Table 4 -.
Predictors of social cognitive performance (Backward linear multivariate regressions)
Dependent variables | Individual predictors | Standardized Beta | t | p |
---|---|---|---|---|
ER 40 (Social Cognition, Emotion Processing) |
Age | −0.154 | −3.194 | 0.002 |
WRAT-3 Standard Score (Premorbid IQ) |
0.241 |
4.563 |
<0.001 |
|
PAUSS (Autism) |
−0.150 |
−3.052 |
0.002 |
|
Global Cognition | 0.220 | 4.093 | <0.001 | |
Model F = 27.031, R2 = 0.241, Adjusted R2 = 0.232 | <0.001 | |||
HINTING (Social Cognition, Mental State Attribution) |
PAUSS (Autism) |
−0.189 |
−3.701 |
<0.001 |
Global Cognition | 0.324 | 6.341 | <0.001 | |
Model F = 35.770, R2 = 0.173, Adjusted R2 = 0.169 |
<0.001 |
|||
EYES (Social Cognition, Mental State Attribution) |
Age | −0.082 | −1.975 | 0.049 |
WRAT-3 Standard Score (Premorbid IQ) |
0.443 | 9.728 | <0.001 | |
Global Cognition | 0.311 | 6.939 | <0.001 | |
Model F = 87.362, R2 = 0.434, Adjusted R2 = 0.429 | <0.001 | |||
BLERT (Social Cognition, Emotion Processing) |
Age | −0.293 | −6.778 | <0.001 |
WRAT-3 Standard Score (Premorbid IQ) | 0.331 | 6.953 | <0.001 | |
Global Cognition | 0.256 | 5.449 | <0.001 | |
Model F = 70.269, R2 = 0.381, Adjusted R2 = 0.376 | <0.001 | |||
TASIT (Social Cognition, Mental State Attribution) |
Age | −0.159 | −3.548 | <0.001 |
WRAT-3 Standard Score (Premorbid IQ) | 0.263 | 5.333 | <0.001 | |
PAUSS(Autism) | −0.185 | −4.033 | <0.001 | |
Global Cognition | 0.292 | 5.825 | <0.001 | |
Model F = 43.784, R2 = 0.340, Adjusted R2 = 0.332 | <0.001 |
ER40: Penn Emotion Recognition Test total score
HINTING: Hinting Task total score
EYES: Reading the Mind in the Eyes total score
BLERT: Bell Lysaker Emotion Recogniton Task total score
TASIT: The Awareness of Social Inferences Task total score
PANSS: Positive and Negative Syndrome Scale total score
PAUSS: PANSS Autism Severity Score (PAUSS) total score
WRAT-3: Wide Range Achievement Test-3 Reading subscale standard score
Global cognition: Global Cognitive Composite Score (t-score)
R2 refers to the variance of the dependent variable explained by the model.