I am working on a multi-level meta analysis. The model we have has a continuous Level 1 outcome, a continuous Level 1 predictor, and a continuous Level 2 predictor. I have been able to easily fit a fixed effects model to the data in MPlus, and would like to now fit a random effects model. However, I am running into serious issues with this model, and in looking more closely at the available examples for such analyses I have noticed that they all only use Level 1 predictors. Indeed, when the Level 2 predictor is excluded, the random effects model runs just fine. I am wondering if it is possible to estimate such a model including a Level 2 predictor, and if you might be able to point me to any references regarding this type of analysis?
My understanding is that in your code ID is the unique identifier for each level 2 cluster (samples in my case). I would like to apply your approach to 3 level data. In addition, I would like to test a level 3 moderator. As in your code, ID is the unique identifier for each level 2 cluster (= samples). CLUSTER is the unique identifier for each level 3 cluster (= countries):
VARIABLE: Names = Cluster Id y sd A_SB;
USEVARIABLE = y A_SB x; CLUSTER = Cluster Id; WITHIN = y x; BETWEEN = (Cluster) A_SB; DEFINE: y = y/sd; x = 1/sd;
ANALYSIS: TYPE = THREELEVEL RANDOM; ESTIMATOR = ML;