No, you shouldn't model it as 2-level. Just use the parallel process model you have on within and the 4 growth factors will be correlated and account for within-family correlations.
Wen-Hsu Lin posted on Thursday, January 16, 2014 - 4:45 am
Thanks a lot.
Wen-Hsu Lin posted on Friday, January 17, 2014 - 7:22 pm
Sorry a follow up question. If I have a cross-dyadic variable (family cohesion), I will just need to use on statements to specify the impact of family cohesion on growth factor. Like: variable: names are id kdep1-kdep5 pdep1-pdep5 family; USEVARIABLES ARE kdep1-kdep5 pdep1-pdep5;
Hello, I am receiving an error message when I run my dyadic parallel process model. It indicates that I may have negative latent variable variances or residual variances, but I cannot find the problem. I do have a correlation between two slopes that is .69. However, I'm not sure how to fix this. Would you please be able to help me figure this out?
A big correlation in combination with several small ones can give a non-pos-def cov matrix. Sometimes correlating observed variable residuals between the processes at each time point helps in that it reduces factor correlations.
Also, I have another question. Does it matter that my processes are not measured on the same scale? For example, two of my processes are measured on a likert scale and the other is a sum scale. I'm wondering if this is the issue because the warning signs are always with the sum scale outcome variable. Thanks,