Anonymous posted on Monday, July 08, 2002 - 11:42 am
Dear Linda & Bengt:
I am running Mplus software using LSAY data set. I intend to run a six year logitudinal model including weight variable. However, it seems to me that in LSAY each year has its own weight variable. That is for six time points, there are six weight variables for each year. According to Mplus manual, I can use a weight variable for the whole six year data. I am wondering if you know how to specify these weight variables in Mplus. If you happened to analyze LSAY data set before, would you please inform me which weight variable did you use for a longitudinal analysis? I am a doctoral student and writing up my dissertation by analyzing a latent growth model using LSAY. Your information will be highly appreciated. Thank you very much.
Anonymous posted on Tuesday, July 09, 2002 - 9:17 am
If you can rewrite your model as a univariate type=twolevel you can use time varying weights.
bmuthen posted on Tuesday, July 16, 2002 - 10:21 am
We have not used weights in our LSAY analyses. I assume that there is also a time-invariant weight variable.
Anonymous posted on Tuesday, February 15, 2005 - 4:18 pm
Dear Linda and Bengt I have an LGC model were i have time-varying binary indicators of a "treatment" at each time point. I have calculated an overall Propensity scores model, beforehand thus each individual has a PS score. Hirano et al (2003) use PS scores p(xi), as weights wi, where wi=p(xi)/[1-p(xi)]. What do we know about using the weights wi into an LGC model? Should i trust in the standard errors that MPLUS generates?
Or it is better to incorporate the paths from a PS observed variables predicting the time-varying variable for Treatment effects at each time point.
What are the differences between these two approaches?
If you use sampling weights in Mplus, the standard errors are trustworthy. I do not know what the results would be if you use propensity scores as sampling weights. There are several Web Notes on the Mplus website that discuss sampling weights in Mplus.
Because of this, I would use the propensity scores as variables in the model.
I'm trying to run some latent growth models using sampling weights. I have already successfully estimated these models without weights. Now, even when estimating the simplest unconditional growth model, I receive the message
no convergence. serious problems in interations. estimated covariance matrix non-invertible. check you starting values.
My syntax is:
USEVARIABLES ARE rc1rdirt rc2rscal rc4rdirt rc5rdirt rc1mirt rc2mscal rc4mirt rc5mirt; MISSING ARE blank; WEIGHT IS c1_5fp0;
I'm running a growth model on 3 time points for 3 groups of persons. I want to know if the trajectories of my 3 groups are the same. So I put 2 dicho variables in the model (tcseul and toseul) and I find that itís different. But, I know that in one of my groupe I have more female and I whant to be sure that the difference that I found was not because of the inequal proportion of female. How can I control this?
Thank you and there is my input
variable: names are id sexe age tdah groupe tc1 tc2 tc3 opp1 opp2 opp3 toseul tcseul tcxs oppxs;
Simone Croft posted on Tuesday, September 02, 2014 - 9:51 am
I am having difficulty running my LGM with categorical variables. I have a 2nd order model, am using the WLSMV estimator and TYPE = GENERAL. I do not have the space here to show you what I want to model, however I am getting the following condition number: -0.145D-14
Mike Zyphur posted on Sunday, February 07, 2016 - 8:51 pm
Regarding the initial purpose of this thread--the use of weights in SEM when different variables have different weights--is there any creative potential to setup such a model in Mplus 7.4? In my case, I do not have a time-invariant weight variable and my model of interest cannot be setup as TWOLEVEL.
You can also try multiple group. Most models can be setup as two-level - maybe add time specific dummy variables.
Also I would recommend questioning the weight non-invariance. Weight should be inverse proportional to the probability of selection - so there should be just one weight. Time specific weights could be constructed for some cross-sectional estimation but should not be used with a longitudinal study.
Mike Zyphur posted on Monday, February 08, 2016 - 5:09 pm
Hi Tihomir, Thanks for your thoughts!
Unfortunately, in my case, two-level models produce biased estimates with longitudinal data and autoregressive effects (Nickel, 1981). The within-group centering in two-level models causes negative autocorrelation (Arellano, 2003; for an intro see Bond, 2002). So, two-level models can't be used unless the bias caused by within-group centering is reduced, but I can't think of a way to do this. A wide-format procedure using latent variables in SEM works (Allison, 2014; Moral-Benito, 2013). If you can think of a way to solve the problem in two-level models, I'd love to hear about it! (Allowing between-level covariance among the lagged and non-lagged variables does not seem to address the issue.)
Thanks again! Mike
Allison, P. D. (2014). Maximum likelihood for dynamic panel models with cross-lagged effects. Unpublished manuscript.
Arellano, M. (2003). Panel data econometrics. Oxford: Oxford University Press.
Bond, S. R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1, 141-162.
Moral-Benito, E. (2013) Likelihood-based estimation of dynamic panels with predetermined regressors. Journal of Business & Economic Statistics, 31, 451-472.
Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49, 1417-1426.