ADHD (IV) - Stress (M) - Executive.Networks (DV)
2022-11-21
Correlation
Correlation Plot (Heat Map) showing the significance pair at alpha = 0.05
Those in Blue color is significant at alpha = 0.05.
Those in Blank/White color is NOT significant at alpha = 0.05.
In this plot, the darker shade of blue indicated a stronger relationship.
Effect of ADHD (IV) on Executive.Networks (DV) —- NOT SIGNIFICANT p<0.05
##
## Call:
## lm(formula = Executive.Networks ~ ADHD, data = df[, -1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -209.78 -47.54 6.72 49.22 169.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 110.4878 43.9292 2.515 0.015 *
## ADHD 1.1390 0.8233 1.383 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.7 on 53 degrees of freedom
## Multiple R-squared: 0.03485, Adjusted R-squared: 0.01664
## F-statistic: 1.914 on 1 and 53 DF, p-value: 0.1723ADHD is Not predicting Attention –> CANNOT consider ANY mediation model
Retest with another method
ADHD - Stress - Attention - NO Mediation
##
## Mediation/Moderation Analysis
## Call: mediate(y = "Executive.Networks", x = "ADHD", m = "Stress", data = df[,
## -1], n.iter = 10000)
##
## The DV (Y) was Executive.Networks . The IV (X) was ADHD . The mediating variable(s) = Stress .
##
## Total effect(c) of ADHD on Executive.Networks = 1.14 S.E. = 0.82 t = 1.38 df= 53 with p = 0.17
## Direct effect (c') of ADHD on Executive.Networks removing Stress = 0.23 S.E. = 0.93 t = 0.25 df= 52 with p = 0.8
## Indirect effect (ab) of ADHD on Executive.Networks through Stress = 0.91
## Mean bootstrapped indirect effect = 0.91 with standard error = 0.56 Lower CI = -0.05 Upper CI = 2.18
## R = 0.32 R2 = 0.1 F = 2.89 on 2 and 52 DF p-value: 0.0439
##
##
## Full output
## Call: mediate(y = "Executive.Networks", x = "ADHD", m = "Stress", data = df[,
## -1], n.iter = 10000)
##
## Direct effect estimates (traditional regression) (c') X + M on Y
## Executive.Networks se t df Prob
## Intercept 116.45 42.93 2.71 52 0.00904
## ADHD 0.23 0.93 0.25 52 0.80300
## Stress 2.04 1.05 1.94 52 0.05750
##
## R = 0.32 R2 = 0.1 F = 2.89 on 2 and 52 DF p-value: 0.0644
##
## Total effect estimates (c) (X on Y)
## Executive.Networks se t df Prob
## Intercept 110.49 43.93 2.52 53 0.015
## ADHD 1.14 0.82 1.38 53 0.172
##
## 'a' effect estimates (X on M)
## Stress se t df Prob
## Intercept -2.92 5.6 -0.52 53 6.04e-01
## ADHD 0.44 0.1 4.23 53 9.25e-05
##
## 'b' effect estimates (M on Y controlling for X)
## Executive.Networks se t df Prob
## Stress 2.04 1.05 1.94 52 0.0575
##
## 'ab' effect estimates (through all mediators)
## Executive.Networks boot sd lower upper
## ADHD 0.91 0.91 0.56 -0.05 2.18ADHD - Depression - Attention - No Mediation
##
## Mediation/Moderation Analysis
## Call: mediate(y = "Executive.Networks", x = "ADHD", m = "Depression",
## data = df[, -1], n.iter = 10000)
##
## The DV (Y) was Executive.Networks . The IV (X) was ADHD . The mediating variable(s) = Depression .
##
## Total effect(c) of ADHD on Executive.Networks = 1.14 S.E. = 0.82 t = 1.38 df= 53 with p = 0.17
## Direct effect (c') of ADHD on Executive.Networks removing Depression = 0.84 S.E. = 0.86 t = 0.97 df= 52 with p = 0.34
## Indirect effect (ab) of ADHD on Executive.Networks through Depression = 0.3
## Mean bootstrapped indirect effect = 0.32 with standard error = 0.3 Lower CI = -0.12 Upper CI = 1.03
## R = 0.24 R2 = 0.06 F = 1.59 on 2 and 52 DF p-value: 0.204
##
##
## Full output
## Call: mediate(y = "Executive.Networks", x = "ADHD", m = "Depression",
## data = df[, -1], n.iter = 10000)
##
## Direct effect estimates (traditional regression) (c') X + M on Y
## Executive.Networks se t df Prob
## Intercept 107.82 43.89 2.46 52 0.0174
## ADHD 0.84 0.86 0.97 52 0.3380
## Depression 1.12 1.00 1.12 52 0.2690
##
## R = 0.24 R2 = 0.06 F = 1.59 on 2 and 52 DF p-value: 0.215
##
## Total effect estimates (c) (X on Y)
## Executive.Networks se t df Prob
## Intercept 110.49 43.93 2.52 53 0.015
## ADHD 1.14 0.82 1.38 53 0.172
##
## 'a' effect estimates (X on M)
## Depression se t df Prob
## Intercept 2.39 6.03 0.4 53 0.6930
## ADHD 0.27 0.11 2.4 53 0.0201
##
## 'b' effect estimates (M on Y controlling for X)
## Executive.Networks se t df Prob
## Depression 1.12 1 1.12 52 0.269
##
## 'ab' effect estimates (through all mediators)
## Executive.Networks boot sd lower upper
## ADHD 0.3 0.32 0.3 -0.12 1.03ADHD - Anxiety - Attention - NO mediation
##
## Mediation/Moderation Analysis
## Call: mediate(y = "Executive.Networks", x = "ADHD", m = "Anxiety",
## data = df[, -1], n.iter = 10000)
##
## The DV (Y) was Executive.Networks . The IV (X) was ADHD . The mediating variable(s) = Anxiety .
##
## Total effect(c) of ADHD on Executive.Networks = 1.14 S.E. = 0.82 t = 1.38 df= 53 with p = 0.17
## Direct effect (c') of ADHD on Executive.Networks removing Anxiety = 0.43 S.E. = 0.91 t = 0.48 df= 52 with p = 0.64
## Indirect effect (ab) of ADHD on Executive.Networks through Anxiety = 0.71
## Mean bootstrapped indirect effect = 0.73 with standard error = 0.53 Lower CI = -0.16 Upper CI = 1.87
## R = 0.29 R2 = 0.09 F = 2.43 on 2 and 52 DF p-value: 0.0757
##
##
## Full output
## Call: mediate(y = "Executive.Networks", x = "ADHD", m = "Anxiety",
## data = df[, -1], n.iter = 10000)
##
## Direct effect estimates (traditional regression) (c') X + M on Y
## Executive.Networks se t df Prob
## Intercept 113.33 43.20 2.62 52 0.0114
## ADHD 0.43 0.91 0.48 52 0.6360
## Anxiety 1.92 1.13 1.70 52 0.0959
##
## R = 0.29 R2 = 0.09 F = 2.43 on 2 and 52 DF p-value: 0.0981
##
## Total effect estimates (c) (X on Y)
## Executive.Networks se t df Prob
## Intercept 110.49 43.93 2.52 53 0.015
## ADHD 1.14 0.82 1.38 53 0.172
##
## 'a' effect estimates (X on M)
## Anxiety se t df Prob
## Intercept -1.49 5.25 -0.28 53 0.778000
## ADHD 0.37 0.10 3.74 53 0.000448
##
## 'b' effect estimates (M on Y controlling for X)
## Executive.Networks se t df Prob
## Anxiety 1.92 1.13 1.7 52 0.0959
##
## 'ab' effect estimates (through all mediators)
## Executive.Networks boot sd lower upper
## ADHD 0.71 0.73 0.53 -0.16 1.87