F (1,26) = 1.05, p = 0.315, η p 2 = 0.039, power = 0.167], and a highly significant interaction [ F (2,52) = 15.63, p < 0.001, ε G-G =0.642, η p 2 =0.375, power G-G =0.988].
In NAF condition, there was a highly significant negative association between k values and the mean of the absolute values of ΔF2 for each PWS and each place of articulation ( r = −0.636, p < 0.001 for Pearson product-moment uncorrected scores, based on 63 observations).
Univariate test results revealed a highly significant and substantial main effect of Group on recognition speed [ F (2, 82) = 22.59, p < 0.001, ηp 2 = 0.355].
The intercept was highly significant ( F = 306.954, p < 0.001, Partial η 2 = 0.448), indicating a strong overall model fit.