As expected, there was a highly significant main effect of tempo, with faster rates producing shorter ITIs [ F (2,100) = 9559.96, p < 0.001, η p 2 = 0.995].
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After entering both mediators (personal norm and perceived health risks) into the model, the intervention predicted perceived health risks significantly ( B = 0.20, p = 0.04, CI [0.014; 0.384]), which in turn predicted participants’ willingness to offer expired but still edible food highly significant ( B = −0.09, p < 0.001, CI [−0.119; −0.067]).
Results from the critical ratio method indicated that all items had highly significant critical values ( P < 0.001).
Test–retest repeatability correlation coefficients for the two domain scores were highly significant ( p < 0.001).
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).
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).
The summary of between-subjects’ effects in Table 1 indicates that both Age and Instruction have a highly significant influence on rate of reading scores ( 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 ).
The results show that the number of patents of invested enterprises has an extremely significant positive correlation with the human capital index ( P < 0.001), and a significant positive correlation with the education level of personnel, the proportion of engineering professionals ( P < 0.05).
There were highly significant, negative correlations between in-body and out-of-body experiences ( r 49 between −0.53 and −0.64, all p < 0.001 for each condition; r 49 = −0.63, p < 0.001 across conditions).
Although the association between intensity and liking was highly significant, F (1, 151.45) = 122.67, p < 0.001, the interaction between emotion type and presentation frequency was eliminated.
The chi-square goodness of fit test was highly significant (χ 2 = 6855.23, df = 113, p < 0.001), but this is usual in case of large samples like ours.
In (4) the effect of self-obese on 6 cat_requests is negative and highly significant ( p value < 0.001).
Conversely, the correlation between each individual’s learning slope and mean threshold value on day 1 of learning was highly significant ( r = −0.91, p < 0.001), which indicates that those who showed a steep learning function (slower learners) started off with a high threshold, whereas those with a flatter learning curve (faster learners) were already close to ceiling performance on the first day of training, as may be seen in Figure 4 .
This showed a highly significant main effect of emotional expression [F(1, 13) = 21.6, p < 0.001] and a marginal effect of presentation [F(1, 13) = 4.4, p = 0.06], while the interaction between the two factors was not significant [F(1, 13) = 0.14, p > 0.1].
ANOVAs applied to latencies, with Condition ( unrelated , far , close ) as a fixed variable, showed a highly significant effect of Condition by participants, F 1(2, 126) = 9.07, MSE = 12028, p < 0.001, which was marginally significant in the analysis by items, F 2(2, 30) = 2.85, MSE = 2456, p = 0.074.
Simple main effect analysis demonstrated that, in controls, differences between self and other were highly significant at Pz ( F 1,20 = 13.85, p = 0.001).
This test was highly significant, p < 0.001, and indicated that the two correlations between Weber fractions of the two visual cue control conditions within each presentation format were significantly different from one and other: Weber fractions between the two visual cue controls in the sequential conditions were correlated, while Weber fractions between the two visual cue controls in the simultaneous conditions were not related.
Results demonstrated a statistically highly significant main effect with a large effect of working memory load, F (1, 30) = 57.48, p < 0.001, η p 2 = 0.657 indicating that task performance declined as memory load increased.
The salience maps were compared pairwise in terms of the Area Under Curve (AUC) for each banner (Le Meur and Baccino, 2012 ); the average correlation was highly significant ( r = 0.81 p < 0.001).
Analysis of the relationship between themes and emotions Addressing the study’s primary analytical aim of characterizing the association between thematic content and viewer-reported emotional responses, the distribution of emotional responses across different types of artistic themes revealed significant differences, with chi-square analysis confirming a highly significant overall association ( χ 2 = 234.7, df = 12, p < 0.001, Cramer’s V = 0.24).
Exceptions were highly significant positive correlations ( p < 0.001) between the statements concerning duty scheduling and work environment in general ( r = 0.47), comments about quality of supervision and personnel capacity ( r = 0.40), as well as salary and working hours ( r = 0.40).
For the color, identity, and location questions, the only independent variable that had predictive power was semantic relatedness (Figure 5 A), which was highly significant in all cases ( p < 0.001).
The final regression model predicting pro-ocean environmental behavior was highly significant [ F (5,1200) = 775.652, p < 0.001], explaining 76.4% of the variance ( R 2 = 0.764, Adjusted R 2 = 0.763).
This interpretation of digit specific influences of increasing magnitude are corroborated by the fact that the raw correlation of unit sum with overall RT (which is one possible measure of problem size in addition) was highly significant ( r = 0.48, p < 0.001).