Return to Question

If anyone could give me any advice I would hugely appreciate it as, based on what I have here, I currently do not know how to interpret my interaction.

> summary(model)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Outcome_Variable ~ Group_Variable * Variable_A * Variable_C +      Group_Variable * Variable_B * Variable_C + (1 | Variable_ID)
   Data: crossvalidate

REML criterion at convergence: -2102.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-7.2535 -0.4783  0.0113  0.5460  4.3982 

Random effects:
 Groups      Name        Variance Std.Dev.
 Variable_ID (Intercept) 0.01559  0.1249  
 Residual                0.03115  0.1765  
Number of obs: 3950, groups:  Variable_ID, 88

Fixed effects:
                                                        Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                                            1.939e-01  5.085e-02  1.181e+03   3.813 0.000144 ***
Group_VariableGroup_2_L                               -9.593e-02  6.816e-02  1.140e+03  -1.407 0.159566    
Group_VariableGroup_3_T                               -5.413e-02  6.571e-02  1.017e+03  -0.824 0.410242    
Variable_A                                            -1.011e-02  1.155e-02  3.851e+03  -0.875 0.381359    
Variable_CLevel2_S                                    -4.811e-02  5.423e-02  3.853e+03  -0.887 0.375013    
Variable_B                                             8.054e-02  8.273e-03  3.862e+03   9.736  < 2e-16 ***
Group_VariableGroup_2_L:Variable_A                     2.542e-02  1.542e-02  3.851e+03   1.648 0.099371 .  
Group_VariableGroup_3_T:Variable_A                     3.246e-02  1.493e-02  3.851e+03   2.175 0.029705 *  
Group_VariableGroup_2_L:Variable_CLevel2_S             4.883e-02  7.282e-02  3.852e+03   0.671 0.502523    
Group_VariableGroup_3_T:Variable_CLevel2_S             2.971e-02  7.060e-02  3.851e+03   0.421 0.673929    
Variable_A:Variable_CLevel2_S                          3.740e-02  1.397e-02  3.850e+03   2.678 0.007441 ** 
Group_VariableGroup_2_L:Variable_B                     1.457e-02  1.119e-02  3.860e+03   1.303 0.192717    
Group_VariableGroup_3_T:Variable_B                     2.173e-02  1.092e-02  3.860e+03   1.990 0.046710 *  
Variable_CLevel2_S:Variable_B                          1.579e-02  1.005e-02  3.856e+03   1.570 0.116423    
Group_VariableGroup_2_L:Variable_A:Variable_CLevel2_S -2.434e-02  1.875e-02  3.850e+03  -1.298 0.194355    
Group_VariableGroup_3_T:Variable_A:Variable_CLevel2_S -4.374e-02  1.836e-02  3.850e+03  -2.382 0.017255 *  
Group_VariableGroup_2_L:Variable_CLevel2_S:Variable_B -1.081e-02  1.363e-02  3.855e+03  -0.793 0.427704    
Group_VariableGroup_3_T:Variable_CLevel2_S:Variable_B  7.997e-03  1.344e-02  3.854e+03   0.595 0.551886    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> car::Anova(model)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: Outcome_Variable
                                         Chisq Df Pr(>Chisq)    
Group_Variable                          6.3807  2   0.041157 *  
Variable_A                             31.3062  1  2.204e-08 ***
Variable_C                            110.3747  1  < 2.2e-16 ***
Variable_B                           1553.9462  1  < 2.2e-16 ***
Group_Variable:Variable_A               1.0300  2   0.597500    
Group_Variable:Variable_C              26.7630  2  1.543e-06 ***
Variable_A:Variable_C                   2.8659  1   0.090477 .  
Group_Variable:Variable_B              18.5980  2  9.152e-05 ***
Variable_C:Variable_B                   7.7037  1   0.005511 ** 
Group_Variable:Variable_A:Variable_C    5.6792  2   0.058450 .  
Group_Variable:Variable_C:Variable_B    2.1613  2   0.339370    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model <- my_outcome ~  lmerTest::lmer(Group_Variable * Variable_A * Variable_C + 
                                      Group_Variable * Variable_B * Variable_C + 
                                      (1 | ID_Variable), data = mydata)

If anyone could give me any advice I would hugely appreciate it as, based on what I have here, I currently do not know how to interpret my interaction.

Many thanks in advance.

model <- my_outcome ~  lmerTest::lmer(Group_Variable * 
    Variable_A * Variable_C + Group_Variable * Variable_B * 
    Variable_C + (1 | ID_Variable), data = mydata)

If anyone could give me any advice I would hugely appreciate it as, based on what I have here, I currently do not know how to interpret my interaction.