Message/Author |
|
|
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! |
|
|
Mplus does not currently have a function to create cluster averages automatically. This is something we are currently adding. |
|
|
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. Thanks! |
|
|
If you are referring to the CLUSTER_MEAN option, level 1 variables are used to create a level 2 variable where the level 2 variable is the average of the level 1 variable for each cluster. |
|
|
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. |
|
|
Thank you, Linda. This has been really helpful. |
|
Back to top |