# results of mediation isnt in line with compression of the included glmm models

I'll begin with my two question and all the related information (description of the research and the data and full output) will follow.

1. When i execute model1 (glmm with random intercept only for subjects): predictor (suppBin) and outcome (DtlsBinUp) and pre-intervention variables, it results with significance . when I carry out model 2: add the mediator (rlctDown) too as a predictor, the association shown in the model1 isn't significant anymore (suppBin-DtlsBinup), and for the mediator and outcome it is (rlctDown-dtlsBinup), with higher coefficient. that should imply for full mediation, meaning there isn't direct effect between the predictor and the outcome, only indirect. but when i the test mediation model (monte carlo method), I gel significant effect for total effect, direct effect and the indirect effect. how can it be that the monte carlo contradicts what shown when substracting model1 from model2? what am i missing?

2.i am having trouble in interpreting the values of the effects estimations in the monte carlo test. I understood the coefficients for the glmm as log odds that after transforming using exponential function can be understood as odds and may also be expressed as probabilities. but the estimates in the monte carlo output are much lower than those in the glmm output. so how should they be understood.

following are description and output, thank you uri.



********** predictor - outcome

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial  ( logit )
Formula: dtlsBinUp ~ suppBin * qu + ageS + gender + (1 | PD)
Data: hypoTest

AIC      BIC   logLik deviance df.resid
15351.9  15406.1  -7669.0  15337.9    17111

Scaled residuals:
Min      1Q  Median      3Q     Max
-0.6655 -0.5281 -0.5140 -0.1889  5.4472

Random effects:
Groups Name        Variance Std.Dev.
PD     (Intercept) 0        0
Number of obs: 17118, groups:  PD, 200

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.20574    0.14668 -21.856

********** predictor, mediator - outcome

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial  ( logit )
Formula: dtlsBinUp ~ suppBin * qu + rlctDown + ageS + gender + (1 | PD)
Data: hypoTest

 AIC      BIC   logLik deviance df.resid


14114.1  14176.0  -7049.0  14098.1    17110

Scaled residuals:
Min      1Q  Median      3Q     Max
-1.5239 -0.4638 -0.4552 -0.1487  6.8990

Random effects:
Groups Name        Variance Std.Dev.
PD     (Intercept) 0        0
Number of obs: 17118, groups:  PD, 200

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.69635    0.15247  -24.24   <2e-16 *
suppBin      0.14896    0.16475    0.90    0.366
qu           2.26040    0.11289   20.02   <2e-16 *
rlctDown     2.06709    0.05947   34.76   <2e-16 ***
ageS        -0.10680    0.10432   -1.02    0.306
gender      -0.02293    0.04360   -0.53    0.599

suppBin:qu   0.13720    0.17963    0.76    0.445

Signif. codes:  0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
(Intr) suppBn qu     rlctDw ageS   gender
suppBin    -0.462
qu         -0.708  0.629
rlctDown   -0.159 -0.088  0.143
ageS       -0.665  0.000 -0.018 -0.005
gender     -0.184  0.008  0.035  0.024  0.066
suppBin:qu  0.426 -0.916 -0.607  0.062  0.005 -0.029

********** predictor, mediator - outcome (function "mediate" from packege "mediation"



** script (syntax):

med.out.8.1.2.1

Simulations: 1000

• I have edited this to make it more readable, please revert any edits which change the meaning. – mdewey Apr 23 '17 at 13:43