```

Model5 = glmer(GatesScore ~ zAge + zWordAttack + zKnowIt + zNarrativity + zWordConcreteness +  zRefCohesion + zDeepCohesion + zArousal + (1|ID) ,data = Brussels3, family = binomial)
summary(Model5)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## GatesScore ~ zAge + zWordAttack + zKnowIt + zNarrativity + zWordConcreteness +  
##     zRefCohesion + zDeepCohesion + zArousal + (1 | ID)
##    Data: Brussels3
## 
##      AIC      BIC   logLik deviance df.resid 
##   7448.7   7518.1  -3714.4   7428.7     7574 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3799 -0.5961  0.3290  0.5390  2.8345 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.7341   0.8568  
## Number of obs: 7584, groups:  ID, 158
## 
## Fixed effects:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        1.19515    0.07625  15.673  < 2e-16 ***
## zAge               0.36317    0.07920   4.585 4.53e-06 ***
## zWordAttack        0.74632    0.08213   9.087  < 2e-16 ***
## zKnowIt            0.49479    0.07874   6.284 3.30e-10 ***
## zNarrativity       0.22444    0.04393   5.109 3.23e-07 ***
## zWordConcreteness -0.29040    0.04064  -7.145 8.99e-13 ***
## zRefCohesion       0.11575    0.04100   2.823  0.00476 ** 
## zDeepCohesion      0.22681    0.03279   6.916 4.64e-12 ***
## zArousal           0.29129    0.03970   7.337 2.18e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) zAge   zWrdAt zKnwIt zNrrtv zWrdCn zRfChs zDpChs
## zAge         0.036                                                 
## zWordAttack  0.050  0.283                                          
## zKnowIt      0.045  0.042 -0.253                                   
## zNarrativty  0.028  0.007  0.014  0.010                            
## zWrdCncrtns -0.038 -0.009 -0.020 -0.013  0.454                     
## zRefCohesin  0.017  0.003  0.007  0.005 -0.565 -0.534              
## zDeepCohesn  0.033  0.010  0.019  0.014 -0.074  0.014 -0.195       
## zArousal     0.041  0.009  0.020  0.014  0.179 -0.395  0.282 -0.367
Model6 = glmer(GatesScore ~ zAge + zWordAttack + zKnowIt + zWordConcreteness +  zRefCohesion + zDeepCohesion + zArousal*zNarrativity  + (1|ID) ,data = Brussels3, family = binomial)
summary(Model6)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: GatesScore ~ zAge + zWordAttack + zKnowIt + zWordConcreteness +  
##     zRefCohesion + zDeepCohesion + zArousal * zNarrativity +      (1 | ID)
##    Data: Brussels3
## 
##      AIC      BIC   logLik deviance df.resid 
##   7449.6   7525.9  -3713.8   7427.6     7573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.2169 -0.5960  0.3271  0.5399  2.9079 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 0.7348   0.8572  
## Number of obs: 7584, groups:  ID, 158
## 
## Fixed effects:
##                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            1.17716    0.07810  15.072  < 2e-16 ***
## zAge                   0.36337    0.07924   4.586 4.52e-06 ***
## zWordAttack            0.74657    0.08217   9.086  < 2e-16 ***
## zKnowIt                0.49502    0.07878   6.284 3.30e-10 ***
## zWordConcreteness     -0.26600    0.04668  -5.698 1.21e-08 ***
## zRefCohesion           0.11007    0.04136   2.662  0.00778 ** 
## zDeepCohesion          0.23787    0.03444   6.906 4.97e-12 ***
## zArousal               0.28987    0.03963   7.314 2.59e-13 ***
## zNarrativity           0.24083    0.04648   5.181 2.21e-07 ***
## zArousal:zNarrativity -0.04016    0.03785  -1.061  0.28868    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) zAge   zWrdAt zKnwIt zWrdCn zRfChs zDpChs zArosl zNrrtv
## zAge         0.034                                                        
## zWordAttack  0.048  0.283                                                 
## zKnowIt      0.044  0.042 -0.253                                          
## zWrdCncrtns -0.137 -0.007 -0.015 -0.010                                   
## zRefCohesin  0.044  0.003  0.007  0.004 -0.524                            
## zDeepCohesn -0.034  0.010  0.020  0.014  0.162 -0.223                     
## zArousal     0.045  0.009  0.020  0.014 -0.357  0.285 -0.362              
## zNarrativty -0.047  0.007  0.015  0.010  0.540 -0.573  0.035  0.152       
## zArsl:zNrrt  0.215 -0.003 -0.004 -0.004 -0.490  0.128 -0.302  0.030 -0.335
Model7 = glmer(GatesScore ~ zArousal*zNarrativity  + (1|ID) ,data = Brussels3, family = binomial)
summary(Model7)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: GatesScore ~ zArousal * zNarrativity + (1 | ID)
##    Data: Brussels3
## 
##      AIC      BIC   logLik deviance df.resid 
##   7651.7   7686.4  -3820.9   7641.7     7579 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.8360 -0.6178  0.3498  0.5427  2.6838 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  ID     (Intercept) 1.707    1.307   
## Number of obs: 7584, groups:  ID, 158
## 
## Fixed effects:
##                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            1.15674    0.11024   10.49   <2e-16 ***
## zArousal               0.30413    0.03476    8.75   <2e-16 ***
## zNarrativity           0.41860    0.03463   12.09   <2e-16 ***
## zArousal:zNarrativity -0.06886    0.03102   -2.22   0.0264 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) zArosl zNrrtv
## zArousal    -0.008              
## zNarrativty  0.011  0.520       
## zArsl:zNrrt  0.118 -0.310 -0.246
plot(effect("zArousal:zNarrativity", Model7), multiline = TRUE)

vif(Model7)
##              zArousal          zNarrativity zArousal:zNarrativity 
##              1.440660              1.386241              1.118735
1/(vif(Model7))
##              zArousal          zNarrativity zArousal:zNarrativity 
##             0.6941263             0.7213752             0.8938666

VIF= Variance inflation factor, which is the reciprocal of tolerence Read the bottom table for tolerence values from Model 7