Lois Downey posted on Monday, January 12, 2009 - 9:24 am
My dataset includes responses collected over time from a group of respondents. I want to compute a correlation coefficient between two quasi-continuous measures, with standard errors corrected for clustering within respondents. However, when I ask for standardized parameter estimates, the standardized estimates match the unstandardized estimates, and both look like covariances, rather than correlations. I'm using the following syntax:
If you do TYPE=BASIC COMPLEX; without a MODEL command, you will get both covariances and correlations.
Lois Downey posted on Monday, January 12, 2009 - 11:54 am
Is there a way to get p-values for the correlations, using the method you've indicated? My goal was to get standard errors corrected for clustering of responses within respondents, so I could evaluate the significance of the correlations.
I have the same question as the topic starter. The syntax above (without MODEL CONSTRAINT) seems to work and produce correlations, standard errors, and p-values. I am not sure I understand why MODEL CONSTRAINT is needed?
I also wanted to ask a follow-up question. If I want to know correlations and their standard errors between a student variable (e.g., achievement) and a teacher variable (e.g., teaching style) where the teaching variable has the same values for all students taught by the same teacher -- would the method above with type = complex appropriate?