

Timeseries predicting level 2? outcome 

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Hi  I am exploring the new Mplus capabilities for time series data following version 8  but I am struggling slightly to understand how I specify some models. So to help me understand, I have the following typical example of something I am trying to do  and was hoping someone could advise me how to specify it in mplus 8. I have a data set where I have measured positive affect and negative affect in individuals daily over 18 days (PA1PA18 and NA1NA18). I expect these to have some autocorrelation and for these factors to be associated with each other. I am particularly interested in whether positive and negative affect over the measured period predicts two outcomes measured at a single time point after the completion of the positive and negative affect measures. One outcome is continuous (vac_con), and one is binary (vac_bin). They do not necessarily need to be modelled together  but I presume specifying may be different between binary and continuous outcomes. Is a twolevel approach now suitable for this kind of data, with the repeated measures at level one and outcome at level 2, and if so how would one specify the above? Thanks in advance! 


See UG ex 9.30 where your 2 outcomes vac_con and vac_bin play the role of z. The data should be in long format person 1, day1 person 1, day2 .. person 1, day18 person 2, day1 ... Note also our workshop on this topic (DSEM) in August at Johns Hopkins (waitlist is available). 


Thanks Bengt, so there is no difference in specifying binary categorical and continuous outcomes? So to extend my understanding a little further then, I have run the following model, with another binary outcome (Pregnant  0=No, 1=Yes): %WITHIN% s  PA ON PA&1; logv  PA; %BETWEEN% Pregnant ON PA s logv Yrs_Inf AGE Attempts; PA S Logv ON Yrs_Inf AGE Attempts; PA S Logv WITH PA S Logv; In interpreting the output, am I right in my understanding that "s" is the autocorrelation of PA? I am less sure about what logv means in lay terms  for example how am I to interpret a significant result for Pregnant ON logv? I would love to come to the DSEM workshop, unfortunately I am UK based so attendance is not likely to be practical. 


No, the categorical outcome must be put on the Categorical list. Yes, is is the random autcorrelation varying across subjects. Logv refers to the residual variance on Within. As variability increases the probability of Pregnant increases/decreases. You missed a workshop in the Netherlands in July. See the DSEM handouts for that which are on our home page. And, our Hopkins workshop  which is longer  will be videotaped and posted on our website. 

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