Then you are probably thinking of the precision with which the factor scores can be estimated for each individual. This topic is studied in Item Response Theory under the heading information curves which describe the inverse of standard error for each estimated factor score (called theta-hat in IRT). The curves vary over true theta values. Mplus provides these information curves in its plot command; see the UG and also this web site's description of IRT capabilities in Mplus.
RuoShui posted on Monday, September 23, 2013 - 8:11 pm
I am new to use SEM with categorical indicators. I have two independent latent variables: f1 has 5 binary indicators (yes/no), f2 has 5 ordinal indicators (ordered as 0,1,2,3). f3 and f4 have continuous indicators. v1 and v2 are observed test scores. I wonder if the following syntax is correct? Thank you very much!
use variables X1-X20 v1 v2 CATEGORICAL ARE X1-X10
f1 BY X1-X5; f2 BY X6-X10; f3 By X11-X15; f4 BY X16-X20; f3 f4 v1 v2 on f1 f2; v1 v2 on f3 f4; TYPE=Basic;
use variables X1-X20 v1 v2 CATEGORICAL ARE X1-X10 MODEL: f1 BY X1-X5; f2 BY X6-X10; f3 By X11-X15; f4 BY X16-X20; f3 f4 v1 v2 on f1 f2; v1 v2 on f3 f4;
See the examples in Chapter 5 of the user's guide.
RuoShui posted on Wednesday, September 25, 2013 - 4:49 am
Thank you very much for the information. My model cannot converge. I know that my observed test scores are on a different scale and result in large variance. I tried to rescale the variance. The model had a poor fit. The modification indices suggest me correlating f3 and f4. Can I do this?
RuoShui posted on Wednesday, September 25, 2013 - 5:07 am
I am sorry. It was actually after I rescaled the test scores , the model won't converge unless I correlate f3 and f4. What does this suggest? Can I do it?