How would I specify a model for a random intercept and slope growth model of time-series data, where I expect there to be autocorrelation?
For example I have 100 participants who completed daily measures of stress over 3 weeks during a potentially stress inducing period. But I expect some autocorrelation between daily measures. I want to be able to extract the factor scores for use in later analyses.
Thanks Bengt, appreciate these responses. I have watched all the DSEM videos on the site - which have been a great help - but as a beginner I think I sometimes miss the obvious! I will keep my eye out for the JHopkins talks.
So based on the smoking urge example I tried the following:
USEVARIABLES ARE ID PA Pregnant Yrs_Inf AGE Attempts time; WITHIN = time; BETWEEN = Yrs_Inf AGE Attempts Pregnant; CATEGORICAL = Pregnant; CLUSTER = ID; LAGGED = PA(1);
We are using syntax provided by a colleague as an example of latent trajectory analysis. We understand everything in the syntax except two things, which I think have to do with autocorrelation in some way. We are unable to reach the colleague for clarification.
The purpose of the model is to understand changes in a variable leading up to, and then following, a major life event. In the syntax below "xm18" would mean the wave 18 measurement waves prior to the event; "x18" would mean the wave 18 waves after.
First, why set the path from a variable x[t] to x[t+1] (e.g., x18 ON x17@1) to one, as opposed to estimating x[t] to arrive at the variance in x[t+1] that is not related to x[t]?
Second, regardless of setting vs estimating, why would one regress the earlier time point on the later time point (e.g., xm18 ON xm17) since xm18 is measured BEFORE xm17? This is done for all the waves before the event, but after the event the regressions are set up in the intuitive (to us at least) way.