The loading you look at are the ones in the factor structure matrix,
which has the item-to-factor correlations. The matrix before rotation
will yield the largest factor as the overall total score, reflecting the
generally-positive correlations among items on a scale. The second
factor will be "bipolar" in the sense of tending to have one set of high
correlations that are positive, and another set that are negative.
--
Rich Ulrich
> Date: Sat, 16 Nov 2013 15:00:56 -0500
> From:
[hidden email]> Subject: Repeated measures in large data set
> To:
[hidden email]>
> I have a date set with approximately 1,000,000 people. Medication usage was
> recorded monthly throughout one calendar year (i.e. each person has 12 time
> points). The variables are numeric and refer to dosage.
> I'm interested in comparing use across time, between two different regions and
> three different groups. I've run Repeated Measures models with factors and
> interactions. Everything is significant because the n is so large. Is there a
> better way to do this? The differences between months are very small but all
> pairwise comparisons are significant. How do I know which are meaningful?
> (I'm particularly interested in comparing one month to the preceding and
> following months).
>
> Thanks!
> ...