Excerpts

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 interaction between learning condition and post-test condition (i.e., the match effect) was also highly significant (β = 1.56, p < 0.001).

As shown in Table 2 , school bullying was an extremely significant positive predictor of depression ( β = 0.87, p < 0.001), and the fit index R 2 showed that the predictive model of school bullying on depression explained 6% of its variance.

Moreover, the second-step modeling revealed a clearly significant full-null model comparison (χ 2 = 185.6, df = 69, p < 0.001) indicating that cycle number and/or species (monkey or human) and/or stage (discrimination or reversal) and/or feature (color, direction, or shape) and/or any of the interactions between them or with training type significantly contributed to the response.

The result of Mardia's test of multivariate normality was highly significant ( z = 27.04, p < 0.001).

In contrast, the outcome scores of the experimental group decreased after the trial, implying that the volunteers in the experimental group had improved their anxiety (the outcome was recorded as 1 for high positive mood, 2 for equal positive and negative moods, and 3 for high negative moods), and the improvement was also highly significant ( p < 0.001).

All differences were highly significant ( P < 0.001).

The one-way ANOVA was highly significant, F (3,623) = 141.78, p < 0.001, η p 2 = 0.41.

Wilks’ Lambda was highly significant for the two discriminant functions ( p < 0.001).

Combining samples A and B yielded a highly significant main effect [disgust: M = 2.63, SD = 0.45; neutral: ( M = 2.46, SD = 0.39), t (50) = 3.75, p < 0.001, d = 0.53 ( Figure 3A )].

As the difference in age was highly significant between the groups [ F (3, 186) = 101, p < 0.001], it was included as a covariate. 3.2 Results for the main hypotheses In the total sample, the SC-IAT showed high internal consistency (adjusted r = 0.87), in line with our hypothesis ( H1 ).

Group analyses confirmed a highly significant mixing cost on rP3 amplitude in Old but a marginal effect in Young adults [2.8μV, F (1,29) = 22.59, p < 0.001 vs. 0.96μV, F (1,24) = 4.57, p = 0.043, respectively; Table 2 ].

As we had expected, the main effect of Manipulations was highly significant, F (3,63) = 130.82, p < 0.001, η 2 = 0.862.

Loving-kindness meditation increases connectedness to others Main effects of meditation type, chills augmentation, and chills occurrence An ANCOVA revealed Meditation Type as a highly significant predictor of connectedness to others ( Figure 1 ) following intervention ( F (1, 372) = 28.94, p < 0.001).

The ANOVA yielded a highly significant result, F (2, 114) = 50.66, p < 0.001, indicating that the magnitude of change in authenticity perceptions varied substantially by source.

Bartlett’s test of sphericity was highly significant ( χ 2 = 3876.42, df = 153, p < 0.001), confirming that the correlation matrix was appropriate for factor analysis.

By the same token, high IAS total scores were associated with high scores on TAS-20 and with high scores on two components of TAS-20, “difficulty identifying feelings” and “difficulty describing feelings.” The results from the regression analysis showed that Interoception was a moderately significant predictor of Alexithymia, explaining 13% of the variance (beta = 0.37, p < 0.001).

There was a highly significant interaction between race of observer and race of face, F 9 , 270 = 5.45, p < 0.001, η p 2 = 0.15 ( Figure 2 ).

Even though the frequency of the expressions in this category is more similar that in the rest of the categories, a Chi 2 test suggests that the difference is highly significant (χ 2 = 763,1422717; p < 0.001).

The inferential statistical outcomes further corroborate these descriptive findings, with the paired-samples t -test indicating a highly significant difference between the initial and final assessment points [ t (46) = 11.36, p < 0.001], accompanied by a large effect size (Cohen’s d = 1.66).

Test-retest reliabilities were highly significant across AOM timepoints for the risk perception index ( r = 0.814, p < 0.001).

A mixed-design ANOVA on the total efficacy score indicated a highly significant main effect of Time, F (1, 479) = 311.17, p < 0.001, η p 2 = 0.39.

The association between this variable and motivation levels was highly significant [ χ 2 (16) = 105.58, p < 0.001, Cramér’s V = 0.14], supporting the hypothesis that ‘higher usage frequency leads to stronger learning motivation’, which aligns with the theoretical connotations of the “Relevance” and “Satisfaction” dimensions in the ARCS model.

Specifically, it demonstrated the largest and most highly significant positive predictive effect on both Social Responsibility ( β = 0.344, p < 0.001 ) and Prosocial Behavior ( β = 0.271, p < 0.001 ).

The preliminary EFA showed a Kaiser-Meyer-Olkin (KMO) value of 0.967, and Bartlett’s test of sphericity was highly significant ( χ 2 = 15520.423, df = 1,176, p < 0.001), confirming that the empirical data were exceptionally suitable for factor extraction.