Thanks for this nice background introduction.
One more question: Do you know any software that can apply FIML to exploratory
FA? it seems that both Amos and Lisrel feature confirmatory FA with regarding
to FIML. This may not be a STATA discussion.
Thanks
SR Millis <[email protected]>:
> Full information maximum likelihood (FIML) uses only
> the observed data. To over-simplify, FIML partitions
> the cases into subsets with the same patterns of
> missing observations. All available statistical
> information is extracted from each subset and all
> cases are retained in the analysis.
>
> More formally, mean vectors and covariance matrices
> are formed for cases that have the same pattern of
> observed data. Once the mean vectors and covariance
> matrices have been formed, the FIML approach of
> Arbuckle (1996) uses the fact that for the i-th case,
> the log-likelihood function can be expressed as:
>
> log Li=Ci - 1/2log|Si| - 1/2(xi-mui)'S^-1(xi-mui)
>
> and the log likelihood of the entire sample is the sum
> of the individual log likelihoods. The likelihood is
> maximized in terms of the parameters of the model.
>
> As a practical matter, FIML can be easily implemented
> in multiple regression and factor analysis models to
> handle missing data to that no cases are lost.
>
> Scott Millis
>
> --- [email protected] wrote:
>
> > Then the analysis would be only on the observed
> > data? Or is there any hidden
> > imputation type of process going on? Thanks!
>
>
>
> Scott R Millis, PhD, MEd, ABPP (CN,CL,RP), CStat
> Professor & Director of Research
> Dept of Physical Medicine & Rehabilitation
> Wayne State University School of Medicine
> 261 Mack Blvd
> Detroit, MI 48201
>
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