Table 5.

Regression Models Predicting Alcohol Problems from IATs and Explicit Predictors.

	Alcohol Problems B	SE B	t	Cohen’s d
Drinking Identity IAT
Gender	0.16	0.16	1.00	0.12
Drinking Identity IAT	0.53*	0.21	2.49	0.29
ASCS	0.13***	0.02	6.26	0.74
IAT × ASCS	−0.07	0.04	−1.60	0.19
Alcohol Excitement IAT
Gender	−0.14	0.16	−0.92	0.11
Alcohol Excitement IAT	0.33	0.20	1.64	0.20
DMQ-Enhance	0.64***	0.07	8.72	1.04
IAT × DMQ-Enhance	−0.23	0.19	−1.23	0.15
Alcohol Approach IAT
Gender	−0.02	0.17	−0.14	0.02
Alcohol Approach IAT	0.50†	0.26	1.94	0.23
BAS-Fun	0.21***	0.04	5.51	0.66
IAT × BAS-Fun	−0.06	0.11	−0.50	0.06
Alcohol Cope BIAT
Gender	−0.23	0.16	−1.48	0.17
Alcohol Cope BIAT	−0.21	0.18	−1.13	0.13
DMQ-Cope	0.95*	0.12	7.69	0.91
BIAT × DMQ Cope	0.11	0.26	0.41	0.05
Combined IAT Model
Gender	0.13	0.18	0.72	0.09
Drinking Identity IAT	0.77**	0.22	3.45	0.42
Alcohol Excitement IAT	0.42	0.24	1.73	0.21
Alcohol Approach IAT	0.29	0.29	1.01	0.12
Alcohol Cope BIAT	−0.07	0.20	−0.36	0.04

Note. IAT scores and explicit predictors were grand-mean centered. Gender was dummy-coded (0 = men, 1 = women). Cohen’s d = 2t/√df. The regression models used generalized linear models with a negative binomial log link. ASCS = Alcohol Self-Concept Scale. DMQ-Enhance = the enhance subscale of the Drinking Motives Questionnaire; DMQ-Cope = the coping subscale of the Drinking Motives Questionnaire; BAS-Fun = the fun-seeking subscale of the Behavioral Activation Scale. Corrected alpha = .029.

^†p < .053.

^*p < .029.

^**p < .01.

^***p < .001.