You should watch the video for Topic 3 to get a full description of growth modeling. The intercept growth factor in your model is defined as initial status because the time score of zero is at the first time point. If gender is scored as girls being one, a significant effect of the regression of i on gender says that girls started higher. If the regression of s on gender is significant and positive, it says girls have a higher growth rate.
I have one further question: What is the difference between non-standardized and standardized model results. I have cases where the influence of predictors is significant in the non-standardized results but not in the STDYX standardization?? Thanks a lot for your help in advance! Yvonne
See the STANDARDIZATION option in the user's guide for a description of the various standardizations available in Mplus.
It can happen that unstandardized and standardized coefficients are not not both signficant or not significant. They should be close. In these cases, I would be conservative as far as significance goes given that you are likely looking at many parameters.
1. You can consider the frequency table chi-square when you don't have covariates in the model. With sparse cell counts, you can consider bivariate fit using TECH10.
2. That would say the covariates don't only have indirect effects on the repeated measures via the growth factors, but also directly. But if you are using BIC to reach this conclusion, you want to compare models with the same variables.
1) No. But it is a good start to first get good fit without covariates. To see if the covariates have good fit you need to work with neighboring models, asking of some covariates influence some outcomes directly (which would violate the usual model assumptions).
I am using the WLSMV estimator (binary outcome variables) for my latent growth analysis with an intercept and a linear slope and have another problem:
Without any covariates, the mean slope is negative and significant. This is in accordance with the appearance of the descriptive data which show an average decline rate by 2.5%.
When introducing (significant) time-invariant covariates of the latent factors, the slope intercept in some cases is not significant or even changes to a positive estimate value. This doesn't seem to match the analysis without covariates and is contrary to the descriptive data.
Could you give me any suggestions how to understand and how to "fix" this problem?
Thank you for your quick reply! Unfortunately, I still don't see my mistake.
I try to compare my descriptive data to the predictions of the LGC model by using the following equations:
Level 1: Y = N(-t + v*i + p*s); Level 2: i = 0 + b01*x1 + b02*x2 + b03*x1x2; s = a10 + b11*x1 + b12*x2 + b13*x1x2;
When x1=0 and x2=0, s = a10. As far as I understand, when a10 is replaced by the positive slope intercept estimate of the model results section, the slope still "produces" a linear increase of Y, given all covariates are 0 and p increases linearly.
In result, the predicted Y goes up, while the outcome data of the "control group" obviously go down. Additionally, the slope intercept becomes also negative, when I use WLS instead of WLSMV.
I still don't see where this difference might come from. Could you please help me further with this?