I would like to do a two-level EFA of a set of 8 indicators where each indicator is actually an aggregate of a further cluster level factor, and these are therefore recorded with differing precision due to differing sample sizes. I would like to take these differing precisions into account.
An example is EFA of 8 indicators where each is measured on 50 schools each year over a 10 year period. Interest is in both the between and within school factor structures. However, the indicators themselves are an average over measurements of a certain number of students within the schools, but I do not have the indicator data at the student level. However I do have access to the standard errors of the indicators and so would like to use these as inverse-variance weights at the within (ie occasion within school) level.
I have read plenty about sampling weights in Mplus, but I wonder whether inverse variance weights can be used in the WEIGHTS command in MPlus and produce valid chi-squared tests etc? For comparison, Stata calls inverse variance weights "aweights" ("analytical weights"), as distinguished from sampling weights or frequency weights.
The inverse-variance weights apply only to linear regression and actually they use residual variances rather than standard errors.
I don't see a viable approach - some corners have to be cut - you could possibly use constraint= command and use type=complex instead of type=twolevel but you have to first get the SE info converted to measurement error info.
anonymous posted on Wednesday, March 10, 2010 - 5:49 am
Hello, I am conducting an EFA within a complex survey sample (clusters and weights). When I attempt to request more than 3 factors, I receive a warning stating that no more than 3 factors are available. Is there a way to examine a model with a greater number of factors?