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 Wen-Hsu Lin posted on Tuesday, January 14, 2014 - 11:31 pm
Hi, my data included 5 waves of depression of a kid and one parent. I would like to model the growth of the depression of the dyadic data.Should I use following syntax:

variable:
names are id kdep1-kdep5 pdep1-pdep5;
within=kdep1-kdep5 pdep1-pdep5;
cluster=id;
analysis: type= twolevel random;
model:
%within%
ki kp|kdep1@0 kdep2@1 kdep3@2 kdep4@3
kdep5@4;
pi pp|pdep1@0 pdep2@1 pdep3@2 pdep4@3
pdep5@4;

%between%
ki kp pi pp;


or regular parallel growth curve modeling syntax?
 Bengt O. Muthen posted on Wednesday, January 15, 2014 - 10:44 am
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;

model:

ki kp|kdep1@0 kdep2@1 kdep3@2 kdep4@3
kdep5@4;
pi pp|pdep1@0 pdep2@1 pdep3@2 pdep4@3
pdep5@4;

ki kp pi pp on family;
 Bengt O. Muthen posted on Saturday, January 18, 2014 - 3:20 pm
Right.
 Danyel A.Vargas posted on Wednesday, January 29, 2014 - 1:31 pm
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?

Thanks so much,

Danyel
 Bengt O. Muthen posted on Wednesday, January 29, 2014 - 1:45 pm
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.
 Danyel A.Vargas posted on Wednesday, January 29, 2014 - 1:59 pm
OK - thanks. I have a dyadic model with husbands' and wives' outcome vars. at 4 time points with three constructs. Is this what you mean for each time point?

d1accsen with m1accsen d1deptts
m1deptts d1relaq1 m1relaq1;

m1accsen with d1deptts
m1deptts d1relaq1 m1relaq1;

d1deptts with m1deptts d1relaq1 m1relaq1;

m1deptts with d1relaq1 m1relaq1;

d1relaq1 with m1relaq1;
 Danyel A.Vargas posted on Wednesday, January 29, 2014 - 5:31 pm
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,

Danyel
 Bengt O. Muthen posted on Thursday, January 30, 2014 - 12:25 pm
On the question in your first message - Yes. But you can write your WITH statements more succinctly as:

d1accsen-m1relaq1 WITH d1accsen-m1relaq1;

On the question in your second message -No.
 Danyel A.Vargas posted on Thursday, January 30, 2014 - 3:14 pm
Dr. Muthen,

Thanks for replying. I appreciate your help!

Danyel
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