How to interpret the mean of stvc? PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
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 Kyle posted on Sunday, March 04, 2012 - 10:58 pm
This is kyle. I am currently working on a longitudinal study consisting of three waves. The outcome is a continuous variable (Depression).

I tried to use growth curve model to explain the social factors of depression in later life. I added several time-varying covariates in the model. But I have difficulty in understanding the means of time-varying covariates.

My understandings is that stvc means the random slopes of time-varying covariates. Therefore, if the means of stvc are significant, the corresponding time-varying covariates have significant effects on the outcome variables over time.

May I ask did I interpret it correctly?

I also have difficulty in interpreting the following status: for example, stvc has significant means, but its relationships with intercept growth factor and slope growth factor are both insignificant. What does it mean? My understanding is that this stvc does not affect the growth trajectory of the outcome, but it has significant affects on the outcome variable in all 3 time points. May I ask your opinion?

Further, I add 6 time-varying covariates in the growth curve model. The means of some time-varying covariates became insignificant after I entered additional time-varying covariates. Does it mean that their effects on the outcome variable become insignificant after all other time-varying covariates are controlled for?

Many thanks for your patience!
 Linda K. Muthen posted on Monday, March 05, 2012 - 9:01 am
Your interpretation of the means of the random slopes is correct.

It can happen that the relationship between the growth factors and the random slope mean is not signficant.

Yes, the effects are conditional.
 Kyle posted on Monday, March 05, 2012 - 10:29 pm
Many thanks for your reply! I am very grateful!

In the output, I got the intercept of stvc. I guess it is because that stvc is regressed on three time-invarying covariates (age, gender and education). Gender and education are binary variables. Age is centered at its grandmean. May I ask whether the intercept of stvc is its mean in this case?

Further, in your previous reply, you suggested that stvc can be significantly associated with the outcome variables but insignificantly relate to the intercept and slope growth factors. And it is because the relationship between the growth factors and the random slope mean is not significant. I have difficulty in interpreting random slope mean.

My understanding is that stvc includes individual differences in the association between time-varying covariates and outcome variables. Therefore, it is a random slope. The effects of the time-varying covariates are different across people. Even the same score of a time-varying covariates means differently for different people. Random slope mean is the average effects of the time-varying covariates on the outcome variables.

If the mean of stvc is significant and stvc is not significantly related to intercept and slope growth factors, it means only higher level or lower level of random slope can affect the initial status or growth trajectory of the outcome. May I ask whether I interpret it correctly?
 Kyle posted on Monday, March 05, 2012 - 10:29 pm
Many thanks for your kind support again! I watched around 6 out of 9 workshop video from your website. I do learn a lot from you!

Kyle
 Kyle posted on Monday, March 05, 2012 - 10:46 pm
I am sorry, I got one more question: in the growth curve model, can time-varying covariates be binary categorical variables?
 Linda K. Muthen posted on Tuesday, March 06, 2012 - 1:17 pm
No, a mean is not estimated in a conditional model. An intercept is estimated.

Your third paragraph is correct. Your fourth paragraph is not.

Yes, time-varying covariates can be binary.

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