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lamjas posted on Saturday, August 04, 2007  2:13 am



Hi, I am doing a 3level CFA for school data (students nested within classrooms; classrooms nested within schools). So far, I don't see any articles doing such kind of analysis (if you have seen such articles, please let me know some references). Therefore, I am planning to follow those articles doing 2level CFA. In the second step, it needs to calculate the within and betweenlevel covariance and obtain ICC values. The output I got is one withinlevel covariance and one betweenlevel covariance. Is it right? My major syntax is the following: usevariables classid schid Q1Q21; cluster is schid classid; analysis: type = meanstructure complex twolevel basic; output: sampstat; Thanks in advanced for you help. 


That's right. The icc's should be part of the output, or you can compute them yourself from the variances of those 2 matrices, icc = between/(between + within). 

lamjas posted on Saturday, August 04, 2007  8:37 am



Then I don't understand. When I look at the articles doing 2level CFA, they got the total, within, and between covariance/correlation matrices and calculate the ICC. But, I am doing a 3level CFA, should I get two within covariance/correlation matrices to identify the 3 levels? 


The ICC for threelevels is the level 3 variance divided by the sum of the level 1, 2, and 3 variances. 


I have the same problem. Classes are nested within schools. my syntax: cluster = school_id class_id; Analysis: Type = basic complex twolevel; But I get only one between covariance matrix and the variance is the same as in the covariance matrix of analyses with "class_id" specified as a single cluster (omitting school cluster). I expected something like two "between covariance matrices", one for school and one for class. Btw., is ICC only for manifest outcomes or exists there also an ICC for latent variables, like growth factors? 


Forgot to mention: The motivation for my first question above is that I'm interested in determining the additional amount of clustering of data within classes beyond what is caused by schools. 


The school clustering associated with COMPLEX is not modeled. The standard errors are adjusted for clustering due to school. To compute an ICC for a latent variable, divide the between variance by the sum of the within and the between variance. 

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