Anonymous posted on Thursday, November 25, 2004 - 8:07 pm
I have two processes measured over time, each with 4 time points. I have a hypothesis that one of the processes is driving the other. That is, that the causal flow is in a particular direction (i.e. an individual's value on process A at time t determines an individuals value on process B at time t+1...rather than an individual's value on process B at time t determines an individual's value on process A at time t+1. Can you recommend a model for this type of hypothesis?
bmuthen posted on Friday, November 26, 2004 - 6:35 am
Such direct influence from the outcomes of one process on the outcomes of another process can for example be combined with a growth model for both if that is a substantively relevant model. Or combined with an auto-regressive model for each if that is more relevant.
Anonymous posted on Saturday, November 27, 2004 - 7:10 am
Thank you. In the case of a dual growth model that includes the cross lagged effects to examine the direction of causation, is the syntax below correct and how should one specifiy the contemporaneous effect?
T2PA ON T1PB; T3PA ON T2PB; T4PA ON T3PB; T2PB ON T1PA; T3PB ON T2PA; T4PB ON T3PA;
bmuthen posted on Saturday, November 27, 2004 - 8:03 am
The syntax is correct. Contemperanous relations could for example be handled by correlating the residuals of the outcomes for the two processes.
Anonymous posted on Wednesday, December 08, 2004 - 1:47 pm
I have a related question. Imagine two models. In one model, both the repeated measures of x and the repeated measures of y are modeled as growth models and all of the growth factors are correlated. In addition, the contemporaneous effect of y on x at each measurement occassion is specified.
I think if the growth factors are correlated, then in the parallel process model, the regressions of y on x would be the regression of the residual of y on x. I would run it both ways and then look at the results and think about it.
Anonymous posted on Thursday, December 09, 2004 - 9:43 am
Thanks, I've done that and I find that the effect of the tvc in the second model is much stronger. I had interpreted it in the same way that you have, but I wanted to make sure that my thinking was correct. I guess the choice of the model will depend on one's hypothesis, that is, whether it is conceptually necessary to control for time in both constructs or if it makes more sense to assess the full effect of x on y after adjusting for change over time in y. Thank you.
I am interested in examining growth of two count outcomes negative binomial (counts; 0-10 range). In running the models as a latent growth curve, the models are able to be estimated. However, there are multiple errors that prevent the model estimation when including predictors of the growth parameters.
I was interested in running the same model in a long data format to attempt to work around this problem. Is it possible to model the growth of two different outcomes using TYPE=TWOLEVEL RANDOM?
Two issues seem to make this difficult. First, each outcome would be regressed on time, however, if modeled as ~ Outcome1 ON Time1; Outcome2 ON Time2; ~ then Time1 would be perfectly correlated with Time2.
Second, I am interested in looking at prediction of intercept and slope variation. However, this would require more than one random effect - one for each outcome.
and I'd like to improve the model fit so I'd like to allow the error terms of the DVs to covary.
so I add one line:
x1 with x2;
But the model fit information is identical to when I just had the regression commands. Running the same model in AMOS, once I allowed the error terms of the DVs to covary (by drawing a curved arrow), the model fit improved a whole lot. I wonder what I'm doing wrong?