Tom posted on Thursday, December 29, 2005 - 1:47 pm
In comparing some models that I have run, when I include type = missing, the sign of some factor loadings from latent variables change.
Any idea what may be going on?
bmuthen posted on Thursday, December 29, 2005 - 6:17 pm
Type = Missing is the preferred option with missing data because it leads to using all available data. In contrast, if you don't say Type = Missing you get listwise deletion (note the sample size change).
Tom posted on Thursday, December 29, 2005 - 8:27 pm
I appreciate the contrast in sample size. Using type = missing, the sample size is 891 and without type = missing, the sample size is 774.
It is striking that the difference in t-scores would change rather dramatically with those cases deleted.
Would there be a difference if the indicators are categorical (vs. continuous)?
It sounds like you have very selective missingness if the results from listwise deletion and TYPE=MISSING; are so different. This can happen for catgegorical or continuous indicators.
Joseph posted on Tuesday, February 21, 2006 - 10:34 am
Dear Linda, I am runing SEM using Mplus. If TYPE=MISSING in the ANALYSIS command and STANDARDIZED in the OUTPUT command were used at the same time, the results only provided "Estimates" (no S.E., Est./S.E., Std, StdYX). Any solution? Thanks.