Dear Dr. Muthèn: I have to compute a latent variable (ABS – affective balance score) that represents the latent difference of two latent variables (PA – positive affect and NA negative affect). Then I need to regress the obtained latent score (ABS) on another latent variable (IQ). I have heard of computing latent difference score which should be NA = PA@1 and then computing NA on IQ – where NA represents now the ABS. Is that syntax correct? OR do you have other suggestions how to best compute the difference of two latent variables? Thank you
Jiawen Chen posted on Tuesday, August 26, 2014 - 4:04 pm
Dear Dr. Muthèn:
I wonder if I can model the latent difference score of two different variables because so far all I have come across is about modeling latent difference score of the same variable across different waves. I have a variable called intrinsic work values, measured by three items: "if you are to look for a full-time job, how important are the following job characteristics (5 point scale): a)job is interesting; b)I make most of the decisions; c)I feel accomplished." I have another variable called intrinsic work rewards, measured by exactly the same items, only the question was asked differently, "describe how strongly you agree with the following characteristics about your current job (5 point scale)." So if I want to study how the discrepancy between work values and rewards predicts a host of outcome variables, is it appropriate to model the latent difference score between them at the same wave? Can I also do a third-order latent growth model of the latent difference score across multiple waves? Or some other method may be more suitable for what I want to study? Thank you very much!
It seems to me that one can do some version of latent difference modeling also with different variables, but I haven't tried.
Lixin Jiang posted on Wednesday, September 03, 2014 - 9:07 pm
I have two time points measures (NSsad, SSsad), which have been counter-balanced in my study. Now I want to use gender and personality trait to predict this latent change. See below that I have used it as latent growth model. However, it seems to be misleading as it is not growth itself. How do I model this "latent difference/change score model"? Thanks.
Lixin Jiang posted on Thursday, September 04, 2014 - 4:21 pm
Thank you, Roger. The results based on your codes were more consistent with the regression results from SPSS.
Meike Slagt posted on Thursday, November 13, 2014 - 9:43 am
Dear Dr. Muthen,
I'm trying to estimate latent change scores, using ordinal data.
I can get one part of that model to work (the measurement part), I can get the other part of that model to work (the difference scores, using factor scores saved from the measurement part as input for the difference scores), but I can't get the complete model to converge. That is, I get parameter estimates, but no standard errors. I'm providing the Mplus code I used in the next post, because it doesn't fit in this post anymore.
I would really appreciate advice on this: Why is my model not converging; Is my sample size just to small for this (N=190); Am I forgetting certain parameter constraints leading my model to be unidentified?
Thank you so much in advance!
Meike Slagt posted on Thursday, November 13, 2014 - 9:47 am
!Measurement Part, factorial invariance across time
neg1 BY PreN_Boos PreN_Ver PreN_Bang (1-3); neg2 BY PostN_Boos PostN_Ver PostN_Bang (1-3);
!Difference scores !Works when I save factor scores of neg1 and neg2 and use those as input. Doesn’t work when I try to estimate measurement part and difference scores in one model.
Dif_Neg BY neg2@1; !define latent change by T2 neg2 ON neg1@1; !autoregression T2 T1 neg2@0 neg1; !var at T2=0, estimate var T1 & change [neg2@0 Dif_Neg neg1]; !mean at T2=0, estimate mean T1 & change Dif_Neg ON neg1; !intercept change association
This model results in a warning: THE MODEL ESTIMATION TERMINATED NORMALLY THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 10, [ DIF_NEG ] THE CONDITION NUMBER IS -0.147D-16.