Table 4.

Hierarchical Linear Model of Daily Affect Across Valence Regressed on Negative Interpretation Bias from Both Image Types

Predictor	b	SE	t	p
Main effects
Valence (positive)	8.85	0.51	17.46	<.001***
Face negative interpretation bias	0.13	0.06	2.25	.026*
Scene negative interpretation bias	0.21	0.11	1.99	.049*
Ambiguous face RT	−0.001	0.01	−0.13	.897
Ambiguous scene RT	−0.01	0.01	−0.58	.565
Interaction effects
Valence × face negative interpretation bias	0.02	0.03	0.80	.426
Valence × scene negative interpretation bias	−0.22	0.05	−4.00	<.001***
Valence × ambiguous face RT	0.01	0.01	2.00	.045*
Valence ambiguous scene RT	−0.01	0.01	−1.07	.286
Interaction simple slopes
NA ~ face negative interpretation bias	0.13	0.06	2.25	.026*
PA ~ face negative interpretation bias	0.15	0.06	2.64	.009**
NA ~ scene negative interpretation bias	0.21	0.11	1.99	.049*
PA ~ scene negative interpretation bias	−0.004	0.11	−0.04	.972
NA ~ ambiguous face RT	−0.002	0.01	−0.13	.897
PA ~ ambiguous face RT	0.01	0.01	0.85	.398
NA ~ ambiguous scene RT	−0.01	0.01	−0.58	.565
PA ~ ambiguous scene RT	−0.01	0.01	−1.11	.270

Note. Model included 6,180 observations across 110 participants. NA = negative affect; PA = positive affect; RT = response time. Valence is coded with NA = 0 and PA = 1. PA scores were also reversed scored so that larger values reflect less PA. This was done so that negative interpretation bias effects would be positive for both positive and negative affect and interaction terms could compared the magnitude of the linear relationships between positive and negative affect. Main effects are interpreted when valence = 0 (for negative affect specifically).

^*p < .05.

^**p < .01.

^***p < .001.