I am trying (operative word) to validate a scale composed of two highly correlated (~.7) subscales, let's call them A and B. In order to establish convergent validity with other measures, I am looking at a lot of correlations. Because the subscales are highly correlated, I am looking at partial correlations (i.e., correlation between A and X controlling for B; and B and X controlling for A) in addition to zero-order correlations between each of the measures and each of the subscales.
Here's my question: For one measure, the Satisfaction with Life Scale (SWLS), zero-order correlations (i.e., between SWLS and A and SWLS and B) are both positive and moderate, whereas partial correlations are null and weak, respectively.
How do I interpret this? I'm wondering because it would be very interesting to me to discover that neither A nor B is alone sufficient for predicting SWL-- that instead you need both. In other words, that the whole is more than the sum of its parts. Is that anything close to what it means, and if not, what does it mean? I've tried searching the internet but haven't come up with anything, so now I'm also just really perplexed/curious.
I should also probably say that, in case it isn't already abundantly clear, statistics is not my strong suit! Thank you in advance for bearing with me (and for any insight you can offer!).