Dear R-mixed models members,

For my PhD, I'm working with multilevel models on educational data. However, I'm struggling with some basic concepts and implementations in R (I'm used to MLwiN). Basically, I'm trying to estimate the effect of grade retention on achievement in the years after grade retention. I split up the 'grade retention' into several conditions (reflecting the situation in Flanders, Belgium). A short summary of what I'm doing:

Dependent variable: NL (language achievement)
Independent variable: TIME (0,1,2), treat (0,1,2,3).
Following Singer & Willett (2003), I nested the 'time' into the students (IDlln) and students into schools (SCHOOL). The - very simple - example model looks as follows (I first did a propensity score matching and work with the weights of the MatchIt package, but that is not important in this example):

Model1=lmer(NL~1+TIME*factor(treat)+(1|IDlln)+(1|SCHOOL),data=datagroei.dat,REML=FALSE)

I get the following output:

Formula: NL ~ 1 + TIME * factor(treat) + (factor(treat)/TIME) + (1 | IDlln) +      (1 | SCHOOL)
   Data: datagroei.dat
  AIC  BIC logLik deviance REMLdev
7523 7592  -3750     7501    7536
Random effects:
Groups   Name        Variance Std.Dev.
IDlln    (Intercept) 0.17087  0.41337
 SCHOOL   (Intercept) 0.21577  0.46451
 Residual             0.29760  0.54553
Number of obs: 4144, groups: IDlln, 1595; SCHOOL, 48

Fixed effects:
                    Estimate Std. Error t value
(Intercept)         -0.75356    0.07910  -9.527
TIME                -0.10915    0.02055  -5.311
factor(treat)1      -0.24797    0.05480  -4.525
factor(treat)2      -0.06242    0.07868  -0.793
factor(treat)3      -0.22599    0.08254  -2.738
TIME:factor(treat)1  0.10704    0.02474   4.326
TIME:factor(treat)2 -0.24052    0.05300  -4.538
TIME:factor(treat)3 -0.37148    0.06052  -6.139

Correlation of Fixed Effects:
            (Intr) TIME   fct()1 fct()2 fct()3 TIME:()1 TIME:()2
TIME        -0.241
factr(trt)1 -0.374  0.347
factr(trt)2 -0.267  0.243  0.399
factr(trt)3 -0.251  0.233  0.347  0.244
TIME:fct()1  0.199 -0.831 -0.409 -0.201 -0.192
TIME:fct()2  0.093 -0.388 -0.134 -0.479 -0.086  0.322
TIME:fct()3  0.090 -0.339 -0.122 -0.087 -0.395  0.282    0.131

My question now is very simple: although I know the difference in growth of the treatment-groups compared to the reference group and whether this growth is significant (which it is). But I also want to test whether these treatment group differ at the last time point (at time 2); whether treatment group 2 has a significantly higher score at Time 2 compared to treatment group 3 (or 1). This sounds like I'll need to test for contrasts, isn't it? For the last two days, I read a lot about these contrasts, but I have still no clue on how to implement these in my model. Or is there a more easy answer to my question?

The (example) data can be found at: https://perswww.kuleuven.be/~u0064197/Growth_upload.txt.

Thank you in advance!

Kind regards,
Carl Lamote

PS: I hope I followed all the forum-rules in describing the situation and providing the dataset? If not, please say so!



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