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to examine the score of MPA and its four aspects at different levels of physical activity and then multiple comparisons between groups used the method of the least significant difference (LSD). Thirdly, using Pearson's correlation analysis to measure

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Journal of Behavioral Addictions
Authors: Hyeonseok Jeong, Jin Kyoung Oh, Eun Kyoung Choi, Jooyeon Jamie Im, Sujung Yoon, Helena Knotkova, Marom Bikson, In-Uk Song, Sang Hoon Lee, and Yong-An Chung

correlation analysis between rCMRglu and behavioral characteristics are presented in Supplementary Tables 2 and 3 . At the baseline, rCMRglu in the abovementioned cluster had negative relationships with scores of BAS-fun seeking ( β = −0.54, P = 0.004) and

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significant interaction effects. We then performed a two-way ANOVA on the brain activation for each trial type, to find a background × group interaction effect (Supplementary materials, Tables S2 and S3). Moreover, a correlation analysis was

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from .80 (exercise addiction) to .95 (hubristic pride). Table 1. Descriptive statistics, composite reliability, and correlational analysis

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suggested that cortical thickness is not affected by ICV ( Buckner et al., 2004 ). To assess the brain-behavior relationships, we performed a correlation analysis for gray matter alterations (GMV and cortical thickness in the OFC and the ACC) and the self

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since 1st bought LB Boys 0.03 −0.13 −0.04 −0.08 Girls −0.17* −0.07 0.08 −0.11 Notes: * P < 0.05 using the Holm-Bonferroni groupwise error adjustment for multiple comparisons. Loot box Behavioural Correlates A partial correlational analysis was conducted

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Journal of Behavioral Addictions
Authors: Francesco Del Prete, Trevor Steward, Juan F. Navas, Fernando Fernández-Aranda, Susana Jiménez-Murcia, Tian P. S. Oei, and José C. Perales

Non-parametric correlational analysis (Spearman’s rho) was used to test GRCS-S convergent validity with SOGS and specificity with MultiCAGE alcohol and drug scores [using the validity sample ( n  = 137), and the whole factor analysis sample ( n  = 500

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statistics regarding general smartphone use were calculated. This was followed by correlational analysis. To explore phone use and nature connectedness in more detail, multiple regression was used. Receiver operating characteristic (ROC) curves were used to

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were summarized with numbers and percentages for categorical variables or mean ± SD and ranges for continuous variables stratified by gender. Pearson correlation analysis was performed to detect linear relationships among study variables stratified by

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. The results of correlation analysis are shown in Table  2 . According to the results, all subscale scores of the adult ADHD symptoms showed significant correlations with IA, childhood ADHD, AUDIT-K scores, depression, and anxiety symptoms. On the other

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