We have a quick question about data structure. We are working with a two-level model and need to calculate cluster averages for level two. (we only have data on the individual level but have to use class-aggregates) Is this possible with mplus or do we have to use spss for this? But if we have to use SPSS we have the problem with missing datas... Thank you!
I have a question about the cluster averages. Assuming a two-level analysis (level 1 is the individual level and level 2 is the group level), are the cluster averages calculated from level one or level two variables? I am also not clear how the cluster averages are calculated when you have multiple predictors at both levels.
I was not aware of the CLUSTER_MEAN option. I am using CHILDID as the cluster variable, school aggression as the dependent variable, parenting and social skills as the level-one predictors, and child gender and family structure as the level-two covariates. (1) Would I use parenting and social skills to calculate two separate cluster averages? (2) How is the CLUSTER-MEAN option different from (1)? And (3) why do we need to calculate cluster averages when cluster averages are not used in the MODEL statements?
See Example 9.1 to see how individual-level variables are handled in the between part of the model for TYPE=TWOLEVEL. There are two options - creating a cluster-level variable or latent variable decomposition.
Thank you, example 9.1 has answered my questions above. I have two more questions: How is missing data handled in the first option (creating a cluster-level variable)? Is the second option (latent variable decomposition) a better way at handling missing data?
In creating a cluster variable, all non-missing values within each cluster are averaged and assigned to each cluster member. If a cluster contains all missing values, each cluster member is assigned a missing value.
With latent variable decomposition of a dependent variable, all observations are used. With latent variable decomposition of an independent variable, observations with missing data on the variable are excluded from the analysis.