0.025 in Model 2; Table 3) and age was a significant factor for predicting IAT scores in Model 2 (B = −1.011, P = 0.017). However, Model 3 did not significantly change from Model 2 (∆ P > 0.050).Table 3Hierarchical regressions predicting the score on IAT in the IGD, AUD, and HC groups.ModelDV ∆ R 2 ∆ F ∆ P B P 1 0.0585.0990.027F4-C4 gamma coherence0.2410.012 2 0.1183.3180.013F4-C4 gamma coherence0.2310.025 Age−0.2830.017 Education−0.1410.127 IQ0.0970.380 3 0.0772.2670.054F4-C4 gamma coherence0.2210.029 Age−0.3100.014 Education−0.0750.448 IQ0.1930.127 BDI-II0.2480.301 BAI−0.1400.495 BIS-110.1810.226Hierarchical regression models predicting the score on IAT were conducted for all participants in this study. Fisher’s Z-transformed scores were used for the EEG data. 2000 sample bootstrapping was applied with regression analyses, and the significance level was set at 0.050. IAT = Young’s Internet Addiction Test; IGD = Internet gaming disorder; AUD = alcohol use disorder; HC = healthy control; F3-C3 = left hemispheric frontocontral, IQ = intelligence quotient; BDI-II = Beck Depression Inventory-II; BAI = Beck Anxiety Inventory; and BIS-11 = Barratt Impulsivity Scale-11; EEG = electroencephalography. EEG coherence and AUD
