Hi all,

I am conducting an analysis in which I need to specify the error 
variance/covariance structure in a Linear Mixed Model analysis. Specifically, I 
had 60 participants, each of them rated their liking of a close and non-close 
other at the end of the day, for consecutive 6 days. The datafile has the 
following structure:


Participant_id Day
(e.g., 1 = first day of rating; 2 = second day of rating) Target
(1 = close other;
2 = non-close other) Liking of Target
(from 1 to 9) 
1 1 1 8 
1 1 2 9 
1 2 1 7 
1 2 2 6 
1 3 1 5 
1 3 2 7 
1 4 1 8 
1 4 2 9 
1 5 1 7 
1 5 2 6 
1 6 1 6 
1 6 2 8 
2 1 1 9 
2 1 2 9 
2 2 1 7 
2 2 2 6 
2 3 1 5 
2 3 2 6 
2 4 1 4 
2 4 2 5 
2 5 1 8 
2 5 2 7 
2 6 1 6 
2 6 2 4 
3 1 1 5 
3 1 2 6 
3 2 1 6 
3 2 2 7 
3 3 1 8 
3 3 2 9 
3 4 1 8 
3 4 2 7 
3 5 1 6 
3 5 2 5 
3 6 1 4 
3 6 2 8 
My major interest is to examine whether there is a main effect of target on 
liking, and whether there is an interaction effect between target and other 
personality variables (personality variables not shown here) on liking.

The MLM equation is as follow:

Liking = Intercept + Beta1*Target + Beta2*Personality + Beta3*Target*Personality 
+ Participants' Random Effect + Error,
and the error covariance structure of day is specified as AR (Heterogeneous).

However, I keep obtaining an error message that complains about having two 
ratings on each day, and thus it cannot model the error covariance structure 
properly. In another trial analysis, I only have one rating on each day and the 
program no longer complains.

Should I specify the error covariance structure in my data? Alternatively, 
should I simply specify the time (i.e., day) as another random effect? Thank you 
very much for your help. It is much appreciated!

Chester Kam
Doctoral Student, Psychology
University of Western Ontario
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