Anonymous posted on Wednesday, August 10, 2005 - 12:10 am
Hi, I've just started using MPlus. I'm testing a CFA-model and I'd like to fix the CORRELATION between the two factors to one. "f1 WITH f2 @ 1" seems to refer to the covariance, which causes a bad model fit in my case. How can I refer to the correlation between the two factors?
If you free the first factor loading of f1 and f2 and set the metric of the factors by fixing the factor variances to one (f1@1f2@1;), then f1 WITH f2 @1; will refer to a correlation.
Anonymous posted on Wednesday, August 10, 2005 - 8:54 am
Thank you very much !!!
anonymous posted on Tuesday, January 16, 2007 - 8:29 pm
I have performed a CFA with 7 factors. These factors represent dimensions under a bigger construct, which I am not testing. I want to say that these 7 represent multiple dimensions of this construct. CFA results show that these 7 factors are correlated. I am using them then to predict a binary outcome. In my logit/probit model should I force these factor to not correlate? What would it be if I do?
If, besides factor 1, and a regression on that factor, I add a second factor which correlates with factor 1, but is not included in the regression, the effect sizes of the regression change. This seems logical, but the exact interpretation is not clear to me. Could you clarify what happens there?
If I save the factor scores and use them in the exact same regression in spss I get slightly different coefficients, p-value and explained variance, resulting in some variables gaining a significant effect. Do you know what causes these differences? (I use the WLSMV as the factor items are categorical)
As I understand it, obtaining a factor correlation >1 means the model is not viable. I have two questions regarding this:
1) If I am comparing a two-factor model that demonstrates a correlation >1 between factors with a one-factor model that fits the data, does this mean it is correct to say that the one-factor model fits the data better?
2) Would it be appropriate to constrain the factor correlation to 1 or less between the two factors for comparison purposes, or will this inevitably result in an error? I have tried this, but still end up with an error saying that psi is not positive definite.
Hello, I have a very simple question. I completed several CFAs. I need to add the correlations between factors to a report that I am working on. There are about 100 models. Is there a way to save the correlations into a file? Obviously, having to open each output file and do a copy and paste into Excel or Word is quite tedious. Thanks.
You can save the results using the RESULTS option of the SAVEDATA command but that is probably just as tedious. See in the left margin of the homepage Using Mplus with R.
Alice posted on Tuesday, November 10, 2015 - 5:05 pm
Dear. Professors, Iím wondering about the meaning for the three different models.
1. Second-order model (f1 by u1-u10; f2 by u11-u20; f3 by u21-u30; f3 by f1 f2 f3) 2. First-order model with correlated factors (f1 by u1-u10; f2 by u11-u20; f3 by u21-u30) 3. First-order model with correlated factors and correlated factor-residuals (f1 by u1-u10; f2 by u11-u20; f3 by u21-u30; f1 WITH f2; f1 WITH f3; f2 WITH f3)
What does #3 means - the residual variance of f1 correlates with the residual variance of f2 and the residual variance of f3? How does this differ from #1 and #2?
Iím trying to replace model #1 with #2 or #3 because the tests that had estimation problems using #1 showed no problem when using #2 and #3. Which one will be conceptually closer to #1?