I am doing mixed method model for a continuous dependent variable and
Fit Model(
Y(:usg_dev_data),
Effects(:Phase, :level, :experience),
Random Effects(:Subject ID),
NoBounds(1),
Personality("Mixed Model"),
Run(Repeated Effects Covariance Parameter Estimates(0))
)
while doing the same in R as
model2 <- lmer(usg_dev_data ~ Phase + level + experience + (1 | Subject_ID), data = df)
I am getting very different parameter estimates
from R
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.40991 0.60584 42.00000 3.978 0.000269 ***
PhaseX 1.80972 0.43405 42.00000 4.169 0.000149 ***
PhaseY 2.37500 0.35440 42.00000 6.701 3.89e-08 ***
levelMid career -0.13815 0.69689 42.00000 -0.198 0.843818
levelSenior -0.07185 0.68069 42.00000 -0.106 0.916436
levelTrainee -0.48593 0.57934 42.00000 -0.839 0.406355
experience1 0.57938 0.57071 42.00000 1.015 0.315827
experience2 0.86185 0.87452 42.00000 0.986 0.330013
experience3 1.63852 0.63288 42.00000 2.589 0.013169 *
while from JMP
I am unable to determine why this is so different?