Dear experts,
          
          I use R to conduct multilevel modeling. However, I have a problem 
about the interpretation of random effect. Unlike the variables in fixed 
effects, the variables in random effects have not shown the standard error 
(s.e.) and p-value, so I don't know whether they are significant or not? I want 
to obtain these figures to make the decision. Thank you for your great help!
 
Below is the syntax and output of my program:
 
library(nlme)
dataset <- read.csv("d:/dataset.csv")
lme11 <- lme(Overall~1, random=~1|School, method="ML", data=dataset)
summary(lme11)
 
Linear mixed-effects model fit by maximum likelihood
Data: dataset
       AIC      BIC   logLik
  12637.06 12656.27 -6315.53
Random effects:
Formula: ~1 | School
               (Intercept)  Residual
StdDev:   0.2912031 0.9894488        (<-- No s.e. & p-value)
Fixed effects: Overall ~ 1
                    Value       Std.Error       DF    t-value     p-value
(Intercept) 0.7755495 0.06758038 4444 11.47596       0            (<-- Have 
s.e. & p-value)
Standardized Within-Group Residuals:
    Min          Q1           Med           Q3            Max
-3.797466473 -0.661750231 -0.007874993  0.652625939  3.549169733
Number of Observations: 4464
Number of Groups: 20
 
Best Regards,
Tommy
Research Assistant of HKIEd


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