General Mixed Effect Model Differences Between R and SPSS

#1
Hello! I have a rather simplistic model which I have been using in SPSS that entails a general mixed effect model explaining a binary outcome. The professor offhandedly mentioned that if I should get this research published, I would want to use R instead of SPSS but she does not know how to work with R, just SPSS. Sure enough, I get marginally different results between the two statistical software and I am not sure why. I am using the Lme4 package in R and "Generalized Linear Mixed Effect Models" in SPSS

Here is the code in R
CVT_Small.mod <- glmer(Choice.Self ~ 1 + True.EV + # Fixed effects
(1 + True.EV|Subject), #Random effects
data = CVTclean, family = binomial(link = "logit")) #not sure if the logit is necessary, seems automatic


Here it is in SPSS (syntax)
GENLINMIXED
/DATA_STRUCTURE SUBJECTS=Subject
/FIELDS TARGET=DecisionSelf TRIALS=NONE OFFSET=NONE
/TARGET_OPTIONS DISTRIBUTION=BINOMIAL LINK=LOGIT
/FIXED EFFECTS=EV USE_INTERCEPT=TRUE
/RANDOM EFFECTS=EV USE_INTERCEPT=TRUE SUBJECTS=Subject COVARIANCE_TYPE=VARIANCE_COMPONENTS
SOLUTION=FALSE
/BUILD_OPTIONS TARGET_CATEGORY_ORDER=DESCENDING INPUTS_CATEGORY_ORDER=ASCENDING
MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=MODEL PCONVERGE=0.000001(ABSOLUTE)
SCORING=0 SINGULAR=0.000000000001
/EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.



Any help is appreciated. I am fine with using either software, but I just want to make sure I am doing the analysis correctly!